no code implementations • 15 Dec 2022 • Engin Dikici, Xuan Nguyen, Noah Takacs, Luciano M. Prevedello
During the deployment, a given test data's LSM distribution is processed to detect its deviation from the forced distribution; hence, the AI system could predict its generalizability status for any previously unseen data set.
no code implementations • 10 Nov 2021 • Engin Dikici, Xuan V. Nguyen, Matthew Bigelow, John. L. Ryu, Luciano M. Prevedello
The framework utilizing only the labeled exams produced 9. 23 false positives for 90% BM detection sensitivity; whereas, the framework using the introduced learning strategy led to ~9% reduction in false detections (i. e., 8. 44) for the same sensitivity level.
no code implementations • 27 May 2021 • Engin Dikici, Xuan V. Nguyen, Matthew Bigelow, Luciano M. Prevedello
In this study, we introduce a novel BM candidate detection CNN (cdCNN) to replace this classical IP stage.
no code implementations • 10 Aug 2020 • Richard D. White, Barbaros S. Erdal, Mutlu Demirer, Vikash Gupta, Matthew T. Bigelow, Engin Dikici, Sema Candemir, Mauricio S. Galizia, Jessica L. Carpenter, Thomas P. O Donnell, Abdul H. Halabi, Luciano M. Prevedello
The two-phase approach consisted of (1) Phase 1 - focused on the development and preliminary testing of an algorithm for vessel-centerline extraction classification in a balanced study population (n = 500 with 50% disease prevalence) derived by retrospective random case selection; and (2) Phase 2 - concerned with simulated-clinical Trialing of the developed algorithm on a per-case basis in a more real-world study population (n = 100 with 28% disease prevalence) from an ED chest-pain series.
