Background: There is limited knowledge about how the anterior cruciate ligament (ACL) and capsuloligamentous structures on the medial side of the knee act to control anteromedial rotatory knee instability. Purpose: To investigate the contribution of the medial retinaculum, capsular structures (anteromedial capsule, deep medial collateral ligament [MCL], and posterior oblique ligament), and different fiber regions of the superficial MCL to restraining knee laxity, including anteromedial rotatory instability. Study Design: Controlled laboratory study. Methods: Eight fresh-frozen human cadaveric knees were tested using a 6 degrees of freedom robotic testing system in a position-controlled mode. Loads of 10 N·m valgus rotation, 5 N·m tibial external rotation, 5 N·m tibial internal rotation, and 134 N anterior tibial translation in 5 N·m external rotation were applied at different flexion angles. The motion of the intact knee at 0° to 120° of flexion was replicated after sequential excision of the sartorial fascia; anteromedial retinaculum; anteromedial capsule; anterior, middle, and posterior fibers of the superficial MCL; the deep MCL; the posterior oblique ligament; and the ACL. The reduction in force/torque indicated the contribution of each resected structure to resisting laxity. A repeated-measures analysis of variance with a post hoc Bonferroni test was used to analyze the relative force and torque changes from the intact state. Results: The superficial MCL was the most important restraint to valgus rotation from 0° to 120° and provided the largest contribution to resisting external rotation between 30° and 120° of knee flexion, gradually increasing from 25.2% ± 7.4% at 30° to 36.9% ± 15.4% at 90°. The posterior oblique ligament contributed significantly to resisting valgus rotation only in extension (17.2% ± 12.1%) but was the major restraint to internal rotation at 0° (46.7% ± 13.1%) and 30° (30.4% ± 17.7%) of flexion. The sartorial fascia and anteromedial retinaculum resisted ER at all knee flexion angles ( P < .05) and was the single most important restraint in the extended knee (19.5% ± 11%). The capsular structures (anteromedial capsule and deep MCL) had a combined contribution of 20% ± 11.5% at 0° and 23.4% ± 10.5% at 120° of knee flexion but were less important from 30° to 90°. The ACL was the primary restraint to anterior tibial translation in external rotation between 0° and 60° of flexion (50.2% ± 16.9% at 30°), but the superficial MCL was more important at 90° to 120° of knee flexion (36.8% ± 16.4% at 90°). The anterior, middle, and posterior regions of the superficial MCL contributed differently to the simulated laxity tests. The anterior fibers were the most important part of the superficial MCL in resisting external rotation and combined anterior tibial translation in external rotation. Conclusion: The superficial MCL not only was the primary restraint to valgus rotation throughout the range of knee flexion but also importantly contributed to resisting anterior tibial translation in external rotation, particularly in deeper flexion in the cadaveric model. The anterior fibers of the superficial MCL are the most important superficial MCL fibers in resisting anterior tibial translation in external rotation. This study suggests that a medial reconstruction that reproduces the function of the posterior MCL fibers and posterior oblique ligament may not best control anteromedial rotatory instability. Clinical Relevance: Based on these data, there is a need for an individualized medial reconstruction to address different types of medial injury patterns and instabilities.

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.

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.

Purpose: The purpose of this study was to retrospectively analyse the pattern of injury to the medial knee structures in anterior cruciate ligament (ACL) injured patients. It was hypothesised that anteromedial injuries would be more common than posteromedial lesions.Methods: One hundred and twenty subjects aged 18–25 years with a primary ACL injury were included. Patients were excluded if the time between injury and magnetic resonance imaging (MRI) was more than 28 days or if a knee dislocation or fracture was present. The MRIs were analysed with particular emphasis on injuries to the medial knee structures, menisci and bone bruise patterns. Injuries to the ligaments and anteromedial retinaculum (AMR) were graded according to severity, ranging from periligamentous oedema (grade I), partial fibre disruption of less or more than 50% (grade IIa or IIb) to complete tears (grade III).Results: AMR injury was seen in 87 subjects (72.5%) on the coronal plane and in 88 (73.3%) on the axial plane, with grade III lesions observed in 27 (22.5%) and 29 knees (24.2%). Injuries to the superficial medial collateral ligament (sMCL), deep MCL (dMCL) and posterior oblique ligament (POL) were detected in 60 patients (50%), 93 patients (77.5%) and 38 patients (31.6%). However, grade III injuries to the POL were observed in only seven knees (5.8%). Medial meniscus injuries were associated with lesions of the sMCL and AMR (p < 0.05), while lateral meniscus injuries were significantly more common in patients with dMCL rupture (p < 0.05).Conclusion: Data from this study suggest that injuries to the AMR are much more common than posteromedial lesions in subjects with ACL injuries.Level of Evidence: Level IV.