Interdisciplinary Platform for Bruise Image Research

Presentation Time: 02:15 PM - 02:30 PM

Abstract Keywords: Data Sharing, Deep Learning, Racial disparities, Data Transformation/ETL, Imaging Informatics
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics

Current research activities on bruise analysis and alternative light sources (ALS) are hampered by lack of data, difficulties in collecting bruise images and linked clinical data, and inability to collaborate across institutions. Access to very large datasets is needed especially when deep learning methods are applied to construct models for classifying images, but also when traditional statistical analyses are performed. This presentation describes a research platform that integrates longitudinal bruise images with clinical and measurement data. The web-based platform allows users (researchers, practitioners) to browse and compare images, access structured data, upload their own images and data, annotate images, and link to deep learning and analytic methods. The platform offers multiple levels of security and is designed with HIPAA compliance as a core functionality. The platform is currently populated with about 30,000 images along with structured data. Additional data are collected from partner institutions, including EHR data. Deep learning methods applied for bruise image classification show promising results in detecting bruises.

Speaker(s):
Janusz Wojtusiak, PhD
George Mason University

Author(s):
Janusz Wojtusiak, PhD - George Mason University; Mohammad Qodrati, MD - George Mason University; Michał Markiewicz, PhD - Jagiellonian University; Kiyarash Aminfar, MS - George Mason University; David Lattanzi, PhD - George Mason University; Katherine Scafide, RN, PhD - George Mason University;