To investigate the feasibility of using a deep learning-based approach to detect an anterior cruciate ligament (ACL) tear within the knee joint at MRI by using arthroscopy as the reference standard. Materials and Methods: A fully automated deep learning-based diagnosis system was developed by using two deep convolutional neural networks (CNNs) to isolate the ACL on MR images followed by a classification CNN to detect structural abnormalities within the isolated ligament. With institutional review board approval, sagittal proton density-weighted and fat-suppressed T2-weighted fast spinecho MR images of the knee in 175 subjects with a full-thickness ACL tear (98 male subjects and 77 female subjects; average age, 27.5 years) and 175 subjects with an intact ACL (100 male subjects and 75 female subjects; average age, 39.4 years) were retrospectively analyzed by using the deep learning approach. Sensitivity and specificity of the ACL tear detection system and five clinical radiologists for detecting an ACL tear were determined by using arthroscopic results as the reference standard. Receiver operating characteristic (ROC) analysis and two-sided exact binomial tests were used to further assess diagnostic performance. Results: The sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively. In comparison, the sensitivity of the clinical radiologists ranged between 0.96 and 0.98, while the specificity ranged between 0.90 and 0.98. There was no statistically significant difference in diagnostic performance between the ACL tear detection system and clinical radiologists at P < .05. The area under the ROC curve for the ACL tear detection system was 0.98, indicating high overall diagnostic accuracy. Conclusion: There was no significant difference between the diagnostic performance of the ACL tear detection system and clinical radiologists for determining the presence or absence of an ACL tear at MRI.

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Background While no "gold-standard" pharmacotherapy for nonalcoholic fatty liver disease (NAFLD) is yet established, essential phospholipids (EPLs) are reported to decrease steatosis and improve laboratory parameters. Objective This analysis evaluated adherence and satisfaction with EPL treatment as patient-reported outcomes and their relationship with changes in laboratory and ultrasound parameters among Russian patients with NAFLD. Methods Data were pooled from three observational Russian studies- MANPOWER (2015MANPOWER ( -2016, LIDER 1 (2012-2013), and LIDER 2 (2013)-in which EPLs were used for at least 12 weeks in the treatment of liver diseases and which measured both subjective and objective endpoints. Only patients who had NAFLD were included in this analysis. The main endpoints were to determine treatment adherence and satisfaction with 12 weeks of EPL therapy, relationship between adherence/satisfaction and changes in the laboratory and ultrasound parameters. A secondary subgroup analysis was performed to identify patients with NAFLD who responded better (or worse) to 24 weeks of adjunctive EPL treatment. Results Overall, 3384 patients were included. A total of 82.2% of patients were adherent to 12 weeks of EPL treatment; high/very high satisfaction was reported by 15.3%/65.9% of clinicians and 15.9%/64.4% of patients. There was positive correlation between patients' adherence and satisfaction and significant improvement in laboratory (transaminases, lipid profile; p < 0.001) and ultrasound (steatosis, p < 0.001) parameters, and improvement in symptoms (p < 0.001) after 24 weeks of EPL. Male patients, patients with unhealthy lifestyles, and those with more comorbidities showed a better response in laboratory and ultrasound parameters. Conclusions Patients with NAFLD treated with adjunctive EPL therapy in real-world clinical practice in Russia showed good treatment adherence and treatment satisfaction. Improvements in laboratory and ultrasound parameters, as well as dynamics of patient symptoms, were positively correlated with adherence and satisfaction.