See also article by Sveinsson et al in this issue. Paul H. Yi, MD, was a musculoskeletal radiology fellow at Johns Hopkins Hospital and is affiliate faculty at the Malone Center for Engineering in Healthcare. His research focuses on application and limitations of deep learning in radiology, including the potential for algorithmic bias. He serves on the RSNA Machine Learning Steering Subcommittee and the trainee editorial board of Radiology: Artificial Intelligence and is the journal's podcast co-host. In July 2021, Dr Yi joined the radiology faculty at the University of Maryland and serves as director of the University of Maryland Intelligent Medical Imaging Center.

To train convolutional neural network (CNN) models to classify benign and malignant soft-tissue masses at US and to differentiate three commonly observed benign masses. In this retrospective study, US images obtained between May 2010 and June 2019 from 419 patients (mean age, 52 years 18 [standard deviation]; 250 women) with histologic diagnosis confirmed at biopsy or surgical excision (n 227) or masses that demonstrated imaging characteristics of lipoma, benign peripheral nerve sheath tumor, and vascular malformation (n 192) were included. Images in patients with a histologic diagnosis (n 227) were used to train and evaluate a CNN model to distinguish malignant and benign lesions. Twenty percent of cases were withheld as a test dataset, and the remaining cases were used to train the model with a 75%-25% training-validation split and fourfold cross-validation. Performance of the model was compared with retrospective interpretation of the same dataset by two experienced musculoskeletal radiologists, blinded to clinical history.

To develop an automated model for staging knee osteoarthritis severity from radiographs and to compare its performance to that of musculoskeletal radiologists. Radiographs from the Osteoarthritis Initiative staged by a radiologist committee using the Kellgren-Lawrence (KL) system were used. Before using the images as input to a convolutional neural network model, they were standardized and augmented automatically. The model was trained with 32 116 images, tuned with 4074 images, evaluated with a 4090-image test set, and compared to two individual radiologists using a 50-image test subset. Saliency maps were generated to reveal features used by the model to determine KL grades.