Researchers developed a machine learning model that uses ultrasound and MRI data to predict cerebral blood flow in simulated microgravity for astronaut health.
Key Details
- 1Study used 36 healthy male participants in a 90-day head-down tilt bed rest (HDTBR) to simulate microgravity.
- 2Multimodal data collected included carotid Doppler ultrasound, 3D-pCASL brain MRI, and clinical metrics.
- 3Eight machine learning models tested; CatBoost delivered the best predictive performance (AUCs up to 0.92, accuracy up to 0.84).
- 4Model features were interpreted using SHAP, identifying BMI, ICA pulsatility index, and blood flow volume as top predictors.
- 5A web application was developed for real-time CBF prediction using clinical and ultrasound data, intended for spaceflight use.
- 6Limitations include exclusion of vertebral artery flow data and a male-only cohort.
Why It Matters
This ML-driven, imaging-integrated approach advances lightweight, non-invasive cerebral blood flow monitoring in space, potentially improving astronaut neurological care and illustrating cross-modal AI application in radiology. It also underscores the growing relevance of explainable AI and real-world deployment via web tools.
Source
EurekAlert
Related News
•EurekAlert
AI and Advanced Microscopy Unveil Cell's Exocytosis Nanomachine
Researchers have discovered the ExHOS nanomachine responsible for constitutive exocytosis using advanced microscopy and AI-enhanced image analysis.
•EurekAlert
Physical Activity Linked to Breast Tissue Biomarkers in Teens
A study links adolescent recreational physical activity to changes in breast tissue composition and stress biomarkers, potentially impacting future breast cancer risk.
•EurekAlert
AI Reveals Key Health System Levers for Cancer Outcomes Globally
AI-based analysis identifies the most impactful policy and resource factors for improving cancer survival across 185 countries.