Citations

Abstract Medial meniscus extrusion, particularly in the setting of medial meniscus posterior root tears (MMPRTs), has become an area of interest in recent years due to the correlation between meniscus extrusion with inferior clinical outcomes, progression to osteoarthritis, and increased rate of conversion to total knee arthroplasty after MMPRT repair. This correlation led to the development of meniscus centralisations procedures that can be performed in addition to MMPRT repair and/or high tibial osteotomy. Although it was originally thought that medial meniscus extrusion was a consequence of MMPRTs, it has been reported that meniscus extrusion often precedes the development of MMPRTs, which suggests that there may be underlying meniscus pathology that predisposes to the development of both meniscus extrusion and MMPRTs. The purpose of this narrative review is to provide an overview of the anatomy, pathology, and surgical management of meniscus extrusion and MMPRTs, while identifying areas of future research to further elucidate the relationship meniscus extrusion and MMPRTs that may be necessary to improve outcomes after MMPRT repair. Level of Evidence Level V.

Background: Optimized surgical fixation and meniscal stabilization during rehabilitation increase healing success. However, the latest generation of all-inside devices has not yet been biomechanically compared with inside-out suture tape (IO-ST) repair. Hypothesis: (1) The contact area of a suture anchor (SA) would compensate for a meniscal defect better than polyether ether ketone anchors (PA); (2) adjustable tensioning for all-inside meniscal repair fixation would result in higher initial load than IO-ST repair; and (3) stiffer constructs would decrease secondary displacement. Study Design: Controlled laboratory study. Methods: This study investigates human menisci (N = 39) via microscopic imaging and a biomechanical testing protocol. For the imaging protocol, needles of an all-inside SA or PA device and an IO-ST device were inserted after staining to measure the iatrogenic defect created by the needle insertion (n = 20) and the length, width, and meniscus contact area of deployed all-inside anchors (n = 6). For biomechanical testing, menisci with longitudinal bucket handle tears were prepared, and single stitches were repaired (each n = 9). After suture tensioning (50 N) and fixation, initial load, initial stiffness, and relief displacement were measured. Constructs underwent cyclic loading between 2 and 20 N, with 10,000 cycles (0.75 Hz), and stiffness and displacement were measured. Ultimate stiffness and load-to-failure were analyzed at 3.15 mm/sec. Results: All-inside needles created greater iatrogenic meniscal defects ( P < .001) than IO-ST repair. While PAs were longer ( P < .001), SAs were wider with a greater meniscal contact area (both P < .001). IO-ST repair resulted in the lowest initial load ( P < .001) and relief displacement ( P < .001), whereas SA repair resulted in a higher initial load ( P < .007) and stiffness ( P < .023) than PA repair. The overall stiffer SA fixation ( P < 001) significantly reduced cyclic displacement compared with other repairs ( P < .044). The PA group failed due to an anchor fracture at a significantly lower load (84.3 ± 10.7 N; P < .001) than the IO-ST (136.4 ± 10.5 N) and the SA repair (122.1 ± 17.5 N), with a suture-based failure mode. The ultimate stiffness of SA constructs was higher ( P < .045) than that of other repairs. Conclusion: While all-inside devices showed improved primary stability, the IO-ST construct demonstrated the highest load-to-failure. In a human cadaveric model, meniscal repair with a more compact and conforming SA was stiffer and reduced cyclic displacement compared with PA and IO-ST repair. Clinical Relevance: All-inside SA repair improved primary stability. Future clinical series will define the overall significance of healing rates.

Purpose To develop an expert consensus statement on the diagnosis, management, and rehabilitation of medial meniscal root tears (MMRTs) using a modified Delphi technique. Methods A working group developed statements on MMRT diagnosis, nonoperative management, surgical indications, surgical management, alignment, and rehabilitation using modified Delphi techniques. Fifty‐six experts were surveyed over 3 rounds to reach consensus, with agreement measured on a 5‐point Likert scale. Statements were included, revised, or excluded on the basis of predefined thresholds (≥75% agreement, <20% disagreement). Experts suggested revisions or new statements in the first 2 rounds, and final consensus statements were included. Results All 56 experts completed 3 survey rounds. Experts agreed that root tears may occur with no known history of trauma, typically in older patients, and that it should be diagnosed with an magnetic resonance imaging. In symptomatic patients with MMRTs without advanced osteoarthritis should be repaired using an anatomic transtibial pull‐out technique (performing a pie‐crusting technique can be helpful for visualization). Nonoperative management is advised for patients with advanced osteoarthritis. High tibial osteotomy may be considered for significant varus malalignment during MMRT repair. The only statement without consensus was the management of asymptomatic MMRTs with mild medial compartment cartilage wear, indicating ongoing debate. Conclusions Overall, 98% of statements reached consensus. There is agreement that magnetic resonance imaging is the gold standard for diagnosis. Symptomatic MMRTs without advanced osteoarthritis should be repaired early using an anatomic transtibial pull‐out technique. End‐stage knee osteoarthritis warrants nonoperative management of MMRTs, and a structured postoperative protocol with limited weightbearing and range of motion is essential after repair. No agreement was reached on managing asymptomatic MMRTs in patients without significant medial compartment degeneration. Meniscal centralization sutures may help in cases of substantial extrusion, but their routine use is debated. Level of Evidence Level V, consensus of expert opinion.

Abstract Purpose The purpose of this article is to explore the potential of digital twin technologies in orthopaedics and to evaluate how their integration with artificial intelligence (AI) and deep learning (DL) can improve orthopaedic evaluation and treatment. This review addresses key applications of digital twins, including surgical planning, patient‐specific outcome prediction, augmented reality‐assisted surgery and simulation‐based surgical training. Methods Existing studies on digital twins in various domains, including engineering, biomedical and orthopaedics are reviewed. We also reviewed advancements in AI and DL relevant to digital twins. We focused on identifying key benefits, challenges and future directions for the implementation of digital twins in orthopaedic practice. Results The review highlights that digital twins offer significant potential to revolutionise orthopaedic care by enabling precise surgical planning, real‐time outcome prediction and enhanced training. Digital twins can model patient‐specific anatomy using advanced imaging techniques and dynamically update with real‐time data, providing valuable insights during surgery and postoperative care. However, challenges such as the need for large‐scale data sets, technological limitations and integration issues must be addressed to fully realise these benefits. Conclusion Digital twins represent a promising frontier in orthopaedic research and practice, with the potential to improve patient outcomes and enhance surgical precision. To enable widespread adoption, future research must focus on overcoming current challenges and further refining the integration of digital twins with AI and DL technologies. Level of Evidence Level V.

Multiligament knee injuries (MLKIs) represent a broad spectrum of pathology with potentially devastating consequences. Currently, disagreement in the terminology, diagnosis and treatment of these injuries limits clinical care and research. This study aimed to develop consensus on the nomenclature, diagnosis, treatment and rehabilitation strategies for patients with MLKI, while identifying important research priorities for further study. An international consensus process was conducted using validated Delphi methodology in line with British Journal of Sports Medicine guidelines. A multidisciplinary panel of 39 members from 14 countries, completed 3 rounds of online surveys exploring aspects of nomenclature, diagnosis, treatment, rehabilitation and future research priorities. Levels of agreement (LoA) with each statement were rated anonymously on a 5-point Likert scale, with experts encouraged to suggest modifications or additional statements. LoA for consensus in the final round were defined ‘a priori’ if >75% of respondents agreed and fewer than 10% disagreed, and dissenting viewpoints were recorded and discussed. After three Delphi rounds, 50 items (92.6%) reached consensus. Key statements that reached consensus within nomenclature included a clear definition for MLKI (LoA 97.4%) and the need for an updated MLKI classification system that classifies injury mechanism, extent of non-ligamentous structures injured and the presence or absence of dislocation. Within diagnosis, consensus was reached that there should be a low threshold for assessment with CT angiography for MLKI within a high-energy context and for certain injury patterns including bicruciate and PLC injuries (LoA 89.7%). The value of stress radiography or intraoperative fluoroscopy also reached consensus (LoA 89.7%). Within treatment, it was generally agreed that existing literature generally favours operative management of MLKI, particularly for young patients (LoA 100%), and that single-stage surgery should be performed whenever possible (LoA 92.3%). This consensus statement will facilitate clinical communication in MLKI, the care of these patients and future research within MLKI.

AbstractThe application of machine learning (ML) to the field of orthopaedic surgery is rapidly increasing, but many surgeons remain unfamiliar with the nuances of this novel technique. With this editorial, we address a fundamental topic—the differences between ML techniques and traditional statistics. By doing so, we aim to further familiarize the reader with the new opportunities available thanks to the ML approach.

Abstract The application of artificial intelligence (AI) and machine learning to the field of orthopaedic surgery is rapidly increasing. While this represents an important step in the advancement of our specialty, the concept of AI is rich with statistical jargon and techniques unfamiliar to many clinicians. This knowledge gap may limit the impact and potential of these novel techniques. We aim to narrow this gap in a way that is accessible for all orthopaedic surgeons. With this manuscript, we introduce the concept of AI and machine learning and give examples of how it can impact clinical practice and patient care. Level of evidence VI.