Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation. (March 2021)

• Record Type: Journal Article
• Authors: Rizk, B.; Brat, H.; Zille, P.; Guillin, R.; Pouchy, C.; Adam, C.; Ardon, R.; d'Assignies, G.
• Is Part Of: Physica medica. Volume 83(2021)
• Page Start: 64
• Page End: 71
• Publication Date: 2021-03
• Subjects: Meniscus -- Deep learning -- Knee -- Magnetic Resonance Imaging; MRI Magnetic Resonance Imaging -- DL Deep learning -- CNN Convolutional Neural Networks -- ACL Anterior Cruciate Ligament -- SFR Société Française de Radiologie (French Radiology Society) -- MSK MusculoSKeletal -- AI Artificial Intelligence -- PD Proton Density -- FS Fat Suppressed -- NLP Natural Language Processing -- IoU Intersection over Union -- ReLU Rectified Linear Unit -- GRU Gated Recurrent Unit -- CBOW Continuous Bag of Words -- ROC Receiver Operating Characteristic -- AUC Area Under the Curve -- DICOM Digital Imaging and COmmunications in Medicine -- CI Confidence Interval; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.02.010
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: This study aims at bridging the gap of bringing AI into routine radiologist practice. First externally validated meniscal tear detection algorithm. A clinically relevant algorithm supporting radiologists in unstable meniscal lesions. Abstract: Purpose: 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.