YOLOv8 algorithm-aided detection of patellar instability or dislocation on knee joint MRI images

Abstract

Background

Patellar instability (PI) or patellar dislocation (PD) is challenging to diagnose accurately based on medical history and clinical manifestations alone. While X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) are commonly employed for detecting PI or PD, computer vision has not yet been widely utilized for this purpose.

Purpose

To explore the feasibility of computer vision, specifically the You Only Look Once (YOLO) algorithm, in identifying patellar instability or dislocation.

Material and Methods

A total of 550 patients (190 diagnosed with patellar instability or dislocation) were divided into a training set (n = 360), validation set (n = 90), and external test set (n = 100). Four indicators were measured on transverse knee MRI scans to determine the presence of patellar instability, and 450 images were labeled using Labelme software. YOLO version 8 (YOLOv8) was refined using these labeled images and validated on 100 unlabeled images. The diagnostic accuracy of YOLOv8 was compared with that of a junior radiologist.

Results

The sensitivity, specificity, and accuracy of the refined YOLO model and the junior radiologist were 62%, 97%, and 83%, and 62%, 82%, and 74%, respectively. Although the YOLO model demonstrated slightly higher accuracy, the difference did not reach statistical significance (P = 0.093). The YOLO model required approximately 14.01 ± 10.34 ms to interpret each image, significantly shorter than the 9.55 ± 2.39 s required by the radiologist (P < 0.001).

Conclusion

The refined YOLOv8 model is not inferior to junior radiologists in identifying patellar instability or dislocation and offers a significantly faster interpretation time.