3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects

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.