Translational research informatics
Translational Research as defined by the National Institutes of Health
includes two areas of translation.
One is the process of applying discoveries generated during research in
the laboratory, and in preclinical studies, to the development of trials and
studies in humans.
The second area of translation concerns research aimed at enhancing the
adoption of best practices in the community. Cost-effectiveness of
prevention and treatment strategies is also an important part of
translational research.
Unit 5
Legislation and Regulation: Health Insurance Portability and
Accountability Act, Certification Commission for Healthcare Information
Technology, Software Systems, Medical software, Dental software, List of
freeware health software, List of open source healthcare software, List of
neuroimaging software, Mirth, Mpro , Open Dental, Personal Health
Application .
Clinical Research Informatics: Translational research informatics, Clinical
trial management, Clinical data management system, Case report form,
Clinical coder, Clinical data acquisition, Data clarification form, Patientreported outcome.
Standards, Coding and Nomenclature: Diagnosis codes, Procedure codes.
Legislation and Regulation
Health Insurance Portability and Accountability Act (HIPAA)
Bill Clinton in 1996
Title I of HIPAA protects health insurance coverage for workers and their families
when they change or lose their jobs.
Title II of HIPAA, known as the Administrative Simplification (AS) provisions, requires
the establishment of national standards for electronic health care transactions and
national identifiers for providers, health insurance plans, and employers
The administrative simplification provisions also address the security and privacy of
health data. The standards are meant to improve the efficiency and effectiveness of
the nation's health care system by encouraging the widespread use of electronic
data interchange in the U.S. health care system
Image processing software
Free
• 3DSlicer
• AFNICell
• Cognition
• CellProfiler
• DlibEndrov
• FijiF
• MRIB
• Software Library
• Free Surfer
• GemIdentGNU Octaveilastik
• ImageJ
• ITKInVesaliusITKSNAPKNIMEMangoOpenCVOsiriXVIGRAVXL
Proprietary
•
•
•
•
•
•
•
•
•
•
Amira
Analyze
Aphelion
Bitplane
IDL
Mathematica
MATLAB
Mimics
MountainsMap
Visage SDK
Analyze (imaging software)
Analyze is a software package developed by the Biomedical Imaging Resource (BIR) at Mayo
Clinic for multi-dimensional display, processing, and measurement of multi-modality biomedical
images. It is a commercial program and is used for medical tomographic scans from magnetic
resonance imaging, computed tomography and positron emission tomography.
The Analyze 7.5 file format[1] has been widely used in the functional neuroimaging field, and
other programs such as SPM, FreeSurfer, AIR, MRIcro and Mango are able to read and write the
format. The files can be used to store voxel-based volumes. One data item consists of two files:
One file with the actual data in a binary format with the filename extension .img and another file
(header with filename extension .hdr) with information about the data such as voxel size and
number of voxels in each dimension. SPM has defined changes to this format, among other
things the voxel ordering within the file.
SEGMENTATION
•Interactive volume segmentation
•Semi-automatic object segmentation
•Comprehensive manual segmentation
•Dual-input segmentation
•Surface generation
TRANSFORM
•Spatial transformations such as cropping and flipping
•Interactive orthogonal and oblique reslicing
•Intensity-based transformations
•Mathematical processing
•Image correction
3D Slicer
3DSlicer
3D Slicer (Slicer) is a free, open source software package for image analysis and scientific visualization.
Slicer is used in a variety of medical applications, including autism, multiple sclerosis, systemic lupus erythematosus,
prostate cancer, schizophrenia, orthopedic biomechanics, COPD, cardiovascular disease and neurosurgery.
Slicer's capabilities include:
• Handling DICOM images and reading/writing a variety of
other formats
• Interactive visualization of volumetric Voxel images,
polygonal meshes, and volume renderings
• Manual editing
• Fusion and co-registering of data using rigid and non-rigid
algorithms
• Automatic image segmentation
• Analysis and visualization of diffusion tensor imaging data
• Tracking of devices for image-guided procedures.
What is FreeSurfer?
• A suite of software tools for the analysis of neuroimaging data
• Full characterizes anatomy
– Cortex – thickness, folding patterns, ROIs
– Subcortical – structure boundaries
• Surface-based inter-subject registration
• Multi-modal integration
– fMRI (task, rest, retinotopy)
– DTI tractography
– PET, MEG, EEG
Why is FreeSurfer special?
• There are other cortical and subcortical tools:
– BrainVoyager, Caret, BrainVisa, SPM, FSL (of late)
• Each has varying degrees of segmentation accuracy w/ varying
levels of user intervention
• FreeSurfer is highly specialized in it’s:
– cortical surface representation from the grey matter
segmentation
– surface-based group registration capabilities
– accuracy of subcortical structure measurements
Why FreeSurfer?
•
Anatomical analysis is not like functional analysis – it is
completely stereotyped.
•
Registration to a template (e.g. MNI/Talairach) doesn’t
account for individual anatomy.
•
Even if you don’t care about the anatomy, anatomical models
allow functional analysis not otherwise possible.
FreeSurfer
ImageJ
• An adaptation of NIH image for the Java platform.
• Can run on any computer systems that can run Java (Sun
Microsystems)
• Open source
• Two powerful scripting languages
• Java Plugins
• Macro Language
• Continual Upgrades
• Active community of several thousand users
The Image Histogram
Log Scale
The histogram shows the number of pixels of
each value, regardless of location. The log
display allows for the visualization of minor
components. Note that there are unused pixel
values
Brightness Adjustment
The brightness adjustment essentially adds or subtracts a constant to every pixel,
causing a shift in the histogram along the x axis, but no change in the distribution
Contrast Enhancement
For contrast enhancement, a lower value, in this case, 88, is set at zero, and a higher
value, 166, is set at 255. The values of each of the pixels are adjusted proportionately.
Note that because of the integer values, not all of the pixel values are used.
Medical and biological signal applications
Medical monitor
In medicine, monitoring is the observation of a
disease, condition or one or several medical
parameters over time
Cardiac monitoring
• Continuous electrocardiography
• Holter monitor
• Invasive Swan-Ganz catheter
Hemodynamic monitoring
• blood pressure and blood flow
• invasively through an inserted blood pressure transducer
• noninvasively with an inflatable blood pressure cuff
Respiratory monitoring
•
Pulse oximetry which involves measurement of the saturated percentage of oxygen in the blood,
referred to as SpO2, and measured by an infrared finger cuff
•
Capnography, which invoolves CO2 measurements, referred to as EtCO2 or end-tidal carbon dioxide
concentration.
•
The respiratory rate monitored as such is called AWRR or airway respiratory rate)
Neurological monitoring
•
intracranial pressure
•
electroencephalography, gas anesthetic concentrations, bispectral index (BIS), etc.
•
brain EEG monitors have a larger multichannel capability
Blood glucose monitoring
Childbirth monitoring
Body temperature monitoring through an adhesive pad containing a thermoelectric transducer
Components
Sensor - biosensors and mechanical sensors
Translating component
Display device
Physiological data are displayed continuously
on a CRT, LED or LCD screen numerical
readouts such as maximum, minimum and
average values, pulse and respiratory
frequencies, and so on
Communication links
An anesthetic machine with integrated systems
for monitoring of several vital parameters,
including blood pressure and heart rate.
Mobile appliances
Applications
Blood glucose monitoring
Stress monitoring
Bio sensors may provide warnings when stress levels signs are rising before human can notice it and
provide alerts and suggestions.
Serotonin biosensor
Future serotonin biosensors may assist with mood disorders and depression.
Continuous blood test based nutrition
In the field of evidence-based nutrition, a lab-on-a-chip implant that can run 24/7 blood tests may
provide a continuous results and a computer can provide nutrition suggestions or alerts.
Psychiatrist-on-a-chip
In clinical brain sciences drug delivery and in vivo Bio-MEMS based biosensors may assist with
preventing and early treatment of mental disorders
Epilepsy monitoring
In epilepsy, next generations of long-term video-EEG monitoring may predict epileptic seizure and
prevent them with changes of daily life activity like sleep, stress, nutrition and mood management.
Toxicity monitoring
Smart biosensors may detect toxic materials such mercury and lead and provide alerts
Wireless Capsule Endoscopy
The PASCAL Dynamic Contour Tonometer
Holter monitor
Holter is a portable device for
continuously monitoring various
electrical
activity
of
the
cardiovascular system for at least 24
hours (often for two weeks at a
time).
The Holter monitor is named after physicist
Norman J. Holter
Atrial fibrillation recorded by a Holter monitor
Canine Holter Monitor with DogLeggs Vest
A Holter monitor can be worn for many days
without causing significant discomfort.
Automated ECG interpretation
Automated ECG interpretation is the use of artificial intelligence and pattern
recognition software and knowledge bases to carry out automatically the
interpretation, test reporting and computer-aided diagnosis of
electrocardiogram tracings obtained usually from a patient.
ECG Waveform
Phases
1. A digital representation of each recorded ECG channel is obtained, by means of an analogdigital conversion device and a special data acquisition software or a digital signal processing
(DSP) chip.
2. The resulting digital signal is processed by a series of specialized algorithms, which start by
conditioning it, e.g., removal of noise, baselevel variation, etc.
3. Feature extraction: mathematical analysis is now performed on the clean signal of all
channels, to identify and measure a number of features which are important for
interpretation and diagnosis, this will constitute the input to AI-based programs, such as the
peak amplitude, area under the curve, displacement in relation to baseline, etc., of the P, Q,
R, S and T waves, the time delay between these peaks and valleys, heart rate frequency
(instantaneous and average), and many others. Some sort of secondary processing such as
Fourier analysis and wavelet analysis may also be performed in order to provide input to
pattern recognition-based programs.
4. Logical processing and pattern recognition, using rule-based expert systems,
probabilistic Bayesian analysis or fuzzy logics algorithms, cluster analysis, artificial
neural networks, genetic algorithms and others techniques are used to derive
conclusions, interpretation and diagnosis
5. A reporting program is activated and produces a proper display of original and
calculated data, as well as the results of automated interpretation.
6. In some applications, such as automatic defibrillators, an action of some sort may be
triggered by results of the analysis, such as the occurrence of an atrial fibrillation or a
cardiac arrest, the sounding of alarms in a medical monitor in intensive-care unit
applications, and so on.
MECIF Protocol
The MECIF Protocol (Medical Computer Interface Protcol), is a rare communications
protocol originally developed by Hewlett-Packard to allow external devices (e.g.
computers) to communicate with certain Hewlett-Packard patient monitors.
It is a client–server based protocol that uses a modified RS-232 cable to allow a
client (e.g. a computer) to send commands to a server (e.g. patient monitor).
The protocol can be used to retrieve vital data from patient monitors, such as ECG,
blood pressure and heart-rate signals.
SCP-ECG
SCP-ECG, which stands for Standard communications protocol for computer assisted electrocardiography,
is a standard for ECG traces, annotations, and metadata, that specifies the interchange format and a
messaging procedure for ECG cart-to-host communication and for retrieval of SCP-ECG records from the
host to the ECG cart. It is defined in the joint ANSI/AAMI standard EC71:2001 and in the CEN standard EN
1064:2005.
History
The SCP Standard was first developed between 1989 to 1991 during a European AIM R&D project.
External links
"OpenECG" — The [OpenECG] Group supports SCP-ECG by providing and supporting open source
implementations and consistent application the standard.
Other ECG data formats
DICOM, HL7 aECG
European Data Format
European Data Format (EDF) is a standard file format designed for exchange and storage
of medical time series.
Being an open and non-proprietary format, EDF(+) is commonly used to archive,
exchange and analyse data from commercial devices in a format that is independent of
the acquisition system.
EDF was published in 1992 and stores multichannel data, allowing different sample rates
for each signal. Internally it includes a header and one or more data records. The header
contains some general information (patient identification, start time...) and technical
specs of each signal (calibration, sampling rate, filtering, ...), coded as ASCII characters.
The data records contain samples as little-endian 16-bit integers.
EDF+ was published in 2003. EDF+ has applications in PSG, electroencephalography
(EEG), electrocardiography (ECG), electromyography (EMG), and Sleep scoring. EDF+ can
also be used for nerve conduction studies, evoked potentials and other data acquisition
studies
OpenXDF
The Open eXchange Data Format, or OpenXDF, is an open, XML-based standard for the
digital storage and exchange of time-series physiological signals and metadata. OpenXDF
primarily focuses on electroencephalography and polysomnography.
History
Neurotronics began work on OpenXDF in 2003 with the goal of providing a modern, open,
and extensible file format with which clinicians and researchers can share physiological data
and metadata, such as signal data, signal montages, patient demographics, and event
logs.[citation needed] Neurotronics released the first draft of the OpenXDF Specification
just before the 18th meeting of the Associated Professional Sleep Societies in 2004.
Neurotronics has since relinquished control of the format to the OpenXDF Consortium. As
of version 1.0, OpenXDF is 100% backward compatible with the European Data Format
(EDF), the current defacto standard format for physiological data exchange