Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance

• DOI: 10.1007/s10278-021-00470-1
• Publication Date: 2021-06-15T22:02:26Z
• Authors: Jeffrey D. Rudie; Jeffrey Duda; Michael Tran Duong; Po-Hao Chen; Long Xie; Robert Kurtz; Jeffrey B. Ware; Joshua Choi; Raghav R. Mattay; Emmanuel J. Botzo...; James C. Gee; R. Nick Bryan; Tessa S. Cook; Suyash Mohan; Ilya M. Nasrallah; Andreas M. Rausch...
• Keywords: Artificial intelligence; Clinical Sciences; Bayesian inference; Clinical sciences; 03 medical and health sciences; Deep Learning; 0302 clinical medicine; Clinical Research; Augmented performance; Humans; Biomedical and Clinical Sciences; Neurosciences; Brain; Deep learning; Clinical decision support; Bayes Theorem; U-Net; Magnetic Resonance Imaging; 3. Good health; Nuclear Medicine & Medical Imaging; Neuroradiology; Brain MRI; Biomedical Imaging; Convolutional neural networks; Radiology

Automated quantitative and probabilistic medical image analysis has the potential to improve the accuracy and efficiency of the radiology workflow. We sought to determine whether AI systems for brain MRI diagnosis could be used as a clinical decision support tool to augment radiologist performance. We utilized previously developed AI systems that combine convolutional neural networks and expert-derived Bayesian networks to distinguish among 50 diagnostic entities on multimodal brain MRIs. We tested whether these systems could augment radiologist performance through an interactive clinical decision support tool known as Adaptive Radiology Interpretation and Education System (ARIES) in 194 test cases. Four radiology residents and three academic neuroradiologists viewed half of the cases unassisted and half with the results of the AI system displayed on ARIES. Diagnostic accuracy of radiologists for top diagnosis (TDx) and top three differential diagnosis (T3DDx) was compared with and without ARIES. Radiology resident performance was significantly better with ARIES for both TDx (55% vs 30%; P < .001) and T3DDx (79% vs 52%; P = 0.002), with the largest improvement for rare diseases (39% increase for T3DDx; P < 0.001). There was no significant difference between attending performance with and without ARIES for TDx (72% vs 69%; P = 0.48) or T3DDx (86% vs 89%; P = 0.39). These findings suggest that a hybrid deep learning and Bayesian inference clinical decision support system has the potential to augment diagnostic accuracy of non-specialists to approach the level of subspecialists for a large array of diseases on brain MRI.