Innovative diagnostic framework for shoulder instability: a narrative review on machine learning-enhanced scapular dyskinesis assessment in sports injuries.

A common shoulder problem that significantly detracts from patients' quality of life is shoulder instability (SI). Abnormal scapular positioning and movement are closely associated with rotator cuff injuries and SI, as shown by several studies. The aetiology of scapular dyskinesia (SD) adversely affects shoulder stability and function, including postural abnormalities, musculoskeletal problems, and neurological conditions. Presently, there is a paucity of studies on scapular kinetic alterations in SI rehabilitation. This paper rigorously examines the correlation between SI and scapular kinetic irregularities, as well as the functional alterations of periscapular muscle groups, offering a thorough theoretical foundation and practical guidance for clinicians to enhance their understanding of the disease mechanism and develop a more holistic and effective treatment for patients with SI. We examined the particular manifestations of scapular kinetic disorders in SI patients, evaluated current clinical assessment tools, and explored novel strategies (machine learning, kinetic chain, and scapular biomechanics) for prospective clinical applications. Our objective is to furnish a thorough theoretical foundation and practical guidance for rehabilitating patients with SI, thereby enhancing clinicians' comprehension of the disease mechanism and enabling the development of more effective rehabilitation programs.