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- Title
Deep Learning–Assisted Identification of Femoroacetabular Impingement (FAI) on Routine Pelvic Radiographs.
- Authors
Hoy, Michael K.; Desai, Vishal; Mutasa, Simukayi; Hoy, Robert C.; Gorniak, Richard; Belair, Jeffrey A.
- Abstract
To use a novel deep learning system to localize the hip joints and detect findings of cam-type femoroacetabular impingement (FAI). A retrospective search of hip/pelvis radiographs obtained in patients to evaluate for FAI yielded 3050 total studies. Each hip was classified separately by the original interpreting radiologist in the following manner: 724 hips had severe cam-type FAI morphology, 962 moderate cam-type FAI morphology, 846 mild cam-type FAI morphology, and 518 hips were normal. The anteroposterior (AP) view from each study was anonymized and extracted. After localization of the hip joints by a novel convolutional neural network (CNN) based on the focal loss principle, a second CNN classified the images of the hip as cam positive, or no FAI. Accuracy was 74% for diagnosing normal vs. abnormal cam-type FAI morphology, with aggregate sensitivity and specificity of 0.821 and 0.669, respectively, at the chosen operating point. The aggregate AUC was 0.736. A deep learning system can be applied to detect FAI-related changes on single view pelvic radiographs. Deep learning is useful for quickly identifying and categorizing pathology on imaging, which may aid the interpreting radiologist.
- Subjects
PELVIC bones; COMPUTED tomography; FEMORACETABULAR impingement; DESCRIPTIVE statistics; RETROSPECTIVE studies; HEALTH Insurance Portability &; Accountability Act; ROUTINE diagnostic tests; DEEP learning; CASE-control method; ARTIFICIAL neural networks; DATA analysis software; MACHINE learning
- Publication
Journal of Digital Imaging, 2024, Vol 37, Issue 1, p339
- ISSN
0897-1889
- Publication type
Article
- DOI
10.1007/s10278-023-00920-y