• Andreas M Rauschecker, Jeffrey D Rudie, Long Xie, Jiancong Wang, Michael Tran Duong, Emmanuel J Botzolakis, Asha M Kovalovich, John Egan, Tessa C Cook, R Nick Bryan, Ilya M Nasrallah, Suyash Mohan, and James C Gee.
• From the Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Ave, Room S-261, Box 0628, San Francisco, CA 94143-0628 (A.M.R.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 (A.M.R., J.D.R., L.X., J.W., M.T.D., A.M.K., J.E., T.C.C., I.M.N., S.M., J.C.G.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); and Department of Radiology, University of Texas at Austin, Austin, TX (R.N.B.).
• Radiology. 2020 Jun 1; 295 (3): 626-637.

AbstractBackground Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for generation of differential diagnoses at brain MRI compared with radiologists. Materials and Methods This retrospective study tested performance of an AI system for probabilistic diagnosis in patients with 19 common and rare diagnoses at brain MRI acquired between January 2008 and January 2018. The AI system combines data-driven and domain-expertise methodologies, including deep learning and Bayesian networks. First, lesions were detected by using deep learning. Then, 18 quantitative imaging features were extracted by using atlas-based coregistration and segmentation. Third, these image features were combined with five clinical features by using Bayesian inference to develop probability-ranked differential diagnoses. Quantitative feature extraction algorithms and conditional probabilities were fine-tuned on a training set of 86 patients (mean age, 49 years ± 16 [standard deviation]; 53 women). Accuracy was compared with radiology residents, general radiologists, neuroradiology fellows, and academic neuroradiologists by using accuracy of top one, top two, and top three differential diagnoses in 92 independent test set patients (mean age, 47 years ± 18; 52 women). Results For accuracy of top three differential diagnoses, the AI system (91% correct) performed similarly to academic neuroradiologists (86% correct; P = .20), and better than radiology residents (56%; P < .001), general radiologists (57%; P < .001), and neuroradiology fellows (77%; P = .003). The performance of the AI system was not affected by disease prevalence (93% accuracy for common vs 85% for rare diseases; P = .26). Radiologists were more accurate at diagnosing common versus rare diagnoses (78% vs 47% across all radiologists; P < .001). Conclusion An artificial intelligence system for brain MRI approached overall top one, top two, and top three differential diagnoses accuracy of neuroradiologists and exceeded that of less-specialized radiologists. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Zaharchuk in this issue.

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