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Pipeline de aprendizaje profundo automatizado para la cuantificación del ángulo de callosidad
Siavash Shirzadeh Barough1, Murat Bilgel2, Catalina Ventura1
1Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
medRxiv : the preprint server for health sciences
|September 2, 2025
Resumen
Un nuevo marco de aprendizaje profundo automatiza la medición del ángulo calloso (CA) de las imágenes de resonancia magnética, mejorando el diagnóstico de la hidrocefalia de presión normal (NPH). Este método fiable mejora la detección temprana y la evaluación clínica de la NPH.
Área de la Ciencia:
- Imágenes neurológicas
- La inteligencia artificial en la medicina
- Enfermedades neurodegenerativas
Sus antecedentes:
- La hidrocefalia de presión normal (NPH) es una condición subdiagnosticada debido a la superposición de síntomas y al análisis manual de biomarcadores de imágenes que consume mucho tiempo.
- Los marcadores diagnósticos clave como el ángulo calloso (AC) a menudo están sujetos a variabilidad de interpretación.
Objetivo del estudio:
- Desarrollar un marco de aprendizaje profundo totalmente automatizado para la medición precisa del ángulo calloso (AC) a partir de exploraciones de resonancia magnética.
- Proporcionar una alternativa robusta y reproducible a las mediciones manuales de CA para el diagnóstico de la HPN.
Principales métodos:
- El marco integra BrainSignsNET para la detección de puntos de referencia (AC, PC) y una red basada en UNet para la segmentación lateral de los ventrículos.
- Las imágenes de resonancia magnética se procesan previamente y se analizan utilizando una rebanada coronal perpendicular a la línea AC-PC para el cálculo de la CA.
Principales resultados:
- El marco automatizado demostró una alta concordancia con las mediciones manuales (r = 0,98, p < 0,001) y un error medio absoluto (MAE) bajo de 2,95 grados.
- El rendimiento fue consistente en diversos grupos demográficos de pacientes e independiente del Índice de Evans (IE).
Conclusiones:
- El marco automatizado de medición de la CA ofrece una alternativa fiable y reproducible a los métodos manuales.
- Esta herramienta tiene un potencial significativo para mejorar la detección y el diagnóstico tempranos de la NPH en entornos de investigación y clínicos.
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