Accuracy and Reliability of Multimodal Imaging in Diagnosing Knee Sports Injuries

TL;DR

This paper shows that combining MRI, CT, and ultrasound with deep learning improves the accuracy and speed of diagnosing knee sports injuries.

Contribution

The study introduces an ensemble deep learning model using multimodal imaging to enhance diagnostic accuracy and consistency for knee injuries.

Findings

The DL model achieved over 90% accuracy for anterior cruciate ligament tear diagnosis.

Cartilage injury diagnosis reached 95.80% accuracy with the model.

The model reduced interpretation time and showed less than 2% error compared to doctors.

Abstract

Due to differences in subjective experience and professional level among doctors, as well as inconsistent diagnostic criteria, there are issues with the accuracy and reliability of single imaging diagnosis results for knee joint injuries.

To address these issues, magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound (US) are adopted in this article for ensemble learning, and deep learning (DL) is combined for automatic analysis.

By steps such as image enhancement, noise elimination, and tissue segmentation, the quality of image data is improved, and then convolutional neural networks (CNN) are used to automatically identify and classify injury types. The experimental results show that the DL model exhibits high sensitivity and specificity in the diagnosis of different types of injuries, such as anterior cruciate ligament tear, meniscus injury, cartilage injury, and fracture.

The diagnostic accuracy of anterior cruciate ligament tear exceeds 90%, and the highest diagnostic accuracy of cartilage injury reaches 95.80%. In addition, compared with traditional manual image interpretation, the DL model has significant advantages in time efficiency, with a significant reduction in average interpretation time per case. The diagnostic consistency experiment shows that the DL model has high consistency with doctors’ diagnosis results, with an overall error rate of less than 2%.

The model has high accuracy and strong generalization ability when dealing with different types of joint injuries. These data indicate that combining multiple imaging technologies and the DL algorithm can effectively improve the accuracy and efficiency of diagnosing sports injuries of knee joints.

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