Convolutional Neural Networks for Automated Fracture Detection and Localization on Wrist Radiographs.

Autor:	Thian YL; Department of Diagnostic Imaging (Y.L.T., P.J., D.S., V.E.Y.C.) and Department of Electrical and Computer Engineering (Y.L., R.T.T.), National University of Singapore, 5 Lower Kent Ridge Rd, Singapore 119074; and Science Division, Yale-NUS College, Singapore (R.T.T.)., Li Y; Department of Diagnostic Imaging (Y.L.T., P.J., D.S., V.E.Y.C.) and Department of Electrical and Computer Engineering (Y.L., R.T.T.), National University of Singapore, 5 Lower Kent Ridge Rd, Singapore 119074; and Science Division, Yale-NUS College, Singapore (R.T.T.)., Jagmohan P; Department of Diagnostic Imaging (Y.L.T., P.J., D.S., V.E.Y.C.) and Department of Electrical and Computer Engineering (Y.L., R.T.T.), National University of Singapore, 5 Lower Kent Ridge Rd, Singapore 119074; and Science Division, Yale-NUS College, Singapore (R.T.T.)., Sia D; Department of Diagnostic Imaging (Y.L.T., P.J., D.S., V.E.Y.C.) and Department of Electrical and Computer Engineering (Y.L., R.T.T.), National University of Singapore, 5 Lower Kent Ridge Rd, Singapore 119074; and Science Division, Yale-NUS College, Singapore (R.T.T.)., Chan VEY; Department of Diagnostic Imaging (Y.L.T., P.J., D.S., V.E.Y.C.) and Department of Electrical and Computer Engineering (Y.L., R.T.T.), National University of Singapore, 5 Lower Kent Ridge Rd, Singapore 119074; and Science Division, Yale-NUS College, Singapore (R.T.T.)., Tan RT; Department of Diagnostic Imaging (Y.L.T., P.J., D.S., V.E.Y.C.) and Department of Electrical and Computer Engineering (Y.L., R.T.T.), National University of Singapore, 5 Lower Kent Ridge Rd, Singapore 119074; and Science Division, Yale-NUS College, Singapore (R.T.T.).
Jazyk:	angličtina
Zdroj:	Radiology. Artificial intelligence [Radiol Artif Intell] 2019 Jan 30; Vol. 1 (1), pp. e180001. Date of Electronic Publication: 2019 Jan 30 (Print Publication: 2019).
DOI:	10.1148/ryai.2019180001
Abstrakt:	Purpose: To demonstrate the feasibility and performance of an object detection convolutional neural network (CNN) for fracture detection and localization on wrist radiographs. Materials and Methods: Institutional review board approval was obtained with waiver of consent for this retrospective study. A total of 7356 wrist radiographic studies were extracted from a hospital picture archiving and communication system. Radiologists annotated all radius and ulna fractures with bounding boxes. The dataset was split into training (90%) and validation (10%) sets and used to train fracture localization models for frontal and lateral images. Inception-ResNet Faster R-CNN architecture was implemented as a deep learning model. The models were tested on an unseen test set of 524 consecutive emergency department wrist radiographic studies with two radiologists in consensus as the reference standard. Per-fracture, per-image (ie, per-view), and per-study sensitivity and specificity were determined. Area under the receiver operating characteristic curve (AUC) analysis was performed. Results: The model detected and correctly localized 310 (91.2%) of 340 and 236 (96.3%) of 245 of all radius and ulna fractures on the frontal and lateral views, respectively. The per-image sensitivity, specificity, and AUC were 95.7% (95% confidence interval [CI]: 92.4%, 97.8%), 82.5% (95% CI: 77.4%, 86.8%), and 0.918 (95% CI: 0.894, 0.941), respectively, for the frontal view and 96.7% (95% CI: 93.6%, 98.6%), 86.4% (95% CI: 81.9%, 90.2%), and 0.933 (95% CI: 0.912, 0.954), respectively, for the lateral view. The per-study sensitivity, specificity, and AUC were 98.1% (95% CI: 95.6%, 99.4%), 72.9% (95% CI: 67.1%, 78.2%), and 0.895 (95% CI: 0.870, 0.920), respectively. Conclusion: The ability of an object detection CNN to detect and localize radius and ulna fractures on wrist radiographs with high sensitivity and specificity was demonstrated.© RSNA, 2019. (2019 by the Radiological Society of North America, Inc.)
Databáze:	MEDLINE
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