Deep learning-assisted diagnosis of femoral trochlear dysplasia based on magnetic resonance imaging measurements

doi:10.12998/wjcc.v11.i7.1477

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World Journal of Clinical Cases

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Retrospective Study

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.

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
Processing time: 95 Days and 8.2 Hours

ARTICLE HIGHLIGHTS

Research background

Femoral trochlear dysplasia (FTD) is an important risk factor for patellar instability, with an incidence of 96% in patients with recurrent patellar instability. Magnetic resonance imaging (MRI) has become the preferred method for evaluating FTD. However, tedious and repeated measurement is essential to using these qualitative and quantitative parameters to diagnose FTD, and it easily produces considerable differences in intragroup consistency and intergroup consistency.

Research motivation

Whether artificial intelligence can be used to assist in the diagnosis of femoral trochlear dysplasia remains unclear.

Research objectives

To propose an artificial intelligence (AI) system to label and detect the key points of knee MRI to assist in diagnosing FTD quickly and accurately.

Research methods

We searched knee MRI cases, including femoral trochlear dysplasia and normal femoral trochlea, all the samples marked by doctors were divided into three sets, including the training set, the validation set and the test set. The performance of AI model to diagnose FTD was improved through continuous training and learning.

Research results

All values (The accuracy, sensitivity, specificity, etc.) were superior to junior doctors and intermediate doctors and similar to senior doctors. In terms of intragroup consistency and intergroup consistency, the AI model was also superior to junior doctors and intermediate doctors. However, diagnostic time was much lower than that of junior doctors and intermediate doctors.

Research conclusions

AI has great potential in the assisted diagnosis of orthopedic diseases. Its greatest significance is to assist young front-line clinicians with less experience to complete the diagnosis of the disease faster and more accurately.

Research perspectives

In the future, we hope to conduct further research based on the existing data and research results, such as how to classify FTD to guide the treatment of different types.