The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods
Ebru Atalar1
, Kemal Üreten2,3, Ulunay Kanatlı4, Murat Çiçeklidağ4, İbrahim Kaya5, Abdurrahman Vural6, Yüksel Maraş1
1Department of Internal Medicine, Division of Rheumatology, Ankara City Hospital, Ankara, Türkiye
2Department of Computer Engineering, Faculty of Engineering, Çankaya University, Ankara, Türkiye
3Department of Internal Medicine, Division of Rheumatology, Ufuk University Faculty of Medicine, Ankara, Türkiye
4Department of Orthopedics and Traumatology, Gazi University Faculty of Medicine, Ankara, Türkiye
5Department of Orthopedics and Traumatology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Türkiye
6Department of Orthopedics and Traumatology, Başakşehir Çam and Sakura City Hospital, Istanbul, Türkiye
Keywords: Computer-assisted image processing, deep learning, femoroacetabular impingement, hip.
Abstract
Objectives: The aim of this study was to evaluate diagnostic ability of deep learning models, particularly convolutional neural network models used for image classification, for femoroacetabular impingement (FAI) using hip radiographs.
Materials and methods: Between January 2010 and December 2020, pelvic radiographs of a total of 516 patients (270 males, 246 females; mean age: 39.1±3.8 years; range, 20 to 78 years) with hip pain were retrospectively analyzed. Based on inclusion and exclusion criteria, a total of 888 hip radiographs (308 diagnosed with FAI and 508 considered normal) were evaluated using deep learning methods. Pre-trained VGG-16, ResNet-101, MobileNetV2, and Inceptionv3 models were used for transfer learning.
Results: As assessed by performance measures such as accuracy, sensitivity, specificity, precision, F-1 score, and area under the curve (AUC), the VGG-16 model outperformed other pre-trained networks in diagnosing FAI. With the pre-trained VGG-16 model, the results showed 86.6% accuracy, 82.5% sensitivity, 89.6% specificity, 85.5% precision, 83.9% F1 score, and 0.92 AUC.
Conclusion: In patients with suspected FAI, pelvic radiography is the first imaging method to be applied, and deep learning methods can help in the diagnosis of this syndrome.
Citation: Atalar E, Üreten K, Kanatlı U, Çiçeklidağ M, Kaya İ, Vural A, et al. The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods. Jt Dis Relat Surg 2023;34(2):298-304. doi: 10.52312/jdrs.2023.996.
The study protocol was approved by the Gazi University Faculty of Medicine Ethics Committee (date: 13.12.2022, no: 2022-1385). The study was conducted in accordance with the principles of the Declaration of Helsinki.
Idea/concept: U.K., E.A.; Design: E.A., Y.M.; Control/supervision: A.V.; Data collection and/or processing: I.K., M.Ç., A.V.; Analysis and/or interpretation: K.U.; Literature review: Y.M., E.A.; Writing the article: E.A.; Critical review: K.U.; References and funding: M.Ç., İ.K.; Materials: I.K., M.Ç., A.V.
The authors declared no conflicts of interest with respect to the authorship and/or publication of this article.
The authors received no financial support for the research and/or authorship of this article.
The data that support the findings of this study are available from the corresponding author upon reasonable request.