A multimodal deep learning radiomics model for predicting degenerative meniscus tear after arthroscopy

Yao He1,2,3,4, Jiaying Wei2,3,4, Yinsong Sun2,3,4

  • 1Department of Orthopedics, Banan Hospital of Chongqing Medical University, Chongqing, China.

Plos One
|August 13, 2025
PubMed
Abstract

Insights

A new multimodal deep learning radiology (MDLR) model integrates MRI data and clinical factors to predict outcomes after arthroscopic partial meniscectomy for degenerative meniscus tears. This tool improves prognostic accuracy, aiding clinical decision-making for patient care.

Area of Science:

  • Orthopedics
  • Radiology
  • Artificial Intelligence

Background:

  • Degenerative meniscus tears often coexist with osteoarthritis, complicating prognosis after arthroscopic partial meniscectomy (APM).
  • Predicting patient outcomes following APM is challenging due to these comorbidities.

Purpose of the Study:

  • To develop and validate a multimodal deep learning radiology (MDLR) model.
  • To integrate deep learning radiology (DLR) scores from MRI with clinical variables for improved prognostic accuracy.

Main Methods:

  • Retrospective enrollment of 452 patients undergoing APM for degenerative meniscus tears (February 2020 - February 2022).
  • Extraction of DLR features from knee MRI scans.
  • Development of an MDLR model incorporating DLR signatures and clinical variables for prognostic risk stratification.

Main Results:

  • A standalone DLR model showed limited predictive performance (ROC curves ranging from 0.720-0.780).
  • Multivariate analysis identified key prognostic indicators: gender, height, weight, pain duration, ESR, and VAS.
  • The integrated MDLR model, particularly using Light Gradient Boosting Machine, significantly improved performance (ROC curves up to 0.951) and stratified patients into four prognostic levels.

Conclusions:

  • The developed MDLR tool effectively stratifies prognosis for APM in degenerative meniscus tear patients.
  • Pain exacerbation time is a crucial prognostic factor identified by the MDLR model.
  • MDLR demonstrates significant potential to assist physicians in therapeutic decision-making and surveillance strategies.

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