Detection of Sacral Fractures on Radiographs Using Artificial Intelligence.

Autor:	Inagaki N; Department of Orthopedic Surgery, Jikei University School of Medicine, Minato, Tokyo, Japan., Nakata N; Department of Radiology, Jikei University School of Medicine, Minato, Tokyo, Japan., Ichimori S; Department of Orthopedic Surgery, Jikei University School of Medicine, Minato, Tokyo, Japan., Udaka J; Department of Orthopedic Surgery, Jikei University School of Medicine, Minato, Tokyo, Japan., Mandai A; Department of Orthopedic Surgery, Jikei University School of Medicine, Minato, Tokyo, Japan., Saito M; Department of Orthopedic Surgery, Jikei University School of Medicine, Minato, Tokyo, Japan.
Jazyk:	angličtina
Zdroj:	JB & JS open access [JB JS Open Access] 2022 Sep 14; Vol. 7 (3). Date of Electronic Publication: 2022 Sep 14 (Print Publication: 2022).
DOI:	10.2106/JBJS.OA.22.00030
Abstrakt:	Sacral fractures are often difficult to diagnose on radiographs. Computed tomography (CT) and magnetic resonance imaging (MRI) can improve the detection rate but cannot always be performed. The accuracy of artificial intelligence (AI) in detecting orthopaedic fractures is now comparable with that of orthopaedic specialists. However, the ability of AI to detect sacral fractures has not been investigated, to our knowledge. We hypothesized that the ability to detect sacral fractures on radiographs could be improved by using AI, and aimed to develop an AI model to detect sacral fractures accurately on radiographs with better accuracy than that of orthopaedic surgeons. Methods: Subjects were patients with suspected pelvic fractures for whom radiographs and CT scans had been obtained. The radiographs were labeled according to sacral fracture status based on CT results. The data set was divided into a training set (2,038 images) and a test set (200 images). Eight convolutional neural network (CNN) models were trained using the training set. Post-trained models were used to evaluate their discrimination ability. The detection ability of 4 experienced orthopaedic surgeons was also measured using the same test set. The results of fracture assessment by the orthopaedic surgeons were compared with those of the 3 CNNs with the greatest area under the receiver operating characteristic curve. Results: Among the 8 trained models, the highest areas under the curve were for InceptionV3 (0.989), Xception (0.987), and Inception ResNetV2 (0.984). The detection rate was significantly higher for these 3 CNNs than for the orthopaedic surgeons. Conclusions: By enhancing the processing of probabilistic tasks and the communication of their results, AI may be better able to detect sacral fractures than orthopaedic surgeons. Level of Evidence: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence. Competing Interests: Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJSOA/A414). (Copyright © 2022 The Authors. Published by The Journal of Bone and Joint Surgery, Incorporated. All rights reserved.)
Databáze:	MEDLINE
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