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Machine learning model successfully identifies important clinical features for predicting outpatients with rotator cuff tears

  • Shoulder
  • Published:
Knee Surgery, Sports Traumatology, Arthroscopy Aims and scope

Abstract

Purpose

The aim of this study is to develop a machine learning model to identify important clinical features related to rotator cuff tears (RCTs) using explainable artificial intelligence (XAI) for efficiently predicting outpatients with RCTs.

Methods

A retrospective review of a local clinical registry dataset was performed to include patients with shoulder pain and dysfunction who underwent questionnaires and physical examinations between 2019 and 2022. RCTs were diagnosed by shoulder arthroscopy. Six machine-learning algorithms (Stacking, Gradient Boosting Machine, Bagging, Random Forest, Extreme Gradient Boost (XGBoost), and Adaptive Boosting) were developed for the prediction. The performance of the models was assessed by the area under the receiver operating characteristic curve (AUC), Brier scores, and Decision curve. The interpretability of the predicted outcomes was evaluated using Shapley additive explanation (SHAP) values.

Results

A total of 1684 patients who completed questionnaires and clinical tests were included, and 417 patients with RCTs underwent shoulder arthroscopy. In six machining learning algorithms for predicting RCTs, the accuracy, AUC values, and Brier scores were in the range of 0.81–0.86, 0.75–0.92, and 0.15–0.19, respectively. The XGBoost model showed superior performance with accuracy, AUC, and Brier scores of 0.85(95% confidence interval, 0.82–0.87), 0.92 (95% confidence interval,0.90–0.94), and 0.15 (95% confidence interval,0.14–0.16), respectively. The Shapley plot showed the impact of the clinical features on predicting RCTs. The most important variables were Jobe test, Bear hug test, and age for prediction, with mean SHAP values of 1.458, 0.950, and 0.790, respectively.

Conclusion

The machine learning model successfully identified important clinical variables for predicting patients with RCTs. In addition, the best algorithm was also integrated into a digital application to provide predictions in outpatient settings. This tool may assist patients in reducing their pain experience and providing prompt treatments.

Level of Evidence

Level III.

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Data availability

The datasets generated and analysed during the current study are not publicly available due to limitations of ethical approval involving the patient data and anonymity but are available from the corresponding author on reasonable request.

Abbreviations

RCTs:

Rotator cuff tears

POSS:

Postoperative shoulder stiffness

XAI:

Explainable artificial intelligence

XGBoost:

Extreme gradient boost

AUC:

Under the receiver operating curve

SHAP:

Shapley additive explanation

VAS:

Visual analogue scale

ROM:

Range of motion

ERLS:

External rotation lag sign

IRLS:

Internal rotation lag sign

ROC:

Receiver-operating characteristic curve

DCA:

Decision curve analysis

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Acknowledgements

The authors acknowledge Dr. Phei Er Saw for proofreading this article and improving the languag.

Funding

This study was supported by the National Natural Science Foundation of China (no.81972067, 82002342) and Sun Yat-Sen University Clinical Research 5010 Program (no.2020004).

Author information

Author notes

  1. Cheng Li and Yamuhanmode Alike contributed equally to this study.

Authors and Affiliations

  1. Department of Orthopedics, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, 107 Yan Jiang Road West, Guangzhou, 510120, Guangdong, China

    Cheng Li, Yamuhanmode Alike, Jingyi Hou, Yi Long, Zhenze Zheng, Ke Meng & Rui Yang

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  1. Cheng Li
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Contributions

RY conceived and design the study. CL participated in study design, conducted experiment and prepared primary manuscript. YA contributed to research thoughts and played a vital role in providing a critical review of the manuscript. HH and YL contributed to data collection and analysis. ZZ, KM participated in the experiment, data collection.

Corresponding author

Correspondence to Rui Yang.

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Conflict of interest

The authors report no conflicts of interest in the authorship and publication of this article.

Ethical approval

This study was approved by the ethics committee of Sun Yat-sen Memorial Hospital (SYSEC-KY-KS-2021–184).

Informed consent

All patients were provided written consent.

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Appendix

Appendix

See Appendix Table 3.

Table 3 Rankings and SHAP values of the clinical variables for predicting rotator cuff tears
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Li, C., Alike, Y., Hou, J. et al. Machine learning model successfully identifies important clinical features for predicting outpatients with rotator cuff tears. Knee Surg Sports Traumatol Arthrosc 31, 2615–2623 (2023). https://doi.org/10.1007/s00167-022-07298-4

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