An automated deep learning-based system can accurately evaluate knee joint cartilage to detect wear and injury, according to a recent Radiology study.

The research—led by Fang Liu, with the University of Wisconsin School of Medicine and Public Health—may also lead to reduced reader variability and improved patient care.

Fang and colleagues utilized segmentation and classification convolutional neural networks (CNNs) to train the automated deep learning cartilage lesion detection system.

To test the deep learning model, the team used retrospective data sets from 175 patient who underwent fat-suppressed T2-weighted fast spin-echo MRI. The reference standard for training the CNN classification was based on prior musculoskeletal radiology interpretation of the articular surfaces of the femur and tibia.