Researchers developed BioCompNet, a dual-sequence AI system that automates body composition measurement from MRI scans for improved cardiometabolic risk management.
Key Details
- 1BioCompNet uses a dual-channel 2D U-Net framework to process fat- and water-sequence MRI for abdominal and thigh segmentation.
- 2It quantifies 15 key body composition components automatically, including muscle, bone, and various fat compartments.
- 3Tested on 503 subjects internally and 30 cases externally, it achieved mean Dice scores of 0.938 (abdomen) and 0.936 (thigh) with strong physician agreement (ICC 0.881–0.999).
- 4Automated processing takes 0.12 minutes per case, compared to 128.8 minutes manually, enabling large-scale studies.
- 5Limitations include the need for broader validation across populations, refinement of clinical integration, and multicenter studies for further generalizability.
Why It Matters
This workflow represents a significant advance in scalable, automated MRI quantitative analysis, potentially enabling precise risk assessment and management for cardiometabolic diseases. It reduces the labor required for body composition measurements, supporting both large-cohort research and future clinical deployment.
Source
EurekAlert
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