I-Corps: Transformation Potential of a Clinical Decision Support System for the Early Detection of Endometriosis

Icahn School Of Medicine At Mount Sinai, New York NY

Investigators

Abstract

The broader impact of this I-Corps project is based on the development of an artificial intelligence-driven clinical decision support system to detect endometriosis. Endometriosis is a chronic medical condition in which tissue similar to the lining inside the uterus, called the endometrium, begins to grow outside the uterus. This condition can cause pain, irregular bleeding, and may lead to infertility. Early diagnosis of endometriosis can improve patient outcomes, increase access to care, and lead to more treatment options. This technology combines patient screening with image analysis to provide a comprehensive clinical decision support system for physicians and radiologists caring for patients at risk of this condition. This innovative approach to early-stage endometriosis detection has the potential to reduce diagnostic disparities and democratize access to high-quality care for all women. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a clinical decision support system that offers a non-invasive, objective, and accessible solution for early-stage detection of the superficial peritoneal lesions found in endometriosis. The technology combines a patient screening tool and an imaging analysis tool to identify key health indicators and risk factors to help primary care physicians and gynecologists detect early-stage endometriosis. The technology also analyzes magnetic resonance imaging (MRI) images based on a deep learning model with more than 5 years of high-quality patient data, offering radiologists a more transparent, reliable, and repeatable scoring mechanism for potential endometriosis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.