Hamrin Senorski, Eric

AbstractPurposePart two of this consensus aimed to provide recommendations for the prevention of meniscus injuries, non‐operative treatment of acute tears and degenerative lesions, return to sports and patient‐reported outcome measures.MethodsThis consensus followed the European Society of Knee Surgery, Sports Traumatology and Arthroscopy (ESSKA) formal consensus methodology. For this combined ESSKA—American Orthopedic Society for Sports Medicine (AOSSM)—American Academy of Sports Physical Therapy (AASPT) initiative, 67 experts from 14 countries, including orthopedic surgeons and physiotherapists, were involved. The 26 Steering Group members established guiding questions, screened the existing evidence, and proposed statements, and provided Grades of recommendations. The 41 Rating Group members assessed the statements according to a Likert scale (1–9). Final documents were assessed by an international peer review group for geographical adaptability.ResultsLow to moderate scientific level of evidence was available, so that grades of recommendations were low (three Grade A ratings, four Grade B, three Grade C and 13 Grade D), underlining the relevance of this consensus. One strong and 17 relative agreements with overall median of 8 (8–9) and a mean of 7.92 ± 0.37 were achieved for 23 statements on 18 questions. Prevention of meniscus injuries is possible with general knee injury reduction programmes and through avoidance of certain activities. Non‐operative treatment including physical therapy is the first line approach for degenerative meniscus lesions and may be an option for some acute tears. Return to sports after meniscus tear surgery should be both criterion‐based and time‐based. Patient reported outcomes in combination with performance‐based measures are recommended to evaluate the rehabilitation process.ConclusionThis international EU–US consensus established recommendations for prevention strategies, describes rehabilitation of non‐operated patients and of patients after partial meniscectomy, meniscus repair and meniscus reconstruction, and establishes return to sport criteria. These updated and structured recommendations may be applied by surgeons and physiotherapists.Level of EvidenceLevel I, consensus.

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.

AbstractArtificial 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 EvidenceLevel V.

Background: Factors relating to the patient and anterior cruciate ligament (ACL) reconstruction may help to identify prognostic factors of long-term outcome after reconstruction. Purpose: To determine 10-year risk factors for inferior knee function after ACL reconstruction. Study Design: Cohort study; Level of evidence, 2. Methods: Prospectively collected data from the Swedish National Knee Ligament Register were extracted for patients who underwent ACL reconstruction between January 2005 and December 2006. Patients who had no data at the 10-year follow-up for the Knee injury and Osteoarthritis Outcome Score (KOOS) were excluded. Multivariable proportional odds regression modeling was used to assess 10-year patient- and surgery-related risk factors across all the KOOS subscales and the KOOS 4 (mean score of 4 subscales: pain, knee-related symptoms, function in sport and recreation, and knee-related quality of life). Results: A total of 874 (41%) patients were included (male, 51.5%; median age at the time of ACL reconstruction, 27.5 years [range, 11.2-61.5 years]). An increase in the severity of concomitant articular cartilage injuries resulted in a reduced KOOS on 4 subscales (odds ratio, 0.64-0.80; P < .05). A higher preoperative KOOS pain score increased the odds of a higher score on the pain, symptoms, and sport subscales and the KOOS 4 . In addition, a higher preoperative body mass index was a significant risk factor for lower scores on 3 KOOS subscales and the KOOS 4 . No patient- or surgery-related predictor was significant across all KOOS subscales. Conclusion: This 10-year risk factor analysis identified several factors that can affect long-term knee function after ACL reconstruction. Most risk factors were related to preoperative patient-reported outcome and potentially modifiable. On the other hand, most of the surgery-related risk factors were nonmodifiable. Nevertheless, this information may be helpful to physicians and physical therapists counseling patients on their expectations of outcome after ACL reconstruction.