Big data in multiple sclerosis: development of a web-based longitudinal study viewer in an imaging informatics-based eFolder system for complex data analysis and management

Kevin Ma; Ximing Wang; Alex Lerner; Mark Shiroishi; Lilyana Amezcua; Brent Liu

doi:10.1117/12.2082650

17 March 2015 Big data in multiple sclerosis: development of a web-based longitudinal study viewer in an imaging informatics-based eFolder system for complex data analysis and management

Kevin Ma, Ximing Wang, Alex Lerner, Mark Shiroishi, Lilyana Amezcua, Brent Liu

Author Affiliations +

Proceedings Volume 9418, Medical Imaging 2015: PACS and Imaging Informatics: Next Generation and Innovations; 941809 (2015) https://doi.org/10.1117/12.2082650
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

In the past, we have developed and displayed a multiple sclerosis eFolder system for patient data storage, image viewing, and automatic lesion quantification results stored in DICOM-SR format. The web-based system aims to be integrated in DICOM-compliant clinical and research environments to aid clinicians in patient treatments and disease tracking. This year, we have further developed the eFolder system to handle big data analysis and data mining in today’s medical imaging field. The database has been updated to allow data mining and data look-up from DICOM-SR lesion analysis contents. Longitudinal studies are tracked, and any changes in lesion volumes and brain parenchyma volumes are calculated and shown on the webbased user interface as graphical representations. Longitudinal lesion characteristic changes are compared with patients’ disease history, including treatments, symptom progressions, and any other changes in the disease profile. The image viewer is updated such that imaging studies can be viewed side-by-side to allow visual comparisons. We aim to use the web-based medical imaging informatics eFolder system to demonstrate big data analysis in medical imaging, and use the analysis results to predict MS disease trends and patterns in Hispanic and Caucasian populations in our pilot study. The discovery of disease patterns among the two ethnicities is a big data analysis result that will help lead to personalized patient care and treatment planning.

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Kevin Ma, Ximing Wang, Alex Lerner, Mark Shiroishi, Lilyana Amezcua, and Brent Liu "Big data in multiple sclerosis: development of a web-based longitudinal study viewer in an imaging informatics-based eFolder system for complex data analysis and management", Proc. SPIE 9418, Medical Imaging 2015: PACS and Imaging Informatics: Next Generation and Innovations, 941809 (17 March 2015); https://doi.org/10.1117/12.2082650

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KEYWORDS

Data analysis

Brain

Imaging systems

Computer aided design

Databases

Magnetic resonance imaging

Data mining

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