Longo, Umile Giuseppe

Background The management of osteochondral lesions of the talus (OLT) is challenging, with 2 main options being conservative and surgical management. Orthobiologic therapies come to bridge the gap between these 2 options; however, inconsistencies in indication, composition, and heterogeneous evidence limit clinical decision-making. To address these challenges, 3 International Congress Meetings on Cartilage Repair of the Ankle were convened in Pittsburgh, USA (2017), Dublin, Ireland (2019), and New York, USA (2025) to establish agreement on the role of conservative management and injectable orthobiologic strategies in the treatment of OLT. Methods Seventy-five multidisciplinary experts from over 25 countries and 1 territory participated in a structured Delphi-based consensus process. Blinded surveys, structured questionnaires, and in-person discussions were used to develop and refine statements, supported by literature review and graded evidence (A1–E). Consensus strength was defined as consensus (51%-74%), strong consensus (75%-99%), or unanimous (100%) Results In total, 29 consensus statements were achieved across the 3 meetings. Strong agreement was reached regarding indications and expected outcomes of conservative management, including the role of immobilization and appropriate timing for surgical intervention. Orthobiologic injections, including platelet-rich plasma, concentrated bone marrow aspirate, and hyaluronic acid, were considered appropriate in select patients with persistent symptoms, although no superiority was demonstrated among formulations, preparation methods, or injection strategies. When used as adjuncts to surgical procedures, orthobiologics may improve radiological outcomes, with limited evidence for improvement in clinical outcomes. Technical considerations, including preferred harvest sites and a lack of standardized preparation protocols, were identified, along with key principles for patient counseling Conclusion These international consensus statements establish a standardized framework for the conservative management and use of injectable orthobiologic therapies in OLT. By clarifying indications, technical considerations, and current limitations, this work aims to improve consistency in clinical practice, guide treatment decision-making, and inform future research.

AbstractPurposeWhile several artificial intelligence (AI)‐assisted medical imaging applications are reported in the recent orthopaedic literature, comparison of the clinical efficacy and utility of these applications is currently lacking. The aim of this systematic review is to evaluate the effectiveness and reliability of AI applications in orthopaedic imaging, focusing on their impact on diagnostic accuracy, image segmentation and operational efficiency across various imaging modalities.MethodsBased on the PRISMA guidelines, a comprehensive literature search of PubMed, Cochrane and Scopus databases was performed, using combinations of keywords and MeSH descriptors ('AI', 'ML', 'deep learning', 'orthopaedic surgery' and 'imaging') from inception to March 2024. Included were studies published between September 2018 and February 2024, which evaluated machine learning (ML) model effectiveness in improving orthopaedic imaging. Studies with insufficient data regarding the output variable used to assess the reliability of the ML model, those applying deterministic algorithms, unrelated topics, protocol studies, and other systematic reviews were excluded from the final synthesis. The Joanna Briggs Institute (JBI) Critical Appraisal tool and the Risk Of Bias In Non‐randomised Studies‐of Interventions (ROBINS‐I) tool were applied for the assessment of bias among the included studies.ResultsThe 53 included studies reported the use of 11.990.643 images from several diagnostic instruments. A total of 39 studies reported details in terms of the Dice Similarity Coefficient (DSC), while both accuracy and sensitivity were documented across 15 studies. Precision was reported by 14, specificity by nine, and the F1 score by four of the included studies. Three studies applied the area under the curve (AUC) method to evaluate ML model performance. Among the studies included in the final synthesis, Convolutional Neural Networks (CNN) emerged as the most frequently applied category of ML models, present in 17 studies (32%).ConclusionThe systematic review highlights the diverse application of AI in orthopaedic imaging, demonstrating the capability of various machine learning models in accurately segmenting and analysing orthopaedic images. The results indicate that AI models achieve high performance metrics across different imaging modalities. However, the current body of literature lacks comprehensive statistical analysis and randomized controlled trials, underscoring the need for further research to validate these findings in clinical settings.Level of evidenceSystematic Review; Level of evidence IV.

AbstractArtificial intelligence (AI) has been influencing healthcare and medical research for several years and will likely become indispensable in the near future. AI is intended to support healthcare professionals to make the healthcare system more efficient and ultimately improve patient outcomes. Despite the numerous benefits of AI systems, significant concerns remain. Errors in AI systems can pose serious risks to human health, underscoring the critical need for safety, as well as adherence to ethical and moral standards, before these technologies can be integrated into clinical practice. To address these challenges, the development, certification, and deployment of medical AI systems must adhere to strict and transparent regulations. The European Commission has already established a regulatory framework for AI systems by enacting the European Union Artificial Intelligence Act. This review article, part of an AI learning series, discusses key considerations for medical AI systems such as reliability, accuracy, trustworthiness, lawfulness and legal compliance, ethical and moral alignment, sustainability, and regulatory oversight.Level of EvidenceLevel V.

AbstractPurposeThe present study reviews the available scientific literature on artificial intelligence (AI)‐assisted ultrasound‐guided regional anaesthesia (UGRA) and evaluates the reported intraprocedural parameters and postprocedural outcomes.MethodsA literature search was performed on 19 September 2023, using the Medline, EMBASE, CINAHL, Cochrane Library and Google Scholar databases by experts in electronic searching. All study designs were considered with no restrictions regarding patient characteristics or cohort size. Outcomes assessed included the accuracy of AI‐model tracking, success at the first attempt, differences in outcomes between AI‐assisted and unassisted UGRA, operator feedback and case‐report data.ResultsA joint adaptive median binary pattern (JAMBP) has been applied to improve the tracking procedure, while a particle filter (PF) is involved in feature extraction. JAMBP combined with PF was most accurate on all images for landmark identification, with accuracy scores of 0.83, 0.93 and 0.93 on original, preprocessed and filtered images, respectively. Evaluation of first‐attempt success of spinal needle insertion revealed first‐attempt success in most patients. When comparing AI application versus UGRA alone, a significant statistical difference (p < 0.05) was found for correct block view, correct structure identification and decrease in mean injection time, needle track adjustments and bone encounters in favour of having AI assistance. Assessment of operator feedback revealed that expert and nonexpert operator feedback was overall positive.ConclusionAI appears promising to enhance UGRA as well as to positively influence operator training. AI application of UGRA may improve the identification of anatomical structures and provide guidance for needle placement, reducing the risk of complications and improving patient outcomes.Level of EvidenceLevel IV.