Research graph
References from Machine learning–based prediction of retear after arthroscopic rotator cuff repair: a multicenter cohort study. Local targets link to admitted publications; unresolved targets remain external evidence.
Using machine learning to predict nonachievement of clinically significant outcomes after Rotator Cuff Repair
10.1177/23259671231206180 · 2023 · External reference
Developing a machine learning algorithm to predict probability of retear and functional outcomes in patients undergoing rotator cuff repair surgery: protocol for a retrospective, multicentre study
10.1136/bmjopen-2022-063673 · 2023 · External reference
Machine learning for the orthopaedic surgeon: uses and limitations
10.2106/jbjs.21.01305 · 2022 · External reference
Deep learning and multimodal artificial intelligence in orthopaedic surgery
2024 · External reference
The role of artificial intelligence and machine learning in predicting orthopaedic outcomes
10.1302/0301-620x.101b12.bjj-2019-0850.r1 · 2019 · External reference
Unresolved reference
2006 · External reference
Arthroscopic rotator Cuff Repair: how to avoid Retear
10.1016/j.arthro.2018.11.002 · 2019 · External reference
SMOTE: synthetic minority over-sampling technique
10.1613/jair.953 · 2002 · External reference
An overview of artificial intelligence and machine learning in shoulder surgery
10.5397/cise.2025.00185 · 2025 · External reference
An evaluation of the Constant-Murley shoulder assessment
10.1302/0301-620x.79b4.0790695c · 1997 · External reference
A clinical method of functional assessment of the shoulder
10.1097/00003086-198701000-00023 · 1987 · External reference
Evaluation metrics and evaluation
2018 · External reference
Predicting the reparability of Rotator Cuff tears: machine learning and comparison with previous scoring systems
10.1177/03635465241287527 · 2024 · External reference
Applications of artificial intelligence in Orthopaedic surgery: a systematic review and meta-analysis
10.1016/j.engappai.2024.108326 · 2024 · External reference
Rotator cuff degeneration and healing after rotator cuff repair
10.5397/cise.2023.00430 · 2023 · External reference
Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review
2023 · External reference
Automated detection and classification of the rotator cuff tear on plain shoulder radiograph using deep learning
10.1016/j.jse.2023.12.009 · 2024 · External reference
Will ChatGPT/GPT-4 be a lighthouse to guide spinal surgeons?
10.1007/s10439-023-03206-0 · 2023 · External reference
Utilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty
2022 · External reference
Machine learning in knee arthroplasty: specific data are key—a systematic review
10.1007/s00167-021-06848-6 · 2022 · External reference
Prediction of complications and surgery duration in primary total hip arthroplasty using machine learning: the necessity of modified algorithms and specific data
2022 · External reference
Machine learning did not outperform conventional competing risk modeling to predict revision arthroplasty
2024 · External reference
Significant radiologic factors related to clinical outcomes after arthroscopic rotator cuff retear repair
10.5397/cise.2022.01046 · 2022 · External reference
Shoulder strength in asymptomatic individuals with intact compared with torn rotator cuffs
10.2106/jbjs.h.00219 · 2009 · External reference
Prediction of the irreparability of rotator cuff tears
10.1016/j.arthro.2018.02.033 · 2018 · External reference
Factors associated with patient satisfaction after arthroscopic rotator cuff repair: do they differ by age?
10.5397/cise.2024.00283 · 2024 · External reference
The destiny of the Subscapularis Tendon after Arthroscopic Supraspinatus repair
10.4055/cios23389 · 2024 · External reference
Midterm functional outcomes after Retear of an arthroscopic Rotator Cuff Repair: a propensity score-matched comparative Study
10.1177/03635465241305742 · 2025 · External reference
Can preoperative magnetic resonance imaging predict the reparability of massive rotator Cuff tears?
10.1177/0363546517694160 · 2017 · External reference
The development of a quantitative scoring system to predict whether a large-to-massive rotator cuff tear can be arthroscopically repaired
10.1302/0301-620x.98b12.bjj-2016-0316 · 2016 · External reference
The rotator Cuff healing index: a new scoring System to predict rotator Cuff healing after surgical repair
10.1177/0363546518810763 · 2019 · External reference
Comparing surgical repair with conservative treatment for degenerative rotator cuff tears: a randomized controlled trial
10.1016/j.jse.2015.05.040 · 2015 · External reference
Machine learning and artificial intelligence are valuable tools, yet dependent on the data input
2024 · External reference
A novel image-based machine learning model with superior accuracy and predictability for knee arthroplasty loosening detection and clinical decision making
2022 · External reference
Deep learning
10.1038/nature14539 · 2015 · External reference
Artificial intelligence in shoulder and elbow surgery: overview of current and future applications
10.1016/j.jse.2024.01.033 · 2024 · External reference
Cost-effectiveness of a preoperative 12-week smoking cessation program prior to arthroscopic rotator cuff repair
2025 · External reference
Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance
10.1016/j.neunet.2007.12.031 · 2008 · External reference
Application of ChatGPT for orthopedic surgeries and patient care
10.4055/cios23181 · 2024 · External reference
Increased patient resilience scores are related to positive postoperative outcomes in rotator cuff repairs
10.1016/j.jse.2023.09.016 · 2024 · External reference
Approaching artificial intelligence in orthopaedics: predictive analytics and machine learning to prognosticate arthroscopic Rotator Cuff surgical outcomes
10.3390/jcm12062369 · 2023 · External reference
Machine learning in medicine
10.1056/nejmra1814259 · 2019 · External reference
Learning representations by back-propagating errors
10.1038/323533a0 · 1986 · External reference
Re-tear after arthroscopic rotator cuff tear surgery: risk analysis using machine learning
10.1016/j.jse.2023.07.017 · 2024 · External reference
Predicting prolonged wound drainage after Hemiarthroplasty for hip fractures: a stacked machine learning Study
10.4055/cios22181 · 2023 · External reference
Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints
10.1186/1471-2288-14-137 · 2014 · External reference
Prognostic factors affecting structural integrity after arthroscopic rotator cuff repair: a clinical and histological study
2023 · External reference
Prevalence and risk factors of a rotator cuff tear in the general population
10.1016/j.jse.2009.04.006 · 2010 · External reference
