Deep Learning-Based System Detects Lesions in Knee MR Images

According to the study published in Radiology, the deep learning method was used to retrospectively analyze fat-suppressed T2-weighted fast spin-echo MRI data sets of the knee of 175 patients with knee pain. The reference standard for training the CNN classification was the interpretation provided by a fellowship-trained musculoskeletal radiologist of the presence or absence of a cartilage lesion within 17,395 small image patches placed on the articular surfaces of the femur and tibia.

In two individual evaluations of the system for the study, the researchers observed a sensitivity of 84.1% and a specificity of 85.2% for evaluation 1, as compared to 80.5% and 87.9%, respectively for evaluation 2. Areas under the ROC curve were 0.917 and 0.914 for evaluations 1 and 2, respectively, indicating high overall diagnostic accuracy for detecting cartilage lesions.

The researchers concluded that the study demonstrated the feasibility of using a fully automated deep learning–based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and that there was good intra-observer agreement for detecting cartilage degeneration and acute cartilage injury.

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University of Wisconsin School of Medicine and Public Health