Identification and diagnosis of meniscus tear by magnetic resonance imaging using a deep learning model

Journal of Orthopaedic TranslationVolume 34, May 2022, Pages 91-101Journal of Orthopaedic TranslationAbstractObjective

Meniscus tear is a common problem in sports trauma, and its imaging diagnosis mainly relies on MRI. To improve the diagnostic accuracy and efficiency, a deep learning model was employed in this study and the identification efficiency was evaluated.

Methods

Standard knee MRI images from 924 individual patients were used to complete the training, validation and testing processes. Mask regional convolutional neural network (R–CNN) was used to build the deep learning network structure, and ResNet50 was adopted to develop the backbone network. The deep learning model was trained and validated with a dataset containing 504 and 220 patients, respectively. Internal testing was performed based on a dataset of 200 patients, and 180 patients from 8 hospitals were regarded as an external dataset for model validation. Additionally, 40 patients who were diagnosed by the arthroscopic surgery were enrolled as the final test dataset.

Results

After training and validation, the deep learning model effectively recognized healthy and injured menisci. Average precision for the three types of menisci (healthy, torn and degenerated menisci) ranged from 68% to 80%. Diagnostic accuracy for healthy, torn and degenerated menisci was 87.50%, 86.96%, and 84.78%, respectively. Validation results from external dataset demonstrated that the accuracy of diagnosing torn and intact meniscus tear through 3.0T MRI images was higher than 80%, while the accuracy verified by arthroscopic surgery was 87.50%.

Conclusion

Mask R–CNN effectively identified and diagnosed meniscal injuries, especially for tears that occurred in different parts of the meniscus. The recognition ability was admirable, and the diagnostic accuracy could be further improved with increased training sample size. Therefore, this deep learning model showed great potential in diagnosing meniscus injuries.

Translational potential of this article

Deep learning model exerted unique effect in terms of reducing doctors’ workload and improving diagnostic accuracy. Injured and healthy menisci could be more accurately identified and classified based on training and learning datasets. This model could also distinguish torn from degenerated menisci, making it an effective tool for MRI-assisted diagnosis of meniscus injuries in clinical practice.

Keywords

Meniscus injury

Deep learning model

MRI

Regional Convolutional Neural Network

AI

AbbreviationsMRI

magnetic resonance imaging

FS FSE PDWI

fat-suppressed fast spin-echo proton density-weighted image

R-CNN

regional convolutional neural network

AI

artificial intelligence

PDW

proton density-weighted

AH_tear

anterior horn tear

PH_tear

posterior horn tear

AD

anterior horn degeneration

PD

posterior horn degeneration

MBD

meniscus body degeneration

AH_intact

anterior horn health

PH_intact

posterior horn health

RPN

region proposal network

IoU

intersection over union

© 2022 The Authors. Published by Elsevier (Singapore) Pte Ltd on behalf of Chinese Speaking Orthopaedic Society.

留言 (0)

沒有登入
gif