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To develop a deep learning model to accurately detect anterior cruciate ligament (ACL) ruptures on magnetic resonance imaging (MRI) and to evaluate its effect on the diagnostic accuracy and efficiency of clinicians. A training dataset was built from MRIs acquired from January 2017 to June 2021, including patients with knee symptoms, irrespective of ACL ruptures. An external validation dataset was built from MRIs acquired from January 2021 to June 2022, including patients who underwent knee arthroscopy or arthroplasty. Patients with fractures or prior knee surgeries were excluded in both datasets. Subsequently, a deep learning model was developed and validated using these datasets. Clinicians of varying expertise levels in sports medicine and radiology were recruited, and their capacities in diagnosing ACL injuries in terms of accuracy and diagnosing time were evaluated both with and without artificial intelligence (AI) assistance. A deep learning model was developed based on the training dataset of 22,767 MRIs from 5 centers and verified with external validation dataset of 4,086 MRIs from 6 centers. The model achieved an area under the receiver operating characteristic curve of 0.987 and a sensitivity and specificity of 95.1%. Thirty-eight clinicians from 25 centers were recruited to diagnose 3,800 MRIs. The AI assistance significantly improved the accuracy of all clinicians, exceeding 96%. Additionally, a notable reduction in diagnostic time was observed. The most significant improvements in accuracy and time efficiency were observed in the trainee groups, suggesting that AI support is particularly beneficial for clinicians with moderately limited diagnostic expertise. This deep learning model demonstrated expert-level diagnostic performance for ACL ruptures, serving as a valuable tool to assist clinicians of various specialties and experience levels in making accurate and efficient diagnoses. Level III, retrospective comparative case series. [ABSTRACT FROM AUTHOR]

Meniscal allograft transplantation (MAT) effectively alleviates symptoms of the meniscus deficiency. Thus, MAT is a widely accepted and recommended treatment for individuals with unicompartmental pain due to meniscus deficiency. Long-term follow-up studies have indicated that MAT yields favorable clinical outcomes, demonstrating high survivorship and low rates of serious complications. In addition, the ability of MAT to function akin to the native meniscus and shield the knee cartilage from osteoarthritis has been a subject of ongoing investigation, and recent direct magnetic resonance imaging evidence shows long-term chondroprotection following MAT. Cartilage lesions worsen during the meniscus deficiency period. Consequently, delaying MAT until patients become more symptomatic may lead to poor outcomes and low graft survivorship due to concomitant cartilage lesions. These findings prompt a reevaluation of the purpose and timing of MAT decisions for meniscectomy patients, suggesting a more proactive approach to recommending MAT, particularly for patients at high risk of postmeniscectomy syndrome and osteoarthritis progression. [ABSTRACT FROM AUTHOR]

Lateral meniscal posterior root tears are defined as tears of meniscus within 9 mm from the bony root attachment. Unrepaired root tears significantly compromise the biomechanical functions of the meniscus, resulting in early and accelerated osteoarthritis. Several authors have described various techniques to reattach the posterior root of the lateral meniscus, and decent short-term results have been observed. Although most previous techniques are capable of repairing radial root tears, difficulties are encountered when repairing oblique type IV tears. In this technique note, the author describes an efficient side-to-side repair technique using the FAST-FIX system, which is practical in repairing the type IV tears of the lateral meniscus. This technique includes certain sequence of the stitches and skills of controlling the root remnant, which makes it easy to penetrate the meniscus and to achieve anatomic repair. [Display omitted] [ABSTRACT FROM AUTHOR]