Ata Jodeiri ^1,2 Hadi Seyedarabi ² Seyed Mohammad Mahdi Hashemi ³ Fatemeh Shahbazi ⁴ Seyed Mohammad Javad Mortazavi ⁵ Seyyed Hossein Shafiei ^3*

• 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.