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ORIGINAL RESEARCH article
Front. Surg.
Sec. Orthopedic Surgery
Volume 11 - 2024 |
doi: 10.3389/fsurg.2024.1329085
Predicting Acetabular Version in Native Hip Joints through Plain X-ray Radiographs: A Comparative Analysis of Convolutional Neural Network Model and the Current Gold Standard, with Insights and Implications for Hip Arthroplasty
Provisionally accepted- 1 Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
- 2 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
- 3 Orthopedic Surgery Research Center, Sina University Hospital, Tehran University of Medical Sciences, Tehran, Alborz, Iran
- 4 School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Alborz, Iran
- 5 Joint Reconstruction Research Center, Tehran University of Medical Sciences, Tehran, Iran
This study presents the development and validation of a Deep Learning Convolutional Neural Network (CNN) model for estimating acetabular version (AV) from native hip plain radiographs. Utilizing a dataset comprising 300 participants with unrelated pelvic complaints, the CNN model was trained and evaluated against CT-Scans, considered the gold standard, using a 5-fold cross-validation. Notably, the CNN model exhibited a robust performance, demonstrating a strong Pearson correlation with CT-Scans (right hip: r=0.70, p<0.001; left hip: r=0.71, p<0.001) and achieving a mean absolute error of 2.95°. Remarkably, over 83% of predictions yielded errors ≤ 5°, highlighting the model's high precision in AV estimation. The model holds promise in preoperative planning for hip arthroplasty, potentially reducing complications like recurrent dislocation and component wear. Future directions include further refinement of the CNN model, with ongoing investigations aimed at enhancing preoperative planning potential and ensuring comprehensive assessment across diverse patient populations, particularly in diseased cases. Additionally, future research could explore the model's potential value in scenarios necessitating minimized ionizing radiation exposure, such as post-operative evaluations.
Keywords: Hip Joint, Acetabulum, Acetabular version, artificial intelligence, machine learning, deep learning, Convolutional Neural Network
Received: 27 Oct 2023; Accepted: 03 Sep 2024.
Copyright: © 2024 Jodeiri, Seyedarabi, Hashemi, Shahbazi, Mortazavi and Shafiei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Seyyed Hossein Shafiei, Orthopedic Surgery Research Center, Sina University Hospital, Tehran University of Medical Sciences, Tehran, Alborz, Iran
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