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Herramientas de análisis avanzado para imágenes de autofluorescencia de dos longitudes de onda de carotenoides de

Andreas Berlin^1,2, Lukas Goerdt^1,3, Mark E Clark¹

¹Ophthalmology and Visual Sciences, The University of Alabama at Birmingham, Heersink School of Medicine, Birmingham, AL, USA.

Translational vision science & technology

|August 21, 2025

Resumen

Las nuevas herramientas automatizadas centran y clasifican con precisión las distribuciones de densidad óptica de pigmento macular (MPOD). Estos avances ayudan a la investigación de los carotenoides xantofílicos, la salud de la visión y la progresión de la degeneración macular relacionada con la edad.

Área de la Ciencia:

Oftalmología
Ciencia de la visión
Imágenes médicas

Sus antecedentes:

La densidad óptica del pigmento macular (MPOD) es crucial para la visión y la comprensión de la degeneración macular relacionada con la edad (AMD).
La medición y clasificación precisas de la distribución de MPOD son esenciales para la investigación.
Los métodos actuales para el análisis de MPOD pueden ser intensivos en mano de obra y subjetivos.

Objetivo del estudio:

Desarrollar y validar herramientas automatizadas para centrar y clasificar los patrones de distribución de MPOD.
Permitir estudios a gran escala sobre el papel de los carotenoides xantofílicos en la visión y la progresión de la DMAE.

Principales métodos:

Utilizó imágenes de autofluorescencia de dos longitudes de onda en una cohorte de ojos sanos envejecidos y jóvenes.
Se han desarrollado complementos FIJI personalizados que emplean cinco algoritmos (HILLCLIMB, CONTOUR, FOVEA, CENTROID, MAX) para el centrado de MPOD.
Clasificación automatizada implementada de las distribuciones espaciales de MPOD en patrones de pico, anillo, mezclado y penetración.

Principales resultados:

Los algoritmos HILLCLIMB y CONTOUR demostraron el mejor acuerdo con la determinación manual del centro foveal.
En los ojos envejecidos, los patrones de pico fueron los más comunes (68,4%), seguidos por Ring (18,1%), Dip (7,2%) y Mixed (6,3%).
Los ojos jóvenes mostraron predominantemente patrones de pico (90%). Se observaron diferencias significativas en los patrones de MPOD basados en el género y el estado de la lente.

Conclusiones:

Las herramientas automatizadas de centralización y clasificación de MPOD son fiables y robustas.
El software desarrollado facilita el análisis a gran escala de la distribución de MPOD.
Los hallazgos sugieren que la distribución espacial de MPOD varía según el género, el estado de la lente y, potencialmente, la edad, lo que afecta a la investigación futura de la DMAE.

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