Detection of Trisomy 21 using Deep Learning
A midterm capstone project report submitted in partial fulfilment of the requirement for the
award of the degree of
Bachelor of Engineering
in
Electronics and Computers Engineering / Electronics and Communication Engineering
Submitted By
Puneet Gupta (Regn. No.:102115213)
Tejas Malhotra (Regn. No.: 102115259)
Anirudh Verma (Regn. No.:102165013)
Abhiyudya Seth (Regn. No.: 102115144)
Chetan Yenigalla (Regn. No.:102106178)
Srinivasa Raghavan S (Regn. No.: 102115262)
(Group No – 26)
Under Supervision of
Dr. Harpreet Vohra (Designation, ECED)
Dr. Chandramohan Dhasarathan (Designation, ECED)
Department of Electronics and Communication Engineering
THAPAR INSTITUTE OF ENGINEERING & TECHNOLOGY, PATIALA, PUNJAB
June 2024
DECLARATION
We hereby declare that the capstone project group report title “Detection of Trisomy 21
using Deep Learning “is authentic record of our own work carried out at “Thapar Institute
of Engineering and Technology, Patiala” as a Capstone Project in seventh semester of
B.E. (Electronics & Communication Engineering), under the guidance of “Dr Harpreet
Vohra and Dr Chandramohan Dhasarathan”, during January to May 2024.
Date:
NAME
ROLL No.
Puneet Gupta
102115213
Tejas Malhotra
102115259
Anirudh Verma
102165013
Abhiyudya Seth
102115144
Chetan Yanigalla
102106178
Srinivasa Raghavan S
102115262
SIGNATURE
ACKNOWLEDGEMENT
We would like to express our thanks to our mentors Dr Harpreet Vohra and Dr Chandramohan
Dhasarathan. They have been of great help in our venture, and indispensable resources of
technical knowledge. They are truly amazing mentors to have. We are also thankful to Dr.
Kulbir Singh, Head of Electronics and Communication Department, and also our friends who
devoted their valuable time and helped us in all possible ways towards successful completion
of this project. We thank all those who have contributed either directly or indirectly to this
project. Lastly, we would also like to thank our families for their unyielding love and
encouragement. They always wanted the best for us and we admire their determination and
sacrifice.
Date:
Roll No.
Name
102115213
Puneet Gupta
102115259
Tejas Malhotra
102165013
Anirudh Verma
102115144
Abhiyudya Seth
102106178
Chetan Yenigalla
102115262
Srinivasa
Raghavan S
ii
ABSTRACT
Down Syndrome (DS) is a genetic disorder caused by an extra copy of chromosome 21, characterized
by distinct facial features and associated medical conditions. This project focuses on developing a deep
learning model for automated detection of DS using facial images, aiming to aid early and accurate
diagnosis. We utilized a deep convolutional neural network (DCNN) to analyze facial patterns
indicative of DS from a diverse and extensive dataset. Preprocessing techniques such as image
normalization, augmentation, and alignment were applied to enhance data quality. The DCNN was
trained with advanced methodologies, including transfer learning and hyperparameter optimization, to
ensure high accuracy and robustness. Our model provides a non-invasive, quick, and reliable tool for
detecting DS, potentially improving early intervention and care.
Future work includes optimizing the model for edge devices like the Jetson Nano, enabling real-time
analysis in resource-constrained environments. This approach promises to enhance clinical diagnostics
and accessibility to healthcare for individuals with Down Syndrome. This non-invasive tool aids
clinicians in prompt DS identification, improving care and treatment. Future work aims to optimize the
model for real-time analysis on edge devices like the Jetson Nano, enhancing accessibility in resourcelimited settings.
iii
LIST OF TABLES
Table No.
Caption
Page No.
Table 2.1
Summary of face detection methods for DS face recognition.
9
Table 2.2
Datasets used by researchers for their studies [27]-[44]
11
Table 2.3
Methodology engaged in studies [28]-[44]
11
Table 4.1
Listo of UG subjects used in the project
18
vii
LIST OF FIGURES
Figure No.
Caption
Page No.
Figure 2.1
Chromosomal Arrangement of Down Syndrome (Wikipedia)
1
Figure 2.2
Graphical representation of CNN Network
5
Figure 2.3
Classification model pipeline
8
Figure 2.4
Procedure of determining the feet structure indices
12
Figure 2.5
The structure of the proposed MLP model in literature [18]
13
Figure 3.1
Concept diagram for proposed system
16
Figure 5.1
Architecture of DCNN model
20
Figure 5.2
Tested Models and Train Test Accuracy
20
Figure 5.3
Confusion matrix of the CNN model
21
Figure 5.4
ROC Curve
21
viii
LIST OF ABBREVATIONS
TIET
Thapar Institute of Engineering and Technology
ECE
Electronics and Communication Engineering
DCNN
Deep Convolutional Neural Networks
AVSD
Atrioventricular Septal Defects
AD
Alzheimer's Disease
HD
Hirschsprung's disease
DST
Duodenal Stenosis
IA
Imperforate Anus
AMKL
and ALL
Leukaemia’s
CHD
Congenital heart defects
NB
Nasal Bone Length
NT
Nuchal Translucency
MLP
Multilayer Perceptron
ROC
Receiving operator characteristics
IoT
Internet of Things
LDF
Learning Deep Format
CNN
Convolutional Neural Network
MTCNN
Multitask Cascaded Convolutional Neural Networks
FL
Femur Length
ADF
Anisotropic Diffusion Filter
ROI
Region of interest
AFP
Alpha-Fetoprotein, hCG Human Chorionic Gonadotropin, and uE3 Unconjugated Estriol
TABLE OF
ix
CONTENTS
DECLARATION
ACKNOWLEDGEMENT
ABSTRACT
LIST OF TABLES
LIST OF FIGURES
LIST OF ABBREVIATIONS
CHAPTER 1- INTRODUCTION
1.1 PROJECT OVERVIEW
1.2 MOTIVATION
1.3 ASSUMPTIONS AND CONSTRAINTS
1.4 NOVELTY OF WORK
CHAPTER 2 – LITERATURE SURVEY
2.1 LITERATURE SURVEY
2.2 RESEARCH GAPS
2.3 PROBLEM DEFINITION AND SCOPE
CHAPTER 3 – FLOW CHART
3.1 SYSTEM ARCHITECTURE
3.2 ANALYSIS
3.3 TOOLS AND TECHNOLOGIES USED
CHAPTER 4 – PROJECT DESIGN AND DESCRIPTION
4.1 DESCRIPTION
4.2 U.G SUBJECTS
4.3 STANDARDS USED
CHAPTER 5 – IMPLEMENTATION & EXPERIMENTAL RESULT (OPTIONAL
5.1 SIMULATION RESULTS
i
ii
iii
iv
v
vi
1
1
1
2
2
3
3
14
14
16
16
16
16
18
18
18
19
20
20
CHAPTER 6 – OUTCOME AND PROSPECTIVE LEARNING
6.1 SCOPE AND OUTCOMES
6.2 PROSPECTIVE LEARNINGS
6.3 CONCLUSION
CHAPTER 7 – PROJECT TIMELINE
7.1 WORK BREAKDOWN & GANTT CHART
7.2 PROJECT TIMELINE
7.3 INDIVIDUAL GANTT CHART
22
22
22
.22
24
24
24
24
REFERENCES
27
iv
CHAPTER-1 INTRODUCTION
1.1 PROJECT OVERVIEW
Down Syndrome (DS) is a genetic disorder caused by the presence of an extra chromosome 21. Early
diagnosis of DS is crucial for timely intervention and management, which can significantly improve the
quality of life for individuals with the condition. Traditional diagnostic methods often require
specialized equipment and expertise, limiting accessibility, especially in low-resource settings. This
project aims to address these limitations by developing an accurate and robust deep learning model
capable of detecting Down Syndrome from facial images and optimizing it for deployment on edge
devices, thereby enhancing accessibility and usability in diverse healthcare environments.
The objectives are:




Develop a Deep Learning Model: Create a convolutional neural network (CNN) that
accurately detects Down Syndrome using facial images.
Validate Model Performance: Evaluate the model's performance through extensive testing to
ensure robustness and reliability in various conditions.
Optimize for Edge Devices: Optimize the model to ensure it is lightweight and efficient
enough to run on edge devices, such as smartphones and tablets, without compromising
accuracy.
Ensure Usability for Non-Specialists: Design the model and its interface to be user-friendly,
enabling non-specialist healthcare providers to effectively use the tool.
By the end of this project, we are expected to have a deep learning model that reliably detects Down
Syndrome with high accuracy from facial images, an optimized model that operates efficiently on edge
devices, making it accessible for use in various healthcare settings, including those with limited
resources, an intuitive interface that enables non-specialist healthcare providers to use the diagnostic
tool effectively, enhanced diagnostic capabilities for Down Syndrome, particularly in remote and
underserved areas, contributing to earlier diagnosis and better management of the condition.
1.2 MOTIVATION
Down syndrome (DS) is one of the most common genetic birth disorders. Down syndrome occurs in
approximately 1 of 830 live births. It is associated with mild to moderate learning disabilities,
developmental delays, characteristic facial features, and low muscle tone in early infancy. Many
individuals with Down syndrome also have heart defects, leukaemia, early-onset Alzheimer’s disease,
gastro-intestinal problems, and other health issues. Through a series of screenings and tests, Down
syndrome can be detected before and after a baby is born.
This project aims to empower non-specialist healthcare providers with the tools needed to detect Down
Syndrome (DS) in children at the earliest possible stage. Though Down syndrome can’t be prevented,
it can be detected in earlier stages. The health problems that may go along with DS can be treated, and
many resources are available to help kids and their families who are living with the condition. By
equipping non-specialist healthcare providers with an accurate, robust, and user-friendly diagnostic
tool, this project seeks to bridge the gap in access to specialized medical expertise, particularly in
underserved and remote areas.
By developing and optimizing a deep learning-based diagnostic tool that can run efficiently on edge
devices such as smartphones and tablets, this project aims to make advanced diagnostic capabilities
accessible to a broader range of healthcare providers. This accessibility is particularly important in
regions where access to specialized medical professionals and diagnostic equipment is limited. The
1
tool's ease of use will allow non-specialist healthcare providers, such as general practitioners, nurses,
and community health workers, to perform preliminary screenings and identify children who may need
further evaluation and support.
In summary, this project strives to democratize access to early Down Syndrome detection, ensuring that
every child has the opportunity to receive the care and support they need from the very beginning. By
leveraging the power of deep learning and edge computing, we aim to create a transformative impact
on the early diagnosis and management of Down Syndrome, ultimately contributing to better health
outcomes and enhanced quality of life for children and their families.
1.3 ASSUMPTIONS AND CONSTRAINTS
One primary assumption is that facial features are a reliable indicator for detecting Down Syndrome,
which is supported by existing medical research. The project also assumes that the dataset collected will
be representative of the diverse population of children with DS, ensuring the model's applicability
across different ethnicities.
Constraints include the limited computational power of edge devices like the Jetson Nano and
Raspberry Pi, which necessitate model optimization techniques such as quantization and compression.
Additionally, the project is constrained by the availability of labeled data, requiring the use of data
augmentation to enhance the dataset.
These assumptions and constraints are critical to defining the project's scope and ensuring that the
developed model is both effective and practical for real-world deployment
1.4 NOVELTY OF WORK
While numerous studies have focused on developing robust deep learning models for the detection of
Down Syndrome (DS) using facial images, there remains a significant gap in research and practical
implementation regarding the deployment of these models on edge devices such as the NVIDIA Jetson
Nano and Raspberry Pi. This project introduces a novel approach by not only developing a highly
accurate deep learning model for DS detection but also optimizing and deploying it on these edge
devices. Unlike traditional implementations that rely on powerful cloud-based servers, this project
addresses the unique challenges of running deep learning models on devices with limited processing
power, memory constraints, and energy efficiency. By leveraging innovative techniques in model
compression and quantization, the project ensures efficient operation on these compact and affordable
devices. This deployment enhances accessibility and portability, enabling non-specialist healthcare
providers to use advanced diagnostic tools in diverse settings, including remote and underserved areas.
The project emphasizes creating a user-friendly interface for easy image capture and analysis, allowing
real-time processing and immediate diagnostic feedback, which is crucial for timely intervention.
Moreover, the cost-effectiveness of these edge devices reduces financial barriers, making advanced
diagnostic technology viable for widespread adoption, particularly in low-resource settings. By
focusing on scalability and practical deployment strategies, this project not only extends the capabilities
of deep learning-based DS detection but also sets a precedent for future research and applications of
deep learning in healthcare on edge devices.
2
CHAPTER-2 LITERATURE SURVEY
2.1 LITERATURE SURVEY
2.1.1 Introduction
Down syndrome, clinically known as Trisomy 21, is a genetic disorder caused by the presence of an
extra copy or part of chromosome 21. It is one of the most commonly occurring chromosomal
abnormalities, affecting approximately 1 in 700-1,000 live births worldwide. Individuals with Down
syndrome exhibit a distinct set of physical characteristics, including craniofacial dysmorphologies,
intellectual disability, and a higher predisposition to certain medical conditions.
Fig. 2.1 Chromosomal Arrangement of Down Syndrome (Wikipedia)
1.


Common Features of Down Syndrome (DS):
Learning disabilities, craniofacial abnormalities, and hypotonia.
Variant phenotypes may include atrioventricular septal defects (AVSD), leukaemia,
Alzheimer's disease (AD), and hypertension.
2.
Physical Characteristics:
3


Slanted eyes, poor muscle tone, and a flat nasal bridge.
Additional features: single palm crease, protruding tongue, and abnormalities in toe size and
fingerprint patterns.
3.


Genetic Causes:
Trisomy of chromosome 21 (most common), Robertsonian translocation, and mosaicism.
Genetic hypotheses: gene dosage imbalance, amplified developmental instability, and critical
region hypotheses.
4.




Associated Clinical Conditions:
Neurological Problems: Early-onset Alzheimer's disease (AD).
Cardiac Problems: Congenital heart defects (CHD).
Haematological Problems: Leukaemia’s (AMKL and ALL).
Gastrointestinal Problems: Hirschsprung's disease (HD), duodenal stenosis (DST), and
imperforate anus (IA).
Various factors influence the risk of Down syndrome (DS), including maternal grandmother's diet,
genotype, lifestyles, and environmental exposures, which can impact recombination errors leading to
chromosome 21 nondisjunction during fetal development. Maternal age at conception, along with
maternal diet, lifestyle, and environmental factors, can favor meiotic errors, particularly at maternal
meiosis II, contributing to DS cases. Paternal dietary habits, lifestyles, and environmental exposures
may influence chromosome 21 disomy in spermatozoa, leading to cases of paternal origin DS. Complex
interactions between the embryo's genome, maternal factors, and environmental exposures can affect
the outcome of DS pregnancies, including miscarriage, survival to birth, congenital disorders, or
predisposition to age-related diseases in DS individuals.
Early and accurate diagnosis of Down syndrome is crucial for prompt interventions, tailored educational
programs, and appropriate medical management. Traditional diagnostic methods involve prenatal
screening tests, such as maternal serum screening and ultrasound examinations, followed by invasive
procedures like amniocentesis or chorionic villus sampling for cytogenetic analysis. However, these
methods are often time-consuming, resource-intensive, and carry potential risks. With the rapid
advancement of artificial intelligence (AI) and computer vision techniques, there has been a growing
interest in developing non-invasive, automated systems for detecting Down syndrome based on facial
analysis. Individuals with Trisomy 21 often exhibit characteristic facial features, including a flattened
facial profile, up slanting palpebral fissures, and a protruding tongue. Deep learning models, particularly
convolutional neural networks (CNNs), have demonstrated remarkable success in recognizing complex
patterns and subtle features in images, making them well-suited for this task. The development of an
accurate and reliable AI model for Trisomy 21 identification from facial images presents several
challenges. First, acquiring a diverse and representative dataset of facial images from individuals with
and without Down syndrome is crucial for robust model training and generalization. Handling variations
in imaging conditions, such as pose, illumination, and occlusions, is another critical aspect to ensure
model performance across different scenarios. Furthermore, ensuring fairness and robustness across
different populations is essential, as biases in the training data or model architecture could lead to
disparate performance and potential ethical concerns. Interpreting the model's decision-making process
and identifying the specific facial features that contribute to the diagnosis is also crucial for clinical
acceptance, transparency, and potential integration with existing diagnostic protocols. Overcoming
these challenges requires a multidisciplinary approach, combining expertise from various fields,
including computer vision, machine learning, genetics, and clinical practice. Successful development
and deployment of such AI models could potentially streamline the diagnostic process, reduce the need
for invasive procedures, and facilitate early interventions for individuals with Down syndrome. This
4
article aims to provide a comprehensive review of the current state-of-the-art in AI model development
for Trisomy 21 identification from facial images. We will explore the various techniques, datasets, and
methodologies employed, as well as the challenges and limitations encountered. Additionally, we will
discuss potential avenues for future research and the ethical considerations surrounding the use of AI
in genetic disorder diagnosis.
This study synthesizes findings from various research papers and studies, utilizing multiple techniques
to develop an AI model for identifying Trisomy 21 from facial features. The various methodologies,
such as deep learning architectures, data preprocessing steps, and classification algorithms, along with
their performance metrics, accuracies, and limitations, are examined in different sections of this article.
2.1.2 Deep Learning Techniques for Medical Imaging
A. Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs) are a type of deep neural network architecture that is
particularly well-suited for processing image data. CNNs are designed to automatically learn and extract
relevant features from input images through multiple layers of convolutional, pooling, and fully
connected layers.
1. Convolutional Layer: This layer applies a set of learnable filters (kernels) that slide across the input
image, performing convolution operations to extract local features such as edges, shapes, and patterns.
2. Pooling Layer: This layer performs down-sampling operations on the feature maps, reducing their
spatial dimensions while retaining the most important information. Common pooling operations include
max pooling and average pooling.
3. Fully Connected Layer: These layers are similar to traditional neural networks, where every node is
connected to all nodes in the previous layer. They are responsible for combining the features learned by
the convolutional and pooling layers and making the final classification or regression decision.
Fig. 2.2 Graphical Representation of CNN network
CNNs have been extensively applied in various medical image analysis tasks, including:
1. Image Classification: Classifying medical images into different categories, such as normal or
abnormal, or identifying specific diseases or conditions.
5
2. Object Detection and Localization: Detecting and localizing regions of interest within medical
images, such as tumors, lesions, or anatomical structures.
3. Segmentation: Partitioning medical images into different regions or structures, enabling precise
delineation of target areas for further analysis or treatment planning.
4. Image Registration: Aligning and matching different medical images, which is crucial for multimodal
analysis, longitudinal studies, and image-guided interventions.
5. Image Enhancement: Improving the quality of medical images by reducing noise, enhancing contrast,
or normalizing intensity distributions.
B. Transfer Learning
Transfer learning is a technique where a deep learning model pre-trained on a large, general dataset is
used as a starting point for a new task, rather than training from scratch. The pre-trained model's weights
are fine-tuned on the target dataset, allowing the model to leverage the knowledge gained from the
initial training and adapt to the new task more efficiently.
In the context of medical diagnostics, transfer learning offers several advantages:
1. Limited Data: Medical datasets are often small and limited due to privacy concerns and the difficulty
of data collection. Transfer learning allows leveraging knowledge from large, general datasets, reducing
the need for extensive labeled medical data.
2. Faster Convergence: Pre-trained models provide a good initialization point, enabling faster
convergence and reducing training time compared to training from scratch.
3. Improved Performance: By leveraging the knowledge from related domains, transfer learning can
improve the performance of medical diagnostic models, especially when the target dataset is small.
Transfer learning has been successfully applied in various healthcare applications, including:
1. Radiology: Pre-trained models on natural images have been fine-tuned for tasks such as chest X-ray
analysis, brain tumor segmentation, and mammogram classification.
2. Dermatology: Models pre-trained on general image datasets have been adapted for skin lesion
classification and melanoma detection.
3. Ophthalmology: Transfer learning has been used for retinal image analysis, including diabetic
retinopathy detection and age-related macular degeneration diagnosis.
4. Pathology: Pre-trained models have been fine-tuned for tasks like cancer histopathology image
classification and tumor grading.
C. Model Training and Validation
Training deep learning models for medical imaging applications requires high-quality, diverse, and
well-annotated datasets. However, obtaining such datasets can be challenging due to the following
factors:
1. Data Privacy and Ethical Concerns: Medical data is highly sensitive, and strict regulations and ethical
guidelines must be followed to protect patient privacy and ensure proper consent.
2. Limited Data Availability: Collecting and annotating medical images is a time-consuming and
resource-intensive process, often leading to small dataset sizes.
3. Class Imbalance: In many medical imaging tasks, the distribution of different classes (e.g., normal
vs. abnormal) can be highly imbalanced, which can affect model performance.
6
4. Data Diversity: Medical imaging data can vary significantly based on factors such as patient
demographics, imaging modalities, and acquisition protocols, making it crucial to have diverse and
representative datasets.
Several techniques are employed to train deep learning models for medical imaging effectively:
1. Data Augmentation: Techniques like rotation, flipping, scaling, and adding noise can be used to
artificially increase the size and diversity of the training dataset.
2. Transfer Learning: As discussed earlier, transfer learning from pre-trained models on large, general
datasets can significantly improve model performance and convergence speed.
3. Ensemble Methods: Combining multiple models trained on different subsets of the data or with
different architectures can improve overall performance and robustness.
4. Regularization: Techniques like dropout, weight decay, and early stopping can help prevent
overfitting and improve model generalization.
5. Attention Mechanisms: Incorporating attention mechanisms into the model architecture can help
focus on the most relevant regions of the input image, improving performance on tasks like object
detection and segmentation.
Proper validation is crucial to ensure the accuracy and reliability of deep learning models for medical
imaging:
1. Cross-Validation: Techniques like k-fold cross-validation can be used to estimate the model's
performance on unseen data and prevent overfitting.
2. Independent Test Sets: Holding out a separate test set, ideally from a different distribution than the
training data, can provide an unbiased evaluation of the model's performance and generalization ability.
3. Human Expert Evaluation: Involving medical experts to evaluate the model's predictions and
compare them with ground truth annotations can provide valuable insights and identify potential biases
or errors.
4. Robustness Testing: Evaluating the model's performance under various conditions, such as different
imaging modalities, noise levels, or perturbations, can help assess its reliability and identify potential
failure modes.
5. Explainable AI Techniques: Methods like saliency maps, attention visualization, and concept
activation vectors can help interpret and explain the model's decision-making process, increasing trust
and transparency in medical applications.
By following best practices in dataset curation, model training, and validation, deep learning techniques
can be effectively applied to medical imaging tasks, potentially improving diagnostic accuracy,
treatment planning, and patient outcomes.
2.1.3 Deep Learning Techniques in Detecting Down Syndrome
A. DS Detection using Facial Images
7
Fig. 2.3 Classification model pipeline [26]
The training algorithm comprises three stages: image preprocessing, training a general facial
recognition network, and fine-tuning the network for the specific task of Down syndrome
identification.[26] The core foundation of this approach lies in leveraging the power of deep
convolutional neural networks (DCNNs) to extract the subtle yet distinct facial features that
differentiate individuals with Down syndrome from their neurotypical counterparts.
Image Preprocessing
Effective image preprocessing is a crucial step in the Down syndrome identification pipeline, as it
ensures that the input data is optimized for accurate facial detection and feature extraction by the neural
network. The image preprocessing routine employed in this study encompasses four main stages: image
enhancement, facial detection, facial cropping, and image resizing.
 Image Enhancement
The raw images used in this study are acquired in unconstrained real-world settings, leading to
significant variability in exposure, contrast, and imaging conditions. To mitigate the impact of these
extraneous factors and enable the subsequent facial detection algorithms to operate optimally,
image enhancement techniques are applied. The image enhancement process serves two critical
purposes. Firstly, it facilitates superior facial area detection in the subsequent steps by enhancing
the visibility and contrast of facial regions. Secondly, it minimizes the influence of irrelevant factors
such as exposure, contrast, and camera characteristics, enabling the neural network to concentrate
more effectively on the intrinsic facial features relevant to Down syndrome identification.
 Facial Detection
After image enhancement, a robust and highly accurate facial detection algorithm is employed to
locate the precise position of the face within the image. This step is crucial, as it isolates the facial
area from the background, allowing the subsequent stages to focus solely on the relevant facial
features. Given the inherent variability in unconstrained 2D facial images, including differences in
face sizes, expressions, and backgrounds, a deep learning-based approach is adopted for facial
detection. Specifically, the multitask cascaded convolutional neural networks (MTCNN) technique
is utilized, as it has demonstrated state-of-the-art performance in facial detection tasks. The
MTCNN approach consists of three cascaded convolutional neural networks: P-Net, R-Net, and ONet. These networks work in a sequential manner, with each stage refining the facial detection
results from the previous stage. The P-Net network performs an initial face candidate detection, the
R-Net network filters and refines these candidates, and the O-Net network generates the final
bounding box coordinates and facial landmarks for each detected face. To adapt the MTCNN model
to the specific requirements of this study, the pre-trained network is fine-tuned using a subset of the
available facial images. This customization process ensures that the facial detector is optimized for
8
the diverse range of facial characteristics, expressions, and imaging conditions present in the Down
syndrome identification dataset.
 Facial Cropping
Once the precise location of the face is determined by the facial detection algorithm, the facial
region is cropped from the enhanced image. This cropping step serves two primary purposes.
Firstly, it isolates the facial area from the background, ensuring that the subsequent neural network
focuses solely on the relevant facial features and is not influenced by extraneous background
information. By concentrating on the facial region, the network can learn to extract the subtle
phenotypic features that distinguish individuals with Down syndrome from healthy individuals
more effectively. Secondly, the facial cropping process helps to standardize the input data, as the
cropped facial images are typically of varying sizes and aspect ratios. This standardization is
essential for compatibility with the input requirements of the neural network architecture employed
in the Down syndrome identification task.
 Image Resizing
By standardizing the input image dimensions, the neural network can process batches of images
efficiently, without the need for additional resizing or interpolation operations during training or
inference. This consistency in input data format contributes to improved training stability and
convergence, as well as faster processing times during real-world deployment. In summary, the
image preprocessing routine employed in this study is a multi-stage process designed to optimize
the input data for accurate facial detection and feature extraction. By enhancing image quality,
locating and isolating the facial region, and standardizing the input dimensions, this preprocessing
pipeline ensures that the neural network receives high-quality, relevant data, enabling it to learn the
subtle phenotypic features associated with Down syndrome more effectively.
Ref
Databases
Total
Images
DS Images
Landmarks
Detection
Methods
[45]
Healthy & 8
known
disorders
including DS
Healthy & 8
known
disorders
including DS
Healthy & DS
2878
209
36
OpenCV
154
50
44
ViolaJones(Haar
like)
261
129
44
6 known
syndromes
including DS
Healthy & DS
1126
537
68
ViolaJones(Haarlike)
PASCAL
VOC
306
153
16
216 known
syndromes
including DS
17,000
-
130
[46]
[47]
[48]
[49]
[50]
ViolaJones(Haarlike)
DCNN
cascade
Table 2.1 Summary of face detection methods for DS face recognition.
9
B. DS Detection using Ultrasound Images
Ultrasonography is a widely adopted medical imaging method that allows real-time visualization
of the abdominal region (Orlandi et al. 2003). It utilizes high-frequency sound waves, above the
audible range for humans, to detect and measure objects within the body. Despite being based on
sound waves, this imaging technique is considered safe and does not have any negative impact on
the human body (Lao et al. 2019). Consequently, it is recommended for pregnant women to monitor
fetal growth. Several biomarkers, such as nasal bone (NB) length, nuchal translucency (NT), femur
length (FL), crown-rump length (CRL), and fronto-maxillary facial angle, are commonly used to
identify potential cases of Down syndrome through ultrasound imaging (Ghonchi et al. 2020).
Nuchal translucency refers to the accumulation of fluid at the back of the fetal neck, and this
measurement is typically taken during the 12th week of pregnancy. A normal nuchal translucency
value falls within the range of 1.30 ± 0.54, while values exceeding 2.5 are considered abnormal,
indicating a potential irregularity (More and Singla 2021). On the other hand, nasal bone length is
another important marker, with normal values ranging from 1.69 mm to 2.94 mm. In cases where
the nasal bone is absent or not visible, it may signify an abnormality in the fetus (Park et al. 2013;
Wee et al. 2010).
While ultrasound imaging offers valuable visual information about the fetus, manually analyzing
and classifying the nuchal translucency (NT) and nasal bone (NB) features can be a time-intensive
and inefficient process. Neural networks, a machine learning technique, have demonstrated
remarkable capabilities in accurately classifying medical images, offering a promising solution to
streamline and automate this process (Chaudhary et al. 2019).
The foetal images are pre-processed using anisotropic diffusion filter (ADF) which removes highfrequency noise while preserving existing objects’ main edges
The pre-processed foetal images are subjected to region of interest (RoI) extraction to isolate the
Down syndrome makers from the ultrasound image
Sensitivity, specificity, accuracy, recall, and F1 score are the evaluation metrics for evaluating the
efficiency of the proposed method.
Author
Dataset used
Bharath Srinivas prabakaran et al.
FPUS23dataset
https://github.com/bharathprabakaran/FPUS2
Ellen Hollands Steffensen et al.
Danish Medical Birth Register
Liwen Zhang et al.
Collected data from 2 hospitals in China (Jan
2009 – Sept 2020)
Li Li et al.
Collected data from obstetric clinic of their
hospital from Jan 2019 – Dec 2021
Luis M LopezGarcia et al.
Data from pregnant women in Spanish
hospitals.
Amir Jamshid Nezhad et al.
private screening reference laboratory
Falin He et al.
58,972 screening data over 10 years were used
to establish a machine model to predict Down
syndrome
10
Bosheng Qin et al.
CASIA-WebFace datasets
Alptekin Durmus ̧o ̆ glu et al.
From Gaziantep University’s S ̧ ahinbey
Training and Research Hospital, 81 patient
records were obtained in Turkey.
Anita Kaul et al
The data was collected between the years 2010
and 2017 from singleton Indian women who had
fetal ultrasound, with measures of the fetal NT
thickness and CRL during 11 and 13 + 6 weeks
of pregnancy.
Ling Li et al
maternal serum screening data.
Chanane Wanapirak et al.
maternal serum screening among Thai
population
Prim Dr Sc med Jasmina Durkovi ́ c et al.
Leveraging the IMMULITE 2000 analyser, data
were produced.
Aki Koivu et al.
Multiple categorization techniques have been
explored with using two real-world data sets.
Bing Feng et al
Illumina genotyping array data
Subhiksha Ramanathan et al.
Subhiksha Ramanathan Hospitals, Solapur,
Maharashtra
Bing Feng et al.
Vanderbilt University Medical Centre
Table 2.2 Datasets used by researchers for their studies [27]-[44]
Methodology Used
Description
ResNet34 Model
Demonstrated remarkable accuracy in classifying diagnostic planes,
achieving approximately 98.29% accuracy.
Faster-RCNN Model
with base of
ResNet34 Model
Dedicated to detecting the anatomical bounds of the foetus within
ultrasound images, showcasing precision in analysis.
Temporal
Developments
Analysis
Evaluated temporal developments in data, particularly through interrupted
time series studies, enhancing understanding of the impact of the national
prenatal screening program on birth biometry, congenital abnormalities,
and early childhood morbidity.
DL Model for Fetal
Head Segmentation
Focused on the fetal head region within ultrasonographic images,
successfully segmented images using bounding boxes and applied data
augmentation techniques to create additional training data.
Diagnostic Methods
Employed various diagnostic methods, including ultrasonographic
diagnosis, serological screening, and genetic counselling for prenatal
diagnosis, involving measurements of nuchal translucency thickness,
analysis of maternal blood for specific markers, and genetic counselling
and amniocentesis for high-risk pregnancies
11
Unified Multivariate
Bayesian Model
Predicted Down syndrome using a unified multivariate Bayesian model,
risk based on biochemical and ultrasound parameters, calculating
likelihood ratios for affected and unaffected foetuses and combining them
with prior risk (related to maternal age) to determine patient specific
Down syndrome risk.
Genetic techniques
and artificial neural
networks (ANN)
Deployed an artificial neural network, also known as an ANN, to predict
the probability of Down syndrome in foetuses through the examination of
input data such as hormone levels, foetal measures, and woman age. The
framework and characteristics of the ANN, such as the number of neurons
in hidden layers, transferred functions, training operations, acquiring
functions, and inputs factor selection, were optimised using the GA.
Convolutional
Neural Network
(CNN)
implemented a convolution neural network, also known as CNN, to
determine individuals with Down syndrome with an emphasis on 2D RGB
face picture classification. obtained a binary categorization with the
likelihood of having Down syndrome (healthy or with Down syndrome).
Table 2.3 Methodology engaged in studies [28]-[44]
C. DS Detection using Feet Deformities
The "sandal gap" is a phenomenon in which, due to the lack of a restrictive toe box in sandals, the
toes can actuate unrestricted, so one may end up with any number of aligned and misaligned toes.
Fifty-five subjects with DS (age: 14.6 ± 7.4 years) had undergone podiatric clinical and podoscopic
examinations to study their main foot deformities and their footprints, respectively. The results of
these examinations were compared to those of an age-matched asymptomatic control group of fiftythree subjects (age: 13.4 ± 11.2 years).
Despite the reported high prevalence of foot anomalies in DS populations, the current literature is
far from being exhaustive and does not include investigations on other foot deformities such
as hallux varus, overriding fifth toe (quintus varus supraductus), short metatarsals and metatarsus
adductus.
Fig 2.4 Procedure for determining the feet structure indices: (a) foot length (D), foot width (S),
and the Wejsflog (W) index; (b) Clarke's angle; (c) hallux valgus angle (α) and the angle of the
varus deformity of the fifth toe (β).[2]
12
This large age range reveals the wide spectrum of foot deformities associated with the different age
groups of subjects with DS with, for instance, significantly increased odds of presenting an increased
space between the first and second toes and short metatarsals with increasing age. However, the
different ages of the patients might also reveal the same deformity at different stages of its natural
evolution. Larger groups of patients with DS of different ages or a long-term longitudinal study would
better elucidate the natural history of these deformities.
Although many of the reported deformities in this study are described in the literature, hallux valgus,
increased space between the first and second toes, syndactyly, and higher grades of flatfoot were found
to be more prevalent in subjects with DS compared to a control group. Some deformities, among which
increased space between the 1st and 2nd toes, were found to be significantly related to general factors
such as age, presence of joint laxity and BMI.
D. DS Detection using Blood Test
The triple test is a screening test (blood test) used to calculate the probability of a pregnant woman
having a fetus that has a chromosomal abnormality like Down Syndrome (DS). AFP (AlphaFetoprotein), hCG (Human Chorionic Gonadotropin), and uE3 (Unconjugated Estriol) values in the
blood sample of pregnant women are computed and compared with the similar real records where the
outputs (healthy fetus or a fetus with DS) are actually known. The likelihood of the indicators is used
to calculate the probability of having a fetus with chromosomal abnormality like DS. It is a blood test
typically performed during the second trimester and these three markers are used in combination to
modify the maternal age-related risk of DS and thus determine individual risk of fetal DS. In the
calculation of associated risk, maternal age-related risk is multiplied by likelihood ratios, determined
according to the deviation of the measured levels of three markers from the expected median values.
The MLP model developed for this study employs three input variables that are AFP, hCG and uE3 to
predict target class (as DS and DS free). We have not used the weight and gestational week attributes
due to multiple missing values in the records. The training phase adjusts the internal weights to get as
close as possible to the known class’s values. As illustrated in Figure, node 0 and node 1 are output
nodes and node 2 and node 3 are hidden nodes.
Fig. 2.5 The structure of the proposed MLP model in literature [18]
13
2.3 RESEARCH GAPS
While conducting a thorough review of the literature on Down Syndrome (DS) prediction, two
significant research gaps have emerged that this project aims to address.
Limited Dataset on Indian Children with Down Syndrome
One of the primary challenges in developing accurate and robust deep learning models for DS detection
is the availability of diverse and representative datasets. Current studies predominantly focus on datasets
from Western populations, resulting in a lack of representation for other ethnic groups, including Indian
children. This limitation is significant because facial features associated with DS can vary across
different ethnicities. Consequently, models trained on non-representative datasets may not generalize
well to Indian children, leading to potential inaccuracies and reduced diagnostic efficacy in this
demographic. Addressing this gap requires the collection and inclusion of a comprehensive dataset of
Indian children with DS to ensure the developed model can accurately detect DS across diverse
populations.
Lack of Research on Implementing DS Detection Models on Edge Devices
Another critical gap identified is the absence of research focused on deploying DS detection models on
edge devices such as the Raspberry Pi and NVIDIA Jetson Nano. While numerous studies have
successfully developed deep learning models for DS detection using facial images, these models
typically rely on cloud-based servers with substantial computational resources. This reliance poses
significant barriers to accessibility and practical implementation, particularly in low-resource settings
where advanced infrastructure may be lacking. Edge devices offer a promising solution by providing
portable, cost-effective, and efficient alternatives for deploying these models. However, the unique
challenges associated with optimizing deep learning models to run on devices with limited processing
power and memory have not been sufficiently addressed in the literature. Bridging this gap involves
developing techniques for model compression and quantization, ensuring that these models maintain
high accuracy while being capable of running efficiently on edge devices.
2.4 PROBLEM DEFINITION AND SCOPE
Problem Statement
Despite significant advancements in the development of deep learning models for the detection of Down
Syndrome (DS) using facial images, there are notable limitations and gaps in current research and
practical implementation. One primary issue is the lack of a diverse and representative dataset,
particularly concerning Indian children with DS, which hinders the generalizability and accuracy of
existing models across different ethnic groups. Additionally, there is a significant gap in the deployment
of these models on edge devices such as the Raspberry Pi and NVIDIA Jetson Nano. Current models
typically rely on cloud-based servers, which are not practical for use in low-resource settings due to
their high cost and need for substantial computational resources. These gaps create a barrier to early,
accessible, and accurate diagnosis of DS, particularly in underserved and remote areas.
Scope
The scope of this project encompasses several key areas aimed at addressing the identified problem
statement. It involves collecting and curating a comprehensive dataset of facial images of Indian
children with and without Down Syndrome to ensure diverse representation and enhance model
generalizability across different ethnicities. The project will develop a robust convolutional neural
network (CNN) model tailored for DS detection using this dataset, implementing data augmentation
and regularization techniques to improve accuracy. Additionally, the project focuses on optimizing the
14
model for deployment on edge devices like the Raspberry Pi and NVIDIA Jetson Nano, employing
techniques such as model compression and quantization to ensure efficient operation on devices with
limited computational power and memory. A user-friendly interface will be designed to enable nonspecialist healthcare providers to easily capture and analyse facial images, supporting real-time
processing and immediate diagnostic feedback. Extensive validation and robustness testing will be
conducted to ensure high accuracy and practical applicability.
15
CHAPTER-3 FLOW CHART
3.1 SYSTEM ARCHITECTURE
Camera will be used to
capture images. Those images
will then be transferred to
jetson nano.
Images received from the camera
will be processed and then are used
to predict Trisomy 21
Fig. 3.1 Concept diagram for proposed system
3.2 ANALYSIS
We begin with the architectural design of the Convolutional Neural Network (CNN), detailing how
various layers and parameters were chosen to enhance model performance. The dataset, consisting of
facial images of children with and without DS, is pre-processed using data augmentation techniques to
increase diversity and improve the model's generalizability. Several metrics, such as accuracy,
precision, recall, and F1-score, are used to evaluate the model's effectiveness. Additionally, confusion
matrices are generated to identify potential areas of misclassification, allowing for further fine-tuning
of the model. Transfer learning techniques are employed to leverage pre-trained models, significantly
reducing training time and improving accuracy. The integration of Tensor RT and OpenCV libraries
further optimizes the model for real-time applications on edge devices like the Jetson Nano/Raspberry
Pi, demonstrating the feasibility of deploying advanced AI diagnostics in resource-limited settings.
3.3 TOOLS AND TECHNOLOGY USED
1. Python: Python serves as the primary implementation language, owing to its ease of use and
extensive libraries. We can utilize Python's extensive ecosystem to create an end-to-end data
pipeline, encompassing data preparation and model implementation, through the use of Python
programming. With this capability, there is an effortless connection to diverse libraries and
architectures, resulting in a harmonious and extensible solution for detecting drowsiness.
2. TensorRT: It is a high-performance deep learning inference library that plays a crucial role in
production environments, offering significant speedups and efficiency improvements over
16
traditional CPU-based inference methods This technology finds significant application in
various fields such as autonomous vehicles, natural language processing, computer vision, and
recommendation systems.
3. Transfer learning : It is a powerful approach for building image classification models, which
capitalizes on pre-trained models trained on extensive datasets. By utilizing pre-trained weights
to initialize the model, the training process can be optimized, and the model can reap the
benefits of the knowledge acquired from the foundational dataset. It offers faster training times,
improved accuracy, and suitability for resource-limited devices.
4. OpenCV: It is a powerful open-source computer vision and machine learning software library.
The objective is to offer a comprehensive collection of tools and algorithms for the processing
and interpretation of visual data, including images and videos. It is designed to operate
effectively on a range of platforms, including Windows, Linux, macOS, Android, and iOS.
With interfaces for Python and Java, this tool caters to a broad developer community.
5. Tensor Flow: TensorFlow serves as an open-source framework for the creation and
implementation of machine learning models. This ecosystem provides a rich set of tools,
libraries, and resources to support the development of a broad spectrum of machine learning
applications, from basic linear regression models to advanced deep neural networks. This is a
widely chosen development tool on account of its flexibility, scalability, and comprehensive
features.
6. Jetson Nano: Jetson Nano is a small, powerful computer developed by Nvidia specifically
designed for embedded applications and artificial intelligence (AI) at the edge. Deploying deep
learning models and AI algorithms in resource-constrained environments is made easier by its
compact form factor and GPU-accelerated computational capacity. It is well-suited for a variety
of applications, such as autonomous vehicles, drones, smart cameras, and industrial automation,
thanks to its mix of GPU acceleration, CPU performance, memory capacity, and I/O capacities.
7. Docker: The software deployment landscape experiences a radical shift with Docker's
containerization technology, as it packages applications and their dependencies into
lightweight, portable containers, ensuring a uniform environment and simplifying deployment
processes. These containers hold the application code, its essential dependencies (libraries,
system tools), and configurations, allowing for a reliable and unified execution across varying
environments. With their harmonic combination of scalability, consistency, and efficiency in
software delivery, these containers represent a paradigm change.
17
CHAPTER-4 PROJECT DESIGN AND DESCRIPTION
4.1 DESCRIPTION
This project proposes a system to detect Down syndrome using a trisomy 21 classifier. The system
leverages a Jetson Nano/Raspberry Pi module for image processing.
 Image Acquisition: The system will use a dedicated camera.
 User Interface: The results could be displayed on a screen.
 Operation: The system might be triggered by a button press, voice command, or another user
input method instead of automatic image capture upon app launch.
4.2 U.G. SUBJECTS
Subject Code
UNC504
Subject Name
Artificial Intelligence
Description
This subfield of AI equips the system to
analyze visual data from the camera feed.
Techniques like image segmentation and
object detection will be used to isolate the
driver's face within the frame.
UCS668
Edge AI and Robotics: Data Vision
Utilising methodologies including image
processing, object detection, facial
recognition, and feature extraction to
examine visual data.
UCS671
Edge AI and Robotics: Embedded Introduction to Edge computing and Jetson
Vision
kits. Understand GPU computing for
building advanced computer vision
pipelines on Jetson devices.
UCS664
Conversational AI: Natural Language Applying pattern recognition, machine
Processing
learning algorithms, and real-time decisionmaking processes to accurately forecast
driver weariness.
UCS622
Conversational Ai: Accelerated Data Utilising sophisticated data science
Science [Advanced]
techniques, such as predictive modelling,
statistical analysis, and machine learning, to
analyse
and
understand
extensive
information in order to enhance the
precision and dependability of sleepiness
detection.
Table 4.1 List of all the UG subjects used in the project
18
4.3 STANDARDS USED




IEEE 610.4-1990 - Standard Glossary of Image Processing and Pattern Recognition
Terminology: Provides a standardized set of terms for image processing and pattern
recognition, assisting in clear communication and understanding within the context of down
syndrome detection.
IEEE P1451.99 - Standard for Harmonization of Internet of Things (IoT) Devices and
Systems: Establishes a framework for seamless integration and interoperability of IoT devices
and systems, enhancing the efficiency and coordination of components used in down syndrome
detection applications.
IEEE 1855™ - Standard for Learning Data Format (LDF): Provides a standardized set of
terms for image processing and pattern recognition, assisting in clear communication and
understanding within the context of down syndrome detection.
IEEE P2841™ - Framework and Process for Deep Learning Evaluation: Defines a
standardized framework and process for evaluating deep learning models, ensuring rigorous
assessment and benchmarking of algorithms employed in down syndrome detection systems to
enhance reliability and performance.
19
CHAPTER-5 IMPLEMENTATION AND EXPERIMENTAL
RESULTS
5.1 SIMULATION RESULTS
Fig 5.1 Architecture of CNN model
Fig 5.2 Tested Models and Train Test Accuracy
20
Fig. 5.3 Confusion matrix of the CNN model
Fig. 5.4 ROC curve
21
CHAPTER-6 OUTCOMES AND PERSPECTIVE LEARNING
6.1 SCOPE AND OUTCOMES
The project aims to bridge the gap in early DS detection by developing a deep learning model that is
not only accurate but also optimized for deployment on edge devices. The outcomes of this project are
multifaceted. Firstly, there is a significant advancement in the understanding and application of CNNs
for medical image recognition. The project also contributes to the practical knowledge of integrating
AI models with hardware, enhancing the accessibility of advanced diagnostic tools in low-resource
settings. Key outcomes include the creation of a user-friendly app developed using Flutter, which allows
non-specialist healthcare providers to utilize this technology effectively. Furthermore, the project sets
a precedent for future research by addressing the challenges of model optimization and hardware
integration, ultimately improving early diagnosis and management of Down Syndrome in diverse
healthcare environments.
6.2 PERSPECTIVE LEARNINGS
This project on Trisomy 21 Detection with Deep learning and implementing on embedded device
presents a valuable opportunity for developing a practical system and gaining significant knowledge
and experience in various domains.
●
Deep Learning Techniques: Understanding of Convolutional Neural Networks (CNNs) for
image recognition and their application in down syndrome detection.
●
OpenCV and Computer Vision: Gaining proficiency in OpenCV libraries for image
processing and feature extraction, crucial for real-time detection.
●
Model Optimization: Learning how to optimize trained neural network for efficient
deployment on embedded device.
●
Hardware Integration: Understanding the process of integrating the trained and optimized
model with embedded devices.
●
App Development: Understanding and learning how an app can be developed for handling
deep learning model using Flutter
●
Project Management and Collaboration: Refining project management skills through
effective task allocation, progress tracking, and teamwork throughout the development process
By successfully completing this project, the team will not only build a working drowsiness detection
system but also gain valuable knowledge and experience transferable to future projects in computer
vision, machine learning, and embedded systems development.
6.3 CONCLUSION
This review provides a comprehensive examination of AI models for detecting Down syndrome (DS)
using methods such as blood tests, ultrasound imaging, facial analysis, and foot deformities. Analyzing
research from 2017 to 2024, it highlights recent advancements, challenges, and future directions.
Convolutional neural networks (CNNs), a form of deep learning, show promise in accurately identifying
DS based on visual features, with transfer learning addressing limited datasets and overfitting.
22
Ultrasound imaging can detect DS markers like nasal bone (NB) length and nuchal translucency (NT)
during pregnancy, aided by machine learning integration for streamlined analysis. Foot abnormalities,
including the "sandal gap," offer additional diagnostic insights into DS, though further research is
needed to understand deformities across age groups. Blood tests like the triple test screen for
chromosomal abnormalities in pregnant women, with machine learning, especially multilayer
perceptron (MLP) networks, enhancing prenatal care by analyzing biomarkers and predicting individual
risk. In conclusion, AI combined with diagnostic modalities improves DS detection, benefiting
individuals and families, with ongoing research essential for addressing ethical concerns and ensuring
fair AI use across diverse populations.
23
CHAPTER-7 PROJECT TIMELINE
7.1 WORK BREAKDOWN & GANTT CHART
7.2 PROJECT TIMELINE
The timeline is divided into several key milestones, beginning with the initial research and dataset
collection phase, followed by model development and validation. Subsequent phases include
optimization for edge devices, interface design, and extensive testing. Each phase is meticulously
planned to ensure that dependencies are managed, and project goals are met within the stipulated
timeframe. Gantt charts for individual team members highlight specific tasks and deadlines, ensuring
accountability and facilitating efficient project management.
7.3 INDIVIDUAL GANTT CHART
 Anirudh’s Gantt Chart (Regn. No.:102165013)
24
 Abhiyudhya’s Gantt Chart (Regn. No.: 102115144):-
 Chetan’s Gantt Chart (Regn. No.:102106178):-
 Srinivasa’s Gantt Chart (Regn. No.: 102115262):-
25
 Tejas’s Gantt Chart (Regn. No.: 102115259):-
 Puneet’s Gantt Chart (Regn. No.: 102115213):-
26
REFERENCES
1. Zhang, L., Dong, D., Sun, Y., Hu, C., Sun, C., Wu, Q., & Tian, J. (2022). Development and
Validation of a Deep Learning Model to Screen for Trisomy 21 During the First Trimester From
Nuchal
Ultrasonographic
Images. JAMA
network
open, 5(6),
e2217854.
https://doi.org/10.1001/jamanetworkopen.2022.17854
2. Puszczałowska-Lizis, E., Nowak, K., Omorczyk, J., Ambroży, T., Bujas, P., & Nosiadek, L.
(2017). Foot Structure in Boys with Down Syndrome. BioMed research international, 2017,
7047468. https://doi.org/10.1155/2017/7047468
3. Bs, V., Madamanchi, S. C., & Kalimuthu, S. (2024). Early Detection of Down Syndrome
Through Ultrasound Imaging Using Deep Learning Strategies — A Review.
https://doi.org/10.1109/ic-etite58242.2024.10493220
4. Saraydemir, A., Taşpınar, N., Eroğul, O., Kayserili, H., & Dinçkan, N. (2011). Down Syndrome
Diagnosis Based on Gabor Wavelet Transform. Journal of Medical Systems, 36(5), 3205–3213.
https://doi.org/10.1007/s10916-011-9811-1
5. Feng, B., Samuels, D. C., Hoskins, W., Guo, Y., Zhang, Y., Tang, J., & Meng, Z. (2017). Down
syndrome prediction/screening model based on deep learning and illumina genotyping array.
https://doi.org/10.1109/bibm.2017.8217674
6. Thomas, M. C., & Arjunan, S. P. (2022). Deep Learning Measurement Model to Segment the
Nuchal Translucency Region for the Early Identification of Down Syndrome. Measurement
Science Review, 22(4), 187–192. https://doi.org/10.2478/msr-2022-0023
7. Geremek, M., & Szklanny, K. (2021). Deep Learning-Based Analysis of Face Images as a
Screening
Tool
for
Genetic
Syndromes.
Sensors,
21(19),
6595.
https://doi.org/10.3390/s21196595
8. Pregitha, R. E., Kumar, R. S. V., & Kumar, C. E. S. (2023). Down syndrome markers
classification via dense neural network in ultrasound foetal image. Soft Computing.
https://doi.org/10.1007/s00500-023-08187-9
9. Agbolade, O., Nazri, A., Yaakob, R., Ghani, A. a. A., & Cheah, Y. K. (2020). Down Syndrome
Face Recognition: A Review. Symmetry, 12(7), 1182. https://doi.org/10.3390/sym12071182
10. Mittal, A., Gaur, H., & Mishra, M. (2019). Detection of Down Syndrome Using Deep Facial
Recognition. In Advances in intelligent systems and computing (pp. 119–130).
https://doi.org/10.1007/978-981-32-9088-4_11
11. Novel Transfer Learning Based Deep Features for Diagnosis of Down Syndrome in Children
Using Facial Images. (2024). IEEE Journals & Magazine | IEEE Xplore.
https://ieeexplore.ieee.org/abstract/document/10415433
12. Early Detection of Down Syndrome Through Ultrasound Imaging Using Deep Learning
Strategies — A Review. (2024, February 22). IEEE Conference Publication | IEEE Xplore.
https://ieeexplore.ieee.org/abstract/document/10493220
13. Detection of Rare Genetic Diseases using Facial 2D Images with Transfer Learning. (2018,
December
1).
IEEE
Conference
Publication
|
IEEE
Xplore.
https://ieeexplore.ieee.org/abstract/document/8703997?casa_token=jlJ0XrFa_CMAAAAA:xu
eKNnCP_Ha5VFsEWzZXyl7vtQwhhC3X7iFvGbxnOGkYA9OjdSfUrlQ9u7axjHAK_afg2E
oc
14. Wu, D., Qiang, J., Hong, W., Du, H., Yang, H., Zhu, H., Pan, H., Shen, Z., & Chen, S. (2024).
Artificial intelligence facial recognition system for diagnosis of endocrine and metabolic
27
syndromes based on a facial image database. Diabetes & Metabolic Syndrome, 18(4), 103003.
https://doi.org/10.1016/j.dsx.2024.103003
15. Alkhalid, F. (2024). The State of the Art Using Transfer Learning for Down Syndrome
Recognition and Data Augmentation. https://doi.org/10.2139/ssrn.4705293
16. Offerdal, K., Blaas, H. K., & Eik‐Nes, S. H. (2008). Prenatal detection of trisomy 21 by second‐
trimester ultrasound examination and maternal age in a non‐selected population of 49 314 births
in
Norway.
Ultrasound
in Obstetrics
& Gynecology,
32(4),
493–500.
https://doi.org/10.1002/uog.5373
17. Mansour, E., Yaacoub, J. J., Bakouny, Z., Assi, A., & Ghanem, I. (2017). A podoscopic and
descriptive study of foot deformities in patients with Down syndrome. Orthopaedics &
Traumatology:
Surgery
&
Research,
103(1),
123–127.
https://doi.org/10.1016/j.otsr.2016.10.001
18. Durmuşoğlu, A., Ay, M. M., & Durmuşoğlu, Z. D. U. (2020). A Classification Model for
Predicting Fetus with down Syndrome – A Study from Turkey. Applied Artificial Intelligence,
34(12), 898–915. https://doi.org/10.1080/08839514.2020.1790246
19. Orlandi, F., Bilardo, C. M., Campogrande, M., Krantz, D., Hallahan, T., Rossi, C., & Viora, E.
(2003). Measurement of nasal bone length at 11–14 weeks of pregnancy and its potential role
in Down syndrome risk assessment. Ultrasound in Obstetrics & Gynecology, 22(1), 36–39.
https://doi.org/10.1002/uog.167
20. Lao, P. J., Handen, B. L., Betthauser, T. J., Cody, K. A., Cohen, A. D., Tudorascu, D. L., Stone,
C. K., Price, J. C., Johnson, S. C., Klunk, W. E., & Christian, B. T. (2018). Imaging
neurodegeneration in Down syndrome: brain templates for amyloid burden and tissue
segmentation. Brain Imaging and Behavior, 13(2), 345–353. https://doi.org/10.1007/s11682018-9888-y
21. Shin, J. (2020). Random Subspace Ensemble Learning for Functional Near-Infrared
Spectroscopy Brain-Computer Interfaces. Frontiers in Human Neuroscience, 14.
https://doi.org/10.3389/fnhum.2020.00236
22. More, S., & Singla, J. (2021). Discrete-MultiResUNet: Segmentation and feature extraction
model for knee MR images. Journal of Intelligent & Fuzzy Systems, 41(2), 3771–3781.
https://doi.org/10.3233/jifs-211459
23. Park, J., Sofka, M., Lee, S., Kim, D., & Zhou, S. K. (2013). Automatic Nuchal Translucency
Measurement from Ultrasonography. In Lecture notes in computer science (pp. 243–250).
https://doi.org/10.1007/978-3-642-40760-4_31
24. Lai, K. W., & Supriyanto, E. (2012). Medicine and Engineering Related Researches on the
Utility of Two Dimensional Nuchal Translucency. In SpringerBriefs in applied sciences and
technology (pp. 11–45). https://doi.org/10.1007/978-981-4021-96-8_2
25. Agarwal, P., Chaudhary, M., & Nijhawan, R. (2020). An Integrated AdaBoost Algorithm for
Down Syndrome Disease Recognition. In Lecture notes in networks and systems (pp. 47–53).
https://doi.org/10.1007/978-981-15-3338-9_6
26. Qin B, Liang L, Wu J, Quan Q, Wang Z, Li D. Automatic Identification of Down Syndrome
Using Facial Images with Deep Convolutional Neural Network. Diagnostics. 2020; 10(7):487.
https://www.mdpi.com/2075-4418/10/7/487
27. V. Chandrika and S. Surendran, “Ai-enabled pregnancy risk monitoring and prediction: A
review,” in 4th EAI International Conference on Big Data Innovation for Sustainable Cognitive
Computing: BDCC 2021. Springer, 2022, pp. 29–43.
28. B. Prabakaran, P. Hamelmann, E. Ostrowski, and M. Shafique, “Fpus23: An ultrasound fetus
phantom dataset with deep neural network evaluations for fetus orientations, fetal planes, and
anatomical features,” IEEE Access, vol. PP, pp. 1–1, 01 2023.
28
29. E. Steffensen, L. Pedersen, S. Lou, and I. Vogel, “Impact of a prenatal screening program on
the down syndrome phenotype:: An interrupted time series analysis,” Acta Obstetricia et
Gynecologica Scandinavica, vol. 102, no. 6, pp. 751–759, Jun. 2023.
30. L. Zhang, D. Dong, Y. Sun, C. Hu, C. Sun, Q. Wu, and J. Tian, “Development and validation
of a deep learning model to screen for trisomy 21 during the first trimester from nuchal
ultrasonographic images,” JAMA Network Open, vol. 5, no. 6, pp. e2 217 854–e2 217 854,
2022.
31. L. Li, C. Ma, L. Sun et al., “Study on the effect of b-ultrasound nt scan in early pregnancy
combined with serum screening in early and middle pregnancy for down syndrome,”
Computational and Mathematical Methods in Medicine, vol. 2022, 2022.
32. L. M. Lopez-Garcia, A. Bataller-Calatayud, C. Sanz-Marti, and A. V. Antoli-Frances,
“Probabilistic machine learning methods for down syndrome prenatal screening: unified first
and second trimester markers multivariate bayesian risk model versus current contingent
sequential strategy,” in 2021 International Symposium on Biomedical Engineering and
Computational Biology, 2021, pp. 1–5.
33. A. Jamshidnezhad, S. M. Hosseini, J. Mohammadi-Asl, and M. Mahmudi, “An intelligent
prenatal screening system for the prediction of trisomy-21,” Informatics in Medicine Unlocked,
vol. 24, p. 100625, 2021.
34. F. He, B. Lin, K. Mou, L. Jin, and J. Liu, “A machine learning model for the prediction of down
syndrome in second trimester antenatal screening,” Clinica Chimica Acta, vol. 521, pp. 206–
211, 2021.
35. B. Qin, L. Liang, J. Wu, Q. Quan, Z. Wang, and D. Li, “Automatic identification of down
syndrome using facial images with deep convolutional neural network,” Diagnostics, vol. 10,
no. 7, p. 487, 2020.
36. A. Durmus ̧o ̆ glu, M. M. Ay, and Z. D. Unutmaz Durmu ̧ so ̆ glu, “A classification model for
predicting fetus with down syndrome–a study from turkey,” Applied Artificial Intelligence, vol.
34, no. 12, pp. 898915, 2020.
37. A. Kaul and P. Radhakrishnan, “Performance of common down syndrome screening methods
used in india with construction of an indian normogram for nuchal translucency/crown–rump
length measurements in 14,337 subjects,” The Journal of Obstetrics and Gynecology of India,
vol. 69, pp. 142–146, 2019.
38. L. Li, W. Liu, H. Zhang, Y. Jiang, X. Hu, and R. Liu, “Down syndrome prediction using a
cascaded machine learning framework designed for imbalanced and feature-correlated data,”
IEEE Access, vol. 7, pp. 97 582–97 593, 2019.
39. C. Wanapirak, P. Buddhawongsa, W. Himakalasa, A. Sarnwong, and T. Tongsong, “Fetal down
syndrome screening models for developing countries; part ii: Cost-benefit analysis,” BMC
health services research, vol. 19, pp. 1–9, 2019.
40. M. Ubavi ́ c, M. Durkovi ́ c, T. Kis et al., “Prenatal screening markers for down syndrome:
Sensitivity, specificity, positive and negative expected value method,” Journal of Medical
Biochemistry, vol. 37, no. 1, p. 62, 2018.
41. A. Koivu, T. Korpim ̈ aki, P. Kivel ̈ a, T. Pahikkala, and M. Sairanen, “Evaluation of machine
learning algorithms for improved risk assessment for down’s syndrome,” Computers in Biology
and Medicine, vol. 98, pp. 1–7, 2018.
42. B. Feng, W. Hoskins, Y. Zhang, Z. Meng, D. C. Samuels, J. Wang, R. Xia, C. Liu, J. Tang, and
Y. Guo, “Bi-stream cnn down syndrome screening model based on genotyping array,” BMC
medical genomics, vol. 11, pp. 25–33, 2018.
43. S. Ramanathan, M. Sangeetha, S. Talwai, and S. Natarajan, “Probabilistic determination of
down’s syndrome using machine learning techniques,” in 2018 International Conference on
29
Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2018, pp. 126–
132.
44. B. Feng, D. C. Samuels, W. Hoskins, Y. Guo, Y. Zhang, J. Tang, and Z. Meng, “Down
syndrome prediction/screening model based on deep learning and illumina genotyping array,”
in 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE,
2017, pp. 347–352
45. Cornejo, J.Y.R.; Pedrini, H.; Machado-Lima, A.; dos Santos Nunes, F.D.L. Down syndrome
detection based on facial features using a geometric descriptor. J. Med. Imaging 2017, 4,
044008. [CrossRef]
46. Kruszka, P.; Porras, A.R.; Sobering, A.K.; Ikolo, F.A.; La Qua, S.; Shotelersuk, V.; Chung,
B.H.; Mok, G.T.; Uwineza, A.; Mutesa, L. Down syndrome in diverse populations. Am. J. Med.
Genet. Part A 2017, 173, 42–53. [CrossRef] [PubMed]
47. Ferry, Q.; Steinberg, J.; Webber, C.; FitzPatrick, D.R.; Ponting, C.P.; Zisserman, A.; Nellåker,
C. Diagnostically relevant facial gestalt information from ordinary photos. eLife 2014, 3,
e02020. [CrossRef]
48. Zhao, Q.; Okada, K.; Rosenbaum, K.; Kehoe, L.; Zand, D.J.; Sze, R.; Summar, M.; Linguraru,
M.G. Digital facial dysmorphology for genetic screening
49. Shukla, P.; Gupta, T.; Saini, A.; Singh, P.; Balasubramanian, R. A deep learning frame-work
for recognizing developmental disorders. In Proceedings of the 2017 IEEE Winter Conference
on Applications of Computer Vision (WACV), Santa Rosa, CA, USA, 27–29 March 2017; pp.
705–714.
50. Gurovich, Y.; Hanani, Y.; Bar, O.; Nadav, G.; Fleischer, N.; Gelbman, D.; Basel-Salmon, L.;
Krawitz, P.M.; Kamphausen, S.B.; Zenker, M. Identifying facial phenotypes of genetic
disorders using deep learning. Nat. Med. 2019, 25, 60. [CrossRef] [PubMed]
30