Published online Mar 6, 2023. doi: 10.12998/wjcc.v11.i7.1477
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
Femoral trochlear dysplasia (FTD) is an important risk factor for patellar instability. Dejour classification is widely used at present and relies on standard lateral X-rays, which are not common in clinical work. Therefore, magnetic resonance imaging (MRI) has become the first choice for the diagnosis of FTD. However, manually measuring is tedious, time-consuming, and easily produces great variability.
To use artificial intelligence (AI) to assist diagnosing FTD on MRI images and to evaluate its reliability.
We searched 464 knee MRI cases between January 2019 and December 2020, including FTD (n = 202) and normal trochlea (n = 252). This paper adopts the heatmap regression method to detect the key points network. For the final evaluation, several metrics (accuracy, sensitivity, specificity, etc.) were calculated.
The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the AI model ranged from 0.74-0.96. All values were superior to junior doctors and intermediate doctors, similar to senior doctors. However, diagnostic time was much lower than that of junior doctors and intermediate doctors.
The diagnosis of FTD on knee MRI can be aided by AI and can be achieved with a high level of accuracy.
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.