Magnetic resonance imaging (MRI) is a sensitive imaging modality for identifying knee bone marrow edema (BME), a significant biomarker in osteoarthritis and injury assessment. The precision of BME detection is contingent upon the radiologist's expertise, and segmentation efficiency demands substantial time.

This study evaluated artificial intelligence's (AI) impact on enhancing general radiologists' diagnostic accuracy for BME detection in knee MRI.

A multi center, multi-reader, multi-case methodology was used in this retrospective diagnostic study, which relied on an external dataset of 198 examinations. Mean age was 46 years with a standard deviation (SD) of 15.8 years and a female/ male ratio of 49 % / 51 %.

An AI algorithm from the AI Solution KEROS, comprising three orientation-specific 3D-UNet models, was deployed for BME segmentation on T2/PD-FATSAT sequences.

The ground truth was set by expert musculoskeletal radiologists.

The purpose was to externally validate the AI algorithm and compare the performance and speed of bone marrow edema identification by less experienced radiologists when using the algorithm versus not using it

A total of 184 patients were included. With AI, readers’ sensitivity for BME detection significantly increased by 6.1% from 79.3% without AI (95CI: 77.2–80.3%) to 85.4% (95CI: 84–86.2%) with AI (p = 0). Specificity significantly increased by 5% with AI assistance, reaching 93.9% (95CI: 93.7–94.6%) from 88.9% (95CI: 88.6–89.4%) (p = 0).. Reading times were reduced by 42% (0.66 minutes per exam, p=3.81e-41).

AI significantly increased the sensitivity and specificity of BME detection for general radiologists and shortened the reading process. AI-assisted detection of bone edema in the knee also opens up new perspectives for the longitudinal monitoring of patients with knee osteoarthritis