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Aprendizaje automático explicable para la predicción del resultado del coma basado en RM estructural y funcional del

Benjamine Sarton1,2, Giulia Maria Mattia2, Eve Cervoni1,2

  • 1Critical Care Unit, University Teaching Hospital of Purpan, Toulouse, France.

Critical care medicine
|February 27, 2026
PubMed
Resumen

Los modelos de aprendizaje automático que analizan datos avanzados de RM pueden predecir con precisión el diagnóstico del coma, el tipo de lesión cerebral y la recuperación del paciente. Estos modelos identifican métricas clave de la red cerebral para evaluar el coma y predecir los resultados.

Palabras clave:
comainteligencia artificial explicableaprendizaje automáticomesocircuitoimagen por resonancia magnética multimodalpronóstico

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Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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