SBIR Phase I: AI-based system for analyzing multiparametric MRI scans for prostate lesion detection

Award ID: 2151532

The broader impact/commercial potential of the Small Business Innovation Research (SBIR) Phase I project will be to improve prostate cancer diagnostics. This project proposes an artificial intelligence (AI)-based platform that overcomes current shortcomings with manual magnetic resonance imaging (MRI) assessment, currently conducted on high-quality machines with results read by expert radiologists at a few locations around the country. AI enables MRI analysis that is automated, standardized, and more accurate. This will reduce unnecessary and invasive biopsy and/or treatment for low-risk prostate cancers, as well as physician time and effort to run and read MRIs. Furthermore, it will enable non-expert centers to accurately diagnose patients without requiring extensive testing, top-tier MRI equipment, or invasive surgery. This Small Business Innovation Research (SBIR) Phase I project develops a novel prostate cancer diagnostic platform that leverages the power of AI-based image analysis for high sensitivity and specificity. While prostate cancer diagnostics rely heavily on MRIs, the accuracy of these assessments depends on both expert experience and MRI quality. The machine learning solution is able to make global assessments on the likelihood of detected lesions being clinically significant (i.e., Gleason 3+4/grade group 2 or higher), better informing clinicians on appropriate treatment. This project proposes the following technology development objectives: 1) Develop a method to measure MRI quality, 2) Create a Generative Adversarial Network (GAN) Framework to repair images from poor to higher quality, and 3) Prospectively evaluate new framework to measure the overall performance of the AI algorithm. 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.

NSF Program Director: Alastair Monk