1. Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning.
- Author
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Krogue JD, Cheng KV, Hwang KM, Toogood P, Meinberg EG, Geiger EJ, Zaid M, McGill KC, Patel R, Sohn JH, Wright A, Darger BF, Padrez KA, Ozhinsky E, Majumdar S, and Pedoia V
- Abstract
Purpose: To investigate the feasibility of automatic identification and classification of hip fractures using deep learning, which may improve outcomes by reducing diagnostic errors and decreasing time to operation., Materials and Methods: Hip and pelvic radiographs from 1118 studies were reviewed, and 3026 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous open reduction and internal fixation, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (or DenseNet) was trained on a subset of the bounding box images, and its performance was evaluated on a held-out test set and by comparison on a 100-image subset with two groups of human observers: fellowship-trained radiologists and orthopedists; senior residents in emergency medicine, radiology, and orthopedics., Results: The binary accuracy for detecting a fracture of this model was 93.7% (95% confidence interval [CI]: 90.8%, 96.5%), with a sensitivity of 93.2% (95% CI: 88.9%, 97.1%) and a specificity of 94.2% (95% CI: 89.7%, 98.4%). Multiclass classification accuracy was 90.8% (95% CI: 87.5%, 94.2%). When compared with the accuracy of human observers, the accuracy of the model achieved an expert-level classification, at the very least, under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance in the multiclass classification., Conclusion: A deep learning model identified and classified hip fractures with expert-level performance, at the very least, and when used as an aid, improved human performance, with aided resident performance approximating that of unaided fellowship-trained attending physicians. Supplemental material is available for this article. © RSNA, 2020., Competing Interests: Disclosures of Conflicts of Interest: J.D.K. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: since April 2019, author is paid consultant with equity stake in Kaliber Labs (not related to the content of this study); Other relationships: disclosed no relevant relationships. K.V.C. disclosed no relevant relationships. K.M.H. disclosed no relevant relationships. P.T. disclosed no relevant relationships. E.G.M. disclosed no relevant relationships. E.J.G. disclosed no relevant relationships. M.Z. disclosed no relevant relationships. K.C.M. disclosed no relevant relationships. R.P. disclosed no relevant relationships. J.H.S. disclosed no relevant relationships. A.W. disclosed no relevant relationships. B.F.D. disclosed no relevant relationships. K.A.P. disclosed no relevant relationships. E.O. disclosed no relevant relationships. S.M. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author is consultant for Smith Research; institution receives grants from GE Healthcare and Samumed; author receives travel support from ISMRM; institution has patent issued and licensed (Nociscan); institution receives patent royalties from UCOP. Other relationships: disclosed no relevant relationships. V.P. disclosed no relevant relationships., (2020 by the Radiological Society of North America, Inc.)
- Published
- 2020
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