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
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