Search

Purpose: Various surgical treatments have been described for the treatment of reverse Hill–Sachs lesions (rHSls) sized between 20% and 50% in the case of posterior shoulder dislocation. The aim of this systematic review is to report the clinical and radiological outcomes of subscapularis or lesser tuberosity transfer (McLaughlin and modified procedures) compared to bone or osteochondral autograft or allograft. Methods: A systematic review was performed on five medical databases up to December 2022. The inclusion criteria were clinical studies of all levels of evidence describing clinical or radiological outcomes of either procedure. The assessment of the quality of evidence was performed with the Modified Coleman Score. Results: A total of 14 studies (five prospective and nine retrospective) were included. A total of 153 patients (155 shoulders, 78.4% male) with a mean age of 37.2 (22–79) years were reviewed at an average follow‐up of 53.1 (7.1–294) months. No relevant difference was found for the clinical scores, range of motion, complications and redislocation rate between the two treatments. Radiological osteoarthritis (OA) was reported in 11% (10/87) in the McLaughlin group and in 21% (16/73) in the humeral reconstruction group. Conclusions: McLaughlin and anatomic humeral reconstruction lead to similar satisfactory clinical results and a low redislocation rate in the treatment of rHSls. Anatomic humeral reconstruction seems associated with an increased risk of OA progression. Level of Evidence: Level IV. [ABSTRACT FROM AUTHOR]

Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality., Level of Evidence: Level V., Competing Interests: The authors declare no conflict of interest., (© 2024 The Author(s). Journal of Experimental Orthopaedics published by John Wiley & Sons Ltd on behalf of European Society of Sports Traumatology, Knee Surgery and Arthroscopy.)

Competing Interests: The authors declare no conflict of interest.

Recent advances in artificial intelligence (AI) present a broad range of possibilities in medical research. However, orthopaedic researchers aiming to participate in research projects implementing AI-based techniques require a sound understanding of the technical fundamentals of this rapidly developing field. Initial sections of this technical primer provide an overview of the general and the more detailed taxonomy of AI methods. Researchers are presented with the technical basics of the most frequently performed machine learning (ML) tasks, such as classification, regression, clustering and dimensionality reduction. Additionally, the spectrum of supervision in ML including the domains of supervised, unsupervised, semisupervised and self-supervised learning will be explored. Recent advances in neural networks (NNs) and deep learning (DL) architectures have rendered them essential tools for the analysis of complex medical data, which warrants a rudimentary technical introduction to orthopaedic researchers. Furthermore, the capability of natural language processing (NLP) to interpret patterns in human language is discussed and may offer several potential applications in medical text classification, patient sentiment analysis and clinical decision support. The technical discussion concludes with the transformative potential of generative AI and large language models (LLMs) on AI research. Consequently, this second article of the series aims to equip orthopaedic researchers with the fundamental technical knowledge required to engage in interdisciplinary collaboration in AI-driven orthopaedic research., Level of Evidence: Level IV., Competing Interests: Michael T. Hirschmann is a consultant for Medacta, Symbios and Depuy Synthes. Kristian Samuelsson is a member on the board of directors for Getinge AB (publ). Robert Feldt is Chief Technology Officer and founder in Accelerandium AB, a software consultancy company., (© 2024 The Author(s). Journal of Experimental Orthopaedics published by John Wiley & Sons Ltd on behalf of European Society of Sports Traumatology, Knee Surgery and Arthroscopy.)