Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance.
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....
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 4; pp. 1049 - 1059 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152559499&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152559499 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2021 vid: 34 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152559499 150906929 152559499 152559499 10.1007/s10278-021-00470-1 152559499 ppf: 1049 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance. aug: au: Rudie, Jeffrey D. Duda, Jeffrey Duong, Michael Tran Chen, Po-Hao Xie, Long Kurtz, Robert Ware, Jeffrey B. Choi, Joshua Mattay, Raghav R. Botzolakis, Emmanuel J. Gee, James C. Bryan, R. Nick Cook, Tessa S. Mohan, Suyash Nasrallah, Ilya M. Rauschecker, Andreas M. affil: Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA sug: subj: Brain Magnetic Resonance Imaging Deep Learning Probability Decision Support Systems, Clinical Specialties, Medical Interns and Residents Clinical Competence Human Female Male Adult Middle Age Aged Descriptive Statistics Neural Networks (Computer) Neuroradiography Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female Male ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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