A multi-branch network to detect post-operative complications following hip arthroplasty on X-ray images

Sijia Guo; Jiping Zhang; Huiwu Li; Jingwei Zhang; Cheng-Kung Cheng

doi:10.3389/fbioe.2023.1239637

A multi-branch network to detect post-operative complications following hip arthroplasty on X-ray images

Front Bioeng Biotechnol. 2023 Sep 28:11:1239637. doi: 10.3389/fbioe.2023.1239637. eCollection 2023.

Authors

Sijia Guo^{1

2}, Jiping Zhang^{1

2}, Huiwu Li³, Jingwei Zhang³, Cheng-Kung Cheng^{1

2}

Affiliations

¹ School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
² Engineering Research Center for Digital Medicine of the Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.
³ Department of Orthopaedics, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

Background: Postoperative complications following total hip arthroplasty (THA) often require revision surgery. X-rays are usually used to detect such complications, but manually identifying the location of the problem and making an accurate assessment can be subjective and time-consuming. Therefore, in this study, we propose a multi-branch network to automatically detect postoperative complications on X-ray images. Methods: We developed a multi-branch network using ResNet as the backbone and two additional branches with a global feature stream and a channel feature stream for extracting features of interest. Additionally, inspired by our domain knowledge, we designed a multi-coefficient class-specific residual attention block to learn the correlations between different complications to improve the performance of the system. Results: Our proposed method achieved state-of-the-art (SOTA) performance in detecting multiple complications, with mean average precision (mAP) and F1 scores of 0.346 and 0.429, respectively. The network also showed excellent performance at identifying aseptic loosening, with recall and precision rates of 0.929 and 0.897, respectively. Ablation experiments were conducted on detecting multiple complications and single complications, as well as internal and external datasets, demonstrating the effectiveness of our proposed modules. Conclusion: Our deep learning method provides an accurate end-to-end solution for detecting postoperative complications following THA.

Keywords: X-ray; deep learning; domain knowledge; multi-branch network; post-operative complications; total hip arthroplasty.

Grants and funding

This work supported by the Fundamental Research Funds for the Central Universities (grant number AF0820060), Shanghai “Rising Stars of Medical Talent” Youth Development Program, Youth Medical Talents-Specialist Program (grant number SHHWRS 2023-62), Outstanding Research-oriented Doctor Cultivation Program at the Ninth People’s Hospital affiliated with the School of Medicine, Shanghai Jiao Tong University, National Natural Science Foundation of China (grant number 31900941).