External COVID-19 Deep Learning Model Validation on ACR AI-LAB: It's a Brave New World.

Purpose: Deploying external artificial intelligence (AI) models locally can be logistically challenging. We aimed to use the ACR AI-LAB software platform for local testing of a chest radiograph (CXR) algorithm for COVID-19 lung disease severity assessment.

Methods: An externally developed deep learning model for COVID-19 radiographic lung disease severity assessment was loaded into the AI-LAB platform at an independent academic medical center, which was separate from the institution in which the model was trained.

Current applications and future directions of deep learning in musculoskeletal radiology.

Deep learning with convolutional neural networks (CNN) is a rapidly advancing subset of artificial intelligence that is ideally suited to solving image-based problems. There are an increasing number of musculoskeletal applications of deep learning, which can be conceptually divided into the categories of lesion detection, classification, segmentation, and non-interpretive tasks. Numerous examples of deep learning achieving expert-level performance in specific tasks in all four categories have been demonstrated in the past few years, although comprehensive interpretation of imaging examinations has not yet been achieved.

Diagnostic Features of Emphysematous Osteomyelitis.

Emphysematous osteomyelitis (EO) is a rare, aggressive, and potentially fatal variant of osteomyelitis related to gas-forming organisms. Imaging plays a vital role in diagnosis. The purpose of this study was to describe a novel and distinct imaging sign of EO, by analysis of the imaging characteristics of 3 newly identified cases of EO as well as all reported cases in the literature.

Barraquer-Simons Syndrome.

Background: Barraquer-Simons syndrome (BSS) is a rare acquired lipodystrophy characterized by gradually symmetric subcutaneous fat loss in a craniocaudal distribution, associated with hypocomplementemia, diabetes and hypertriglyceridemia. Few investigators have studied body fat distribution with cross-sectional imaging techniques.

Methods: We present 2 cases of BSS with emphasis on phenotypic analysis through cross-sectional imaging.