Computer‐Aided Detection AI Reduces Interreader Variability in Grading Hip Abnormalities With MRI. Issue 4 (15th April 2020) - Tibrewala, Radhika; Ozhinsky, Eugene; Shah, Rutwik; Flament, Io; Crossley, Kay; Srinivasan, Ramya; Souza, Richard; Link, Thomas M.; Pedoia, Valentina; Majumdar, Sharmila
Abstract : Background: Accurate interpretation of hip MRI is time‐intensive and difficult, prone to inter‐ and intrareviewer variability, and lacks a universally accepted grading scale to evaluate morphological abnormalities. Purpose: To 1) develop and evaluate a deep‐learning‐based model for binary classification of hip osteoarthritis (OA) morphological abnormalities on MR images, and 2) develop an artificial intelligence (AI)‐based assist tool to find if using the model predictions improves interreader agreement in hip grading. Study Type: Retrospective study aimed to evaluate a technical development. Population: A total of 764 MRI volumes (364 patients) obtained from two studies (242 patients from LASEM [FORCe] and 122 patients from UCSF), split into a 65–25–10% train, validation, test set for network training. Field Strength/Sequence: 3T MRI, 2D T2 FSE, PD SPAIR. Assessment: Automatic binary classification of cartilage lesions, bone marrow edema‐like lesions, and subchondral cyst‐like lesions using the MRNet, interreader agreement before and after using network predictions. Statistical Tests: Receiver operating characteristic (ROC) curve, area under curve (AUC), specificity and sensitivity, and balanced accuracy. Results: For cartilage lesions, bone marrow edema‐like lesions and subchondral cyst‐like lesions the AUCs were: 0.80 (95% confidence interval [CI] 0.65, 0.95), 0.84 (95% CI 0.67, 1.00), and 0.77 (95% CI 0.66, 0.85), respectively. The sensitivity and specificity of the radiologist for binary classification were: 0.79 (95% CI 0.65, 0.93) and 0.80 (95% CI 0.59, 1.02), 0.40 (95% CI –0.02, 0.83) and 0.72 (95% CI 0.59, 0.86), 0.75 (95% CI 0.45, 1.05) and 0.88 (95% CI 0.77, 0.98). The interreader balanced accuracy increased from 53%, 71% and 56% to 60%, 73% and 68% after using the network predictions and saliency maps. Data Conclusion: We have shown that a deep‐learning approach achieved high performance in clinical classification tasks on hip MR images, and that using the predictions from the deep‐learning model improved the interreader agreement in all pathologies. Level of Evidence: 3 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;52:1163–1172.