Purpose: To develop a deep learning model for the detection of Segond fractures on anteroposterior (AP) knee radiographs and to compare model performance to that of trained human experts., Methods: AP knee radiographs were retrieved from the Hospital for Special Surgery ACL Registry, which enrolled patients between 2009 and 2013. All images corresponded to patients who underwent anterior cruciate ligament reconstruction by 1 of 23 surgeons included in the registry data. Images were categorized into 1 of 2 classes based on radiographic evidence of a Segond fracture and manually annotated. Seventy percent of the images were used to populate the training set, while 20% and 10% were reserved for the validation and test sets, respectively. Images from the test set were used to compare model performance to that of expert human observers, including an orthopaedic surgery sports medicine fellow and a fellowship-trained orthopaedic sports medicine surgeon with over 10 years of experience., Results: A total of 324 AP knee radiographs were retrieved, of which 34 (10.4%) images demonstrated evidence of a Segond fracture. The overall mean average precision (mAP) was 0.985, and this was maintained on the Segond fracture class (mAP = 0.978, precision = 0.844, recall = 1). The model demonstrated 100% accuracy with perfect sensitivity and specificity when applied to the independent testing set and the ability to meet or exceed human sensitivity and specificity in all cases. Compared to an orthopaedic surgery sports medicine fellow, the model required 0.3% of the total time needed to evaluate and classify images in the independent test set., Conclusions: A deep learning model was developed and internally validated for Segond fracture detection on AP radiographs and demonstrated perfect accuracy, sensitivity, and specificity on a small test set of radiographs with and without Segond fractures. The model demonstrated superior performance compared with expert human observers., Clinical Relevance: Deep learning can be used for automated Segond fracture identification on radiographs, leading to improved diagnosis of easily missed concomitant injuries, including lateral meniscus tears. Automated identification of Segond fractures can also enable large-scale studies on the incidence and clinical significance of these fractures, which may lead to improved management and outcomes for patients with knee injuries., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: J.F.O. is a consultant or advisor for Kaliber.ai. B.U.N. is a consultant or advisor for 10.13039/100008894Stryker, has equity or stocks with Remote Health, and is a board member of Figur8 and BICMD. H.G.G. is a consultant or advisor for Motion Orthopaedics and Professional Imaging and has equity or stocks with Tate Greditzer, LLC. C.L.C. is a paid consultant for 10.13039/100007307Arthrex, has received research support from 10.13039/100007062Major League Baseball, and has received publishing royalties and financial or material support from Springer. B.T.K. is a consultant or advisor for 10.13039/100007307Arthrex and BICMD. A.D.P. is a consultant or advisor for DePuy Synthes, Exactech, Smith & Nephew, and 10.13039/100006338Zimmer and has equity or stocks with ACLIP, myHealthTrack, PerfectFict, and Vent Creativity. A.S.R. is a consultant or advisor for 10.13039/100014927Anika Therapeutics, 10.13039/100007307Arthrex, Bodycad USA, Marrow Cellution, Newclip Technics, Smith & Nephew, 10.13039/100008894Stryker, and Xiros; has received speaking and lecture fees from Arthrex; and has equity or stocks with Conformis, Enhatch, and Ranfac. R.J.W. has equity or stocks with BICMD, CyMedica Orthopedics, Gramercy Extremity Orthopedics, Remote Health Ventures, Tonal, and Overture and is a board member of Pristine Surgical, Rehab Boost, and Tonal. All other authors (A.P., K.N.K.) declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)