Artificial Intelligence Can Define and Predict the "Optimal Observed Outcome" After Anterior Shoulder Instability Surgery: An Analysis of 200 Patients With 11-Year Mean Follow-Up

Application of Artificial Intelligence in Shoulder Pathology

Artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopedic-specific problems. One particular area of investigation focuses on shoulder pathology, which is a range of disorders or abnormalities of the shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate current AI applications in shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI technology are also discussed.

Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications

Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available-ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting.

Mid- to Long-Term Outcomes of Arthroscopic Shoulder Stabilization in Athletes: A Systematic Review

There exists a considerable amount of evidence regarding short-term outcomes of shoulder arthroscopy in athletes; however, mid- to long-term data are limited. Therefore, the purpose of this review is to evaluate studies assessing mid- to long-term outcomes and rates of return to sport in athletes undergoing primary shoulder arthroscopy. A search for the systematic review was performed in PubMed, Scopus, and Embase on 14 March 2023. Study parameters, as well as their respective outcomes, were described in detail and compiled into diagrams. Five studies were included, which contained data on a total of 307 shoulders in patients with mean ages ranging from 20.3 to 26.9 years and mean follow-up times ranging from 6.3 to 14 years. The arthroscopic Bankart repair was the primary surgical intervention performed in all five studies. The overall rate of return to sport was 84% (range, 70-100%) across the studies. The rate of return to sport at pre-injury level was 65.2% (range, 40-82.6%) across four studies. The overall rate of recurrent instability was 17.3%, with redislocation specifically occurring in 13.7% of patients across all studies. The overall rate of revision surgery was 11.1%. Athletes who underwent primary shoulder arthroscopy demonstrated favorable outcomes and a high rate of RTS at a minimum follow-up of 5 years. However, rates of recurrent instability, redislocation, and revision surgery occurred at less than favorable numbers, which emphasizes the importance of proper patient selection when considering candidates for arthroscopic versus open repairs.