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Optimización y validación externa de un modelo de aprendizaje profundo para la segmentación y cuantificación de

Francois Mathieu1,2, Carolina Piçarra3, Armaan Malhotra1,4,5

  • 1Department of Surgery, Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada.

Journal of neurotrauma
|February 18, 2026
PubMed
Resumen

Este estudio optimizó la Herramienta de Análisis y Segmentación de Lesiones Cerebrales para Tomografía Computarizada (BLAST-CT) utilizando más datos, mejorando la segmentación automatizada de lesiones por traumatismo craneoencefálico (TCE). El BLAST-CT 2.0 mejorado demuestra una mayor precisión y generalización para el análisis de TCE.

Palabras clave:
visión por computadoravalidación externaaprendizaje automáticoneuroimagentraumatismo craneoencefálico

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