Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments

Background and purpose - Artificial intelligence has rapidly become a powerful method in image analysis with the use of convolutional neural networks (CNNs). We assessed the ability of a CNN, with a fast object detection algorithm previously identifying the regions of interest, to detect distal radi...

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Veröffentlicht in:Acta orthopaedica Jg. 90; H. 4; S. 394 - 400
Hauptverfasser: Gan, Kaifeng, Xu, Dingli, Lin, Yimu, Shen, Yandong, Zhang, Ting, Hu, Keqi, Zhou, Ke, Bi, Mingguang, Pan, Lingxiao, Wu, Wei, Liu, Yunpeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Taylor & Francis 04.07.2019
Medical Journals Sweden
Schlagworte:
Adult
Aged
Aged, 80 and over
Algorithms
Artificial Intelligence
Deep Learning
Female
Humans
Male
Middle Aged
Neural Networks, Computer
Radiographic Image Interpretation, Computer-Assisted - methods
Radiography
Radius Fractures - diagnosis
Radius Fractures - diagnostic imaging
ROC Curve
Sensitivity and Specificity
Wrist Injuries - diagnosis
Wrist Injuries - diagnostic imaging
Young Adult
ISSN:1745-3674, 1745-3682, 1745-3682
Online-Zugang:Volltext
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Zusammenfassung:Background and purpose - Artificial intelligence has rapidly become a powerful method in image analysis with the use of convolutional neural networks (CNNs). We assessed the ability of a CNN, with a fast object detection algorithm previously identifying the regions of interest, to detect distal radius fractures (DRFs) on anterior-posterior (AP) wrist radiographs. Patients and methods - 2,340 AP wrist radiographs from 2,340 patients were enrolled in this study. We trained the CNN to analyze wrist radiographs in the dataset. Feasibility of the object detection algorithm was evaluated by intersection of the union (IOU). The diagnostic performance of the network was measured by area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity, and Youden Index; the results were compared with those of medical professional groups. Results - The object detection model achieved a high average IOU, and none of the IOUs had a value less than 0.5. The AUC of the CNN for this test was 0.96. The network had better performance in distinguishing images with DRFs from normal images compared with a group of radiologists in terms of the accuracy, sensitivity, specificity, and Youden Index. The network presented a similar diagnostic performance to that of the orthopedists in terms of these variables. Interpretation - The network exhibited a diagnostic ability similar to that of the orthopedists and a performance superior to that of the radiologists in distinguishing AP wrist radiographs with DRFs from normal images under limited conditions. Further studies are required to determine the feasibility of applying our method as an auxiliary in clinical practice under extended conditions.
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2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group, on behalf of the Nordic Orthopedic Federation. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits ­unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
DOI 10.1080/17453674.2019.1600125
ISSN:1745-3674
1745-3682
1745-3682
DOI:10.1080/17453674.2019.1600125