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Development of a Rabbit Chronic-Like Rotator Cuff Injury Model for Study of Fibrosis and Muscular Fatty Degeneration
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Re-tear after arthroscopic rotator cuff tear surgery: risk analysis using machine learning.

Issei Shinohara1, Yutaka Mifune1, Atsuyuki Inui1

  • 1Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan.

Journal of Shoulder and Elbow Surgery
|August 25, 2023
PubMed
Summary

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Machine learning accurately predicts rotator cuff retear after arthroscopic rotator cuff repair (ARCR). Key factors include patient age, stump classification, and tear size, aiding in risk assessment for improved patient outcomes.

Area of Science:

  • Orthopedic Surgery
  • Artificial Intelligence in Medicine
  • Biomedical Imaging Analysis

Background:

  • Rotator cuff retear after arthroscopic rotator cuff repair (ARCR) remains a significant clinical challenge.
  • Identified risk factors include patient demographics and tear characteristics, with stump classification emerging as a key indicator of rotator cuff fragility.
  • Previous studies have highlighted stump type 3 as having a high retear rate, yet comprehensive risk prediction models are lacking.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) models for predicting the risk of postoperative retear following ARCR.
  • To identify the most significant clinical and imaging features contributing to retear risk.
  • To leverage artificial intelligence for more accurate and flexible predictive modeling in orthopedic surgery outcomes.
Keywords:
Arthroscopic rotator cuff repairLightGBMSHAPartificial intelligencefeature importancemachine learningretearstump classification

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Main Methods:

  • A retrospective case-control study involving 353 patients who underwent ARCR for complete rotator cuff tears using the suture-bridge technique.
  • Exclusion of patients with initial retears or traumatic tears; retears defined by Sugaya classification types IV and V via MRI.
  • Training and testing of five ML models (logistic regression, random forest, AdaBoost, CatBoost, LightGBM) using parameters such as age, gender, stump classification, tear size, Goutallier classification, diabetes, and hyperlipidemia.

Main Results:

  • The LightGBM model achieved the highest predictive accuracy, with an area under the receiver operating characteristic curve of 0.87.
  • Other models demonstrated strong performance: CatBoost (0.83), Random Forest (0.82), Logistic Regression (0.78), and AdaBoost (0.78).
  • Key predictors identified for retear risk were patient age, stump classification, and rotator cuff tear size.

Conclusions:

  • Machine learning models can accurately predict postoperative rotator cuff retears after ARCR.
  • Patient age and imaging findings, particularly stump classification, are the most critical factors influencing retear risk.
  • This AI-driven model offers a promising tool for predicting postoperative retears based on readily available clinical and imaging data.