Retrospective Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Mar 6, 2023; 11(7): 1477-1487
Published online Mar 6, 2023. doi: 10.12998/wjcc.v11.i7.1477
Deep learning-assisted diagnosis of femoral trochlear dysplasia based on magnetic resonance imaging measurements
Sheng-Ming Xu, Dong Dong, Wei Li, Tian Bai, Ming-Zhu Zhu, Gui-Shan Gu
Sheng-Ming Xu, Wei Li, Gui-Shan Gu, Department of Orthopedic Surgery, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
Dong Dong, Department of Radiology, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
Tian Bai, Ming-Zhu Zhu, College of Computer Science and Technology, Jilin University, Changchun 130000, Jilin Province, China
Author contributions: Xu SM, Dong D and Gu GS proposed the research topics; Gu GS, Xu SM, Bai T and Li W designed the research protocols; Xu SM, Dong D, Li W and Zhu MZ performed the data acquisition; Dong D and Zhu MZ participated in experimental data analysis; Xu SM was responsible for the primary manuscript generation; Gu GS and Bai T substantively revised it; all authors have read and agreed to the published version of the manuscript.
Institutional review board statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by Medical Ethics Committee of the first hospital of of Jilin University (registration number 2020-607, approved on 15 Dec 2020).
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: All authors declare no conflicts of interest for this article.
Data sharing statement: The data presented in this study are available on request from the corresponding author.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Gui-Shan Gu, MD, Professor, Department of Orthopedic Surgery, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun 130000, Jilin Province, China. gugs@jlu.edu.cn
Received: November 27, 2022
Peer-review started: November 27, 2022
First decision: January 19, 2023
Revised: January 27, 2023
Accepted: February 13, 2023
Article in press: February 13, 2023
Published online: March 6, 2023
Core Tip

Core Tip: Femoral trochlear dysplasia is an important risk factor for patellar instability. Magnetic resonance imaging has become the preferred method for evaluating femoral trochlear dysplasia. However, manually measuring femoral trochlea parameters on magnetic resonance imaging is tedious, time-consuming, and easily produces great variability. In this work, we propose an assisted diagnosis algorithm framework based on deep learning technology, which can quickly and accurately distinguish whether there is trochlear dysplasia in the femur. All values (The accuracy, sensitivity, specificity, etc.) were superior to junior doctors and intermediate doctors, similar to senior doctors. Our model is beneficial to both orthopedic surgeons and radiologists, especially, the young front-line clinicians with less experience.