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Anterior cruciate ligament (ACL) deficient and reconstructed knees display altered biomechanics during gait. Identifying significant gait changes is important for understanding normal and ACL function and is typically performed by statistical approaches. This paper focuses on the development of an explainable machine learning (ML) empowered methodology to: (i) identify important gait kinematic, kinetic parameters and quantify their contribution in the diagnosis of ACL injury and (ii) investigate the differences in sagittal plane kinematics and kinetics of the gait cycle between ACL deficient, ACL reconstructed and healthy individuals. For this aim, an extensive experimental setup was designed in which three-dimensional ground reaction forces and sagittal plane kinematic as well as kinetic parameters were collected from 151 subjects. The effectiveness of the proposed methodology was evaluated using a comparative analysis with eight well-known classifiers. Support Vector Machines were proved to be the best performing model (accuracy of 94.95%) on a group of 21 selected biomechanical parameters. Neural Networks accomplished the second best performance (92.89%). A state-of-the-art explainability analysis based on SHapley Additive exPlanations (SHAP) and conventional statistical analysis were then employed to quantify the contribution of the input biomechanical parameters in the diagnosis of ACL injury. Features, that would have been neglected by the traditional statistical analysis, were identified as contributing parameters having significant impact on the ML model’s output for ACL injury during gait.

The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim of this paper is to present the findings of a systematic literature review of knee (anterior cruciate ligament, meniscus, and cartilage) injury detection papers using deep learning. The systematic review was carried out following the PRISMA guidelines on several databases, including PubMed, Cochrane Library, EMBASE, and Google Scholar. Appropriate metrics were chosen to interpret the results. The prediction accuracy of the deep-learning models for the identification of knee injuries ranged from 72.5–100%. Deep learning has the potential to act at par with human-level performance in decision-making tasks related to the MRI-based diagnosis of knee injuries. The limitations of the present deep-learning approaches include data imbalance, model generalizability across different centers, verification bias, lack of related classification studies with more than two classes, and ground-truth subjectivity. There are several possible avenues of further exploration of deep learning for improving MRI-based knee injury diagnosis. Explainability and lightweightness of the deployed deep-learning systems are expected to become crucial enablers for their widespread use in clinical practice.

Background: Long-term studies of patients after anterior cruciate ligament (ACL) reconstruction with or without concomitant meniscal tear treatment are limited. Purposes: To (1) report postoperative outcomes after anatomic ACL reconstruction with a hamstring autograft, (2) investigate how concomitant treatment of meniscal injury could affect these outcomes, and (3) evaluate the association between quality of life and activity levels at a minimum 10-year follow-up. Study Design: Cohort study; Level of evidence, 3. Methods: Patients treated with a unilateral, anatomic ACL reconstruction between 2005 and 2011 were investigated. The following patient-reported outcome measures (PROMs) were reported for the overall sample as well as a subsample of patients with meniscal injury: International Knee Documentation Committee Subjective Knee Form (IKDC-SKF), Knee injury and Osteoarthritis Outcome Score (KOOS), Lysholm knee score, Tegner activity scale, 5-level EQ-5D (EQ-5D-5L), and patient satisfaction. Sex, age, body mass index (BMI), and meniscal injury treatment (meniscectomy vs meniscal repair) were examined as patient-specific risk factors regarding long-term activity and quality of life. Results: Overall, 106 patients, 90 men (85%) and 16 women (15%), were enrolled in the study, with a mean follow-up of 13.2 years. The ACL retear rate was 2.8%. The mean scores were 80.6 ± 16.7 (IKDC-SKF), 87.4 ± 15.0 (KOOS), 90.5 ± 11.5 (Lysholm), 5.6 ± 1.9 (Tegner), and 91.8 ± 14.5 (EQ-5D-5L). The majority (90.6%) of patients considered their knee state satisfactory during follow-up. When compared with patients who underwent meniscal repair, patients who underwent meniscectomy had statistically significantly lower scores on all PROMs except for the Tegner and EQ-5D-5L (P < .05 for all). The mean difference between the 2 groups was ≥7 points on all PROM scores. Patient sex, age, and BMI did not affect PROM scores. There was a statistically significant, strong positive correlation between quality of life and activity. Conclusion: Patients had few or no symptoms and considered their knee state satisfactory 13.2 years after anatomic ACL reconstruction. Patients with concomitant meniscal tears having undergone meniscal repair had improved PROMs compared with those treated with meniscectomy. Finally, participation in activities of daily living and sports was interrelated with quality of life and was not affected by patient age, sex, or BMI.

Purpose Osteochondral knee defects usually affect young, active patients and may alter knee biomechanics and progressively lead to joint degeneration. Various treatment options exist with autologous, impaction bone grafting in combination with autologous matrix-induced chondrogenesis (BG-AMIC) being a less-expensive, one-step, promising option. The purpose of this study is to evaluate the clinical and radiological mid-term outcomes of large osteochondral lesions treated with BG-AMIC, identify a possible correlation between the two and report postoperative complications and reoperation rate. Methods A retrospective analysis of 25 patients treated with the BG-AMIC technique for knee osteochondral lesions was performed. Patients were assessed using the following PROMs: the IKDC, the KOOS and the Lysholm score, the Tegner activity scale and a patient acceptable symptom state (PASS). The EQ-5D-5L score was used to assess health-related quality of life. Radiological assessment was performed using the MOCART 2.0 score on a 3 T MRI. Results At a mean of 3.8 (± 0.8)-year follow-up, all functional scores increased significantly ( p  < 0.005) when compared to the preoperative baseline. IKDC increased from 44.5 (± 15.9) to 81.4 (± 14.7), KOOS from 41.5 (± 16.1) to 91.6 (± 11.6) and Lysholm from 54.4 (± 23) to 95.2 (± 5.5) ( p  < 0.005). The EQ-5D-5L score also revealed a significant improvement [59.9 (± 25) to 93.4 (± 10.2), p  < 0.005]. Mean Tegner score reached pre-injury levels. The PASS was positive in 100% of patients. The minimum clinically important difference was reached in all PROMs except for the KOOS Sports subscale. There were no re-operations. Morphological evaluation of the repair tissue using the MOCART 2.0 score revealed a mean total score of 52.8 (± 30.5). A statistically significant, positive correlation was found between the MOCART 2.0 score and the IKDC score, the KOOS ADL subscale and the EQ-5D-5L ( p  < 0.05). Conclusion BG-AMIC is a safe and reliable option for treating deep, knee osteochondral lesions, providing a statistically significant and clinically important improvement in patient-reported outcomes. No complications were noticed, and no re-operations were performed after the procedure. A moderate positive correlation between the MOCART 2.0 score and the IKDC, KOOS ADL and EQ-5D-5L was noticed. However, this correlation is not necessarily clinically relevant, and excellent clinical results can be expected even in patients with low MOCART scores. Level of evidence III.

Modern lifestyles require new tools for determining a person’s ability to return to daily activities after knee surgery. These quantitative instruments must feature high discrimination, be non-invasive, and be inexpensive. Machine learning is a revolutionary approach that has the potential to satisfy the aforementioned requirements and bridge the knowledge gap. The scope of this study is to summarize the results of a systematic literature review on the identification of gait-related changes and the determination of the functional recovery status of patients after knee surgery using advanced machine learning algorithms. The current systematic review was conducted using multiple databases in accordance with the PRISMA guidelines, including Scopus, PubMed, and Semantic Scholar. Six out of the 405 articles met our inclusion criteria and were directly related to the quantification of the recovery status using machine learning and gait data. The results were interpreted using appropriate metrics. The results demonstrated a recent increase in the use of sophisticated machine learning techniques that can provide robust decision-making support during personalized post-treatment interventions for knee-surgery patients.

PurposeHip fractures are associated with functional decline and increased mortality. The aim of this study was to investigate the effect of zoledronic acid and high-dose vitamin D on function and mortality after hip fractures.Patients and methodsForty-five patients received zoledronic acid and high dose of vitamin D during hospitalization after fracture management. These patients were compared with a control group of 46 patients. Pre- and postoperative prospectively collected data including ASA score, Charlson comorbidity score, presence of dementia, Vitamin D, and the Barthel index were available. Final follow-up was performed after one year. Primary outcome was patients’ function at final follow-up as measured with Barthel index score. Secondary outcomes included mortality, assessment of pain, and complications.ResultsBarthel index score at final follow-up was decreased in both groups. There was no significant difference in Barthel index between the two groups (15.5 ± 5.0 vs 15.8 ± 5.8, p = 0.850). However, the Barthel index in the control group decreased beyond the smallest detectable change (3 points). Mortality was statistically different between groups (8.8% vs 28.2%, p = 0.047). Complications and pain at final follow-up were not different between groups. Multivariate analysis revealed that preoperative Barthel index and Charlson comorbidity score independently affected function at final follow-up. Logistic regression analysis disclosed that not receiving active treatment and complications were associated with increased mortality.ConclusionsMedical treatment after surgical management of hip fractures results in reduced mortality and lessens the functional decline associated with these fractures.