Evaluation of a deep learning approach for the detection of meniscal tears and their characterization (presence/absence of migrated meniscal fragment). Methods: A large annotated adult knee MRI database was built combining medical expertise of radiologists and data scientists' tools. Coronal and sagittal proton density fat suppressed-weighted images of 11,353 knee MRI examinations (10,401 individual patients) paired with their standardized structured reports were retrospectively collected. After database curation, deep learning models were trained and validated on a subset of 8058 examinations. Algorithm performance was evaluated on a test set of 299 examinations reviewed by 5 musculoskeletal specialists and compared to general radiologists' reports. External validation was performed using the publicly available MRNet database. Receiver Operating Characteristic (ROC) curves results and Area Under the Curve (AUC) values were obtained on internal and external databases. Results: A combined architecture of meniscal localization and lesion classification 3D convolutional neural networks reached AUC values of 0.93 (95% CI 0.82, 0.95) for medial and 0.84 (95% CI 0.78, 0.89) for lateral meniscal tear detection, and 0.91 (95% CI 0.87, 0.94) for medial and 0.95 (95% CI 0.92, 0.97) for lateral meniscal tear migration detection. External validation of the combined medial and lateral meniscal tear detection models resulted in an AUC of 0.83 (95% CI 0.75, 0.90) without further training and 0.89 (95% CI 0.82, 0.95) with fine tuning.
Conclusion:Our deep learning algorithm demonstrated high performance in knee menisci lesion detection and characterization, validated on an external database.
An otherwise healthy 24-year-old male was sent to the emergency department by an urgent care center with remote concern for appendicitis. The patient was afebrile, eating and drinking normally and non-leukocytotic, but because of mild tenderness to palpation in the right lower quadrant of his abdomen and symptomatology persisting for seven days computed tomography was obtained which revealed a ruptured appendix and localized peritonitis. The patient was admitted to the acute care emergency surgery service and managed non-operatively with antibiotics.
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