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
Related News
•EurekAlert
AI Tool Predicts Financial Toxicity Risk in Cancer Patients
Researchers developed a machine learning model to proactively identify cancer patients at high risk of financial stress from treatment.
•EurekAlert
Researchers Develop All-Optical Synapse for Neuromorphic Imaging Systems
A new artificial synapse, controlled entirely by light, enables in-sensor neuromorphic processing for more efficient and noise-resistant imaging systems.
•EurekAlert
Mayo Clinic Showcases Imaging AI and Early Cancer Detection Advances at ASCO 2026
Mayo Clinic researchers will present over 30 studies at ASCO 2026, highlighting new advances in imaging AI, data science, and early cancer detection.