ArticleJournal of clinical medicine2023
Approaching Artificial Intelligence in Orthopaedics: Predictive Analytics and Machine Learning to Prognosticate Arthroscopic Rotator Cuff Surgical Outcomes.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in ACL injury prediction and prevention: a systematic review.Journal of orthopaedic surgery and research · 2026Pooled it
- Changes in shoulder range of motion following arthroscopic rotator cuff repair: a systematic review.BMC musculoskeletal disorders · 2025Pooled it
- Free tendon grafts for surgical management of chronic tears of the main body of the Achilles tendon: a systematic review.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2023Pooled it
- Machine learning-based prediction of retear after arthroscopic rotator cuff repair: a multicenter cohort study.JSES international · 2026Article
- Integrating Artificial Intelligence into Orthopedic Practice: Modeling Attitudes and Intentions to Use AI.Healthcare (Basel, Switzerland) · 2026Article
- Artificial intelligence and machine learning: an important new set of tools for clinical shoulder arthroplasty research.JSES international · 2026Review
- Quantifying the Contribution of Bone Morphology to Implant Selection in Shoulder Arthroplasty Using CT-Based Deep Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence models cannot yet replace experts in providing patient education for shoulder and elbow orthopaedic pathologies: a systematic review and meta-analysis.Annals of joint · 2026Article
- Predictors of patient-reported outcomes after primary arthroscopic rotator cuff repair.Journal of shoulder and elbow surgery · 2026Article
- Explainable machine learning to predict prolonged post-operative opioid use in rotator cuff patients.BMC musculoskeletal disorders · 2025Article
- Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives.Military Medical Research · 2025Review
- Red Staining of the LHB and the Rotator Interval on Arthroscopy-A Valid Indicator of Symptomatic Inflammation? A Machine Learning Assisted Investigation.Orthopaedic journal of sports medicine · 2025Article
- Detection of Acromion Types in Shoulder Magnetic Resonance Image Examination with Developed Convolutional Neural Network and Textural-Based Content-Based Image Retrieval System.Journal of clinical medicine · 2025Article
- Context-Aware Dual-Task Deep Network for Concurrent Bone Segmentation and Clinical Assessment to Enhance Shoulder Arthroplasty Preoperative planning.IEEE open journal of engineering in medicine and biology · 2025Article
- Artificial Intelligence (AI): A Potential Game Changer in Regenerative Orthopedics-A Scoping Review.Indian journal of orthopaedics · 2024Article
- Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations.Current reviews in musculoskeletal medicine · 2024Review
- Application of Artificial Intelligence in Shoulder Pathology.Diagnostics (Basel, Switzerland) · 2024Review
- Rotator cuff tears.Nature reviews. Disease primers · 2024Review
- Advancements in the diagnosis and management of rotator cuff tears. The role of artificial intelligence.Journal of orthopaedics · 2024Review
- Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.