Approaching Artificial Intelligence in Orthopaedics: Predictive Analytics and Machine Learning to Prognosticate Arthroscopic Rotator Cuff Surgical Outcomes

Machine learning (ML) has not yet been used to identify factors predictive for post-operative functional outcomes following arthroscopic rotator cuff repair (ARCR). We propose a novel algorithm to predict ARCR outcomes using machine learning. This is a retrospective cohort study from a prospectively...

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Bibliographic Details
Published in:Journal of clinical medicine 2023-03, Vol.12 (6), p.2369
Main Authors: Potty, Anish G, Potty, Ajish S R, Maffulli, Nicola, Blumenschein, Lucas A, Ganta, Deepak, Mistovich, R Justin, Fuentes, Mario, Denard, Patrick J, Sethi, Paul M, Shah, Anup A, Gupta, Ashim
Format: Article
Language:English
Subjects:
Algorithms
Arthroscopy
Artificial intelligence
Care and treatment
Clinical medicine
Clinical outcomes
Datasets
Diabetes
Endoscopic surgery
Gender
Joints
Machine learning
Methods
Orthopedics
Outcome and process assessment (Health Care)
Patient outcomes
Patients
Predictive analytics
Prognosis
Recovery (Medical)
Regression analysis
Rotator cuff
Rotator cuff repair
Shoulder injuries
Statistical analysis
Surgeons
Surgery
Surgical outcomes
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Summary:Machine learning (ML) has not yet been used to identify factors predictive for post-operative functional outcomes following arthroscopic rotator cuff repair (ARCR). We propose a novel algorithm to predict ARCR outcomes using machine learning. This is a retrospective cohort study from a prospectively collected database. Data were collected from the Surgical Outcome System Global Registry (Arthrex, Naples, FL, USA). Pre-operative and 3-month, 6-month, and 12-month post-operative American Shoulder and Elbow Surgeons (ASES) scores were collected and used to develop a ML model. Pre-operative factors including demography, comorbidities, cuff tear, tissue quality, and fixation implants were fed to the ML model. The algorithm then produced an expected post-operative ASES score for each patient. The ML-produced scores were compared to actual scores using standard test-train machine learning principles. Overall, 631 patients who underwent shoulder arthroscopy from January 2011 to March 2020 met inclusion criteria for final analysis. A substantial number of the test dataset predictions using the XGBoost algorithm were within the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) thresholds: 67% of the 12-month post-operative predictions were within MCID, while 84% were within SCB. Pre-operative ASES score, pre-operative pain score, body mass index (BMI), age, and tendon quality were the most important features in predicting patient recovery as identified using Shapley additive explanations (SHAP). In conclusion, the proposed novel machine learning algorithm can use pre-operative factors to predict post-operative ASES scores accurately. This can further supplement pre-operative counselling, planning, and resource allocation. Level of Evidence: III.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm12062369