Comprehensive Summary
In this study, Maarek et al., evaluated whether artificial intelligence (AI) could improve radiologists’ detection of bone marrow edema (BME) on knee MRI’s. As a retroactive diagnostic study, researchers evaluated 198 examinations. Using an AI algorithm that was based on three orientation specific 3D-UNet models (Keros), the performance of radiologists was measured and compared with and without AI assistance. The results showed that AI support for the radiologists improved sensitivity by ~6% and specificity ~4% for BME detection while also reducing average reading time by 42%. These findings demonstrate that AI can both enhance diagnostic accuracy and increase efficiency in musculoskeletal MRI interpretation.
Outcomes and Implications
MRI is a sensitive imaging tool that can allow for early detection of diseases and conditions that could be life threatening. The integration of AI into the field of radiology can potentially elevate and standardize diagnostic quality. In the case of an MRI, this could then translate into more reliable detection of osteoarthritis related changes which consequently can improve treatment planning and long term prognosis. AI could also reduce the workload on specific departments while also increasing the output and making it more efficient. AI could play a central role in high quality diagnostic imaging as well as narrowing the gap between differences in performance based on experience. Broader adoption will require continued observation and validation in clinical settings and careful consideration of how AI systems are integrated into diagnostic practices.