Enhanced reliability and time efficiency of deep learning-based posterior tibial slope measurement over manual techniques.

PURPOSE: Multifaceted factors contribute to inferior outcomes following anterior cruciate ligament (ACL) reconstruction surgery. A particular focus is placed on the posterior tibial slope (PTS). This study introduces the integration of machine learning and artificial intelligence (AI) for efficient measurements of tibial slopes on magnetic resonance imaging images as a promising solution. This advancement aims to enhance risk stratification, diagnostic insights, intervention prognosis and surgical planning for ACL injuries. METHODS: Images and demographic information from 120 patients who underwent ACL reconstruction surgery were used for this study. An AI-driven model was developed to measure the posterior lateral tibial slope using the YOLOv8 algorithm. The accuracy of the lateral tibial slope, medial tibial slope and tibial longitudinal axis measurements was assessed, and the results reached high levels of reliability. This study employed machine learning and AI techniques to provide objective, consistent and efficient measurements of tibial slopes on MR images. RESULTS: Three distinct models were developed to derive AI-based measurements. The study results revealed a substantial correlation between the measurements obtained from the AI models and those obtained by the orthopaedic surgeon across three parameters: lateral tibial slope, medial tibial slope and tibial longitudinal axis. Specifically, the Pearson correlation coefficients were 0.673, 0.850 and 0.839, respectively. The Spearman rank correlation coefficients were 0.736, 0.861 and 0.738, respectively. Additionally, the interclass correlation coefficients were 0.63, 0.84 and 0.84, respectively. CONCLUSION: This study establishes that the deep learning-based method for measuring posterior tibial slopes strongly correlates with the evaluations of expert orthopaedic surgeons. The time efficiency and consistency of this technique suggest its utility in clinical practice, promising to enhance workflow, risk assessment and the customization of patient treatment plans. LEVEL OF EVIDENCE: Level III, cross-sectional diagnostic study.

Proximal tibial morphology and risk of posterior tibial cortex impingement in patients with AA-sized Oxford unicompartmental knee arthroplasty tibial implants.

BACKGROUND: In cases of Oxford unicompartmental knee arthroplasty (UKA), an increase in anteroposterior and medial-lateral length is usually disproportional when comparing AA and A-sized tibial components. Asynchronous increments may cause tibial keel impingement leading to complications. METHODS: Radiographic measurements were performed in five patients with AA-sized tibial implants. The posterior cortex of proximal tibia had two angles recorded as ∠ M1 and ∠ M2. The minimum distance between the tibial component keel and outer margin of the posterior tibial cortex (mDKC) was measured, and the correlation between the preoperative posterior slope angle (PSA), ∠ M1, and mDKC was analyzed. RESULTS: All patients showed an acceptable component positioning. Only one patient had an mDKC of < 4 mm that fulfilled the criteria for the posterior tibial cortex at risk. The patient had an increased PSA and ∠ M1 compared to other patients. A negative correlation was found between preoperative PSA and mDKC (r = - 0.935, p = 0.0193); and ∠ M1 and mDKC (r = - 0.969, p = 0.0032). However, no stem tip pain, periprosthetic fracture, or component loosening were observed. CONCLUSIONS: The distance between the tibial keel and posterior tibial cortex was reduced in AA-sized patients with a large PSA and ∠M1; therefore, the risk of the tibial cortex injury should be considered.