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

Background: 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.

Aim: To use artificial intelligence (AI) to assist diagnosing FTD on MRI images and to evaluate its reliability.

Methods: We searched 464 knee MRI cases between January 2019 and December 2020, including FTD ( = 202) and normal trochlea ( = 252). This paper adopts the heatmap regression method to detect the key points network. For the final evaluation, several metrics (accuracy, sensitivity, specificity, .) were calculated.

Results: 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.

Conclusion: The diagnosis of FTD on knee MRI can be aided by AI and can be achieved with a high level of accuracy.