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Predicción del pronóstico de la esclerosis múltiple mediante IA y aprendizaje automático: integración de variables

Suhail Al-Shammri¹, Ahmet Özdil², Amro Aboukoura³

¹Department of Medicine, College of Medicine, Kuwait University, Safat, Kuwait.

Frontiers in neurology

|February 2, 2026

Resumen

Los modelos de aprendizaje automático predicen con precisión la progresión de la esclerosis múltiple (EM) utilizando perfiles de citoquinas. Estos modelos predicen la discapacidad y los cambios en las lesiones por resonancia magnética, lo que ayuda en el manejo clínico de la EM remitente-recurrente (EM-RR).

Sus antecedentes:

La predicción precisa de la progresión de la esclerosis múltiple (EM) es crucial para un manejo clínico eficaz.
La EM remitente-recurrente (EM-RR) requiere métodos confiables para monitorear el avance de la enfermedad y la discapacidad.
Los métodos existentes para predecir la progresión de la EM tienen limitaciones en precisión y puntualidad.

Conclusiones:

La combinación de perfiles de citoquinas con estrategias de ML proporciona predicciones precisas de la progresión funcional y radiológica en la EM-RR.
Estas herramientas predictivas pueden mejorar el monitoreo del paciente, la toma de decisiones terapéuticas y la estratificación del riesgo.
Se necesita una mayor validación en cohortes prospectivas para la implementación clínica de estos modelos predictivos basados en ML.

Palabras clave:

EDSS RM aprendizaje automático esclerosis múltiple predicción sensibilidad especificidad

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