Abstract
BACKGROUND: Semiquantitative assessment of MRI plays a central role in musculoskeletal research; however, in the clinical setting MRI reports often tend to be subjective and qualitative. Grading schemes utilized in research are not used because they are extraordinarily time-consuming and unfeasible in clinical practice. PURPOSE: To evaluate the ability of deep-learning models to detect and stage severity of meniscus and patellofemoral cartilage lesions in osteoarthritis and anterior cruciate ligament (ACL) subjects. STUDY TYPE: Retrospective study aimed to evaluate a technical development. POPULATION: In all, 1478 MRI studies, including subjects at various stages of osteoarthritis and after ACL injury and reconstruction. FIELD STRENGTH/SEQUENCE: 3T MRI, 3D FSE CUBE. ASSESSMENT: Automatic segmentation of cartilage and meniscus using 2D U-Net, automatic detection, and severity staging of meniscus and cartilage lesion with a 3D convolutional neural network (3D-CNN). STATISTICAL TESTS: Receiver operating characteristic (ROC) curve, specificity and sensitivity, and class accuracy. RESULTS: Sensitivity of 89.81% and specificity of 81.98% for meniscus lesion detection and sensitivity of 80.0% and specificity of 80.27% for cartilage were achieved. The best performances for staging lesion severity were obtained by including demographics factors, achieving accuracies of 80.74%, 78.02%, and 75.00% for normal, small, and complex large lesions, respectively. DATA CONCLUSION: In this study we provide a proof of concept of a fully automated deep-learning pipeline that can identify the presence of meniscal and patellar cartilage lesions. This pipeline has also shown potential in making more in-depth examinations of lesion subjects for multiclass prediction and severity staging. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:400-410.