REALIDAD VIRTUAL Y BIOMARCADORES DIGITALES: UNA HERRAMIENTA CLÍNICA PARA EL DIAGNÓSTICO DEL AUTISMO.

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Bibliographic Details
Title:	REALIDAD VIRTUAL Y BIOMARCADORES DIGITALES: UNA HERRAMIENTA CLÍNICA PARA EL DIAGNÓSTICO DEL AUTISMO.
Alternate Title:	Virtual reality and digital biomarkers: a clinical tool for early autism diagnosis.
Authors:	MINISSI, MARIA ELEONORA¹, ALTOZANO, ALBERTO¹, MARIN-MORALES, JAVIER¹, CENTELLES, NEUS², SIRERA, MARIAN², ABAD, LUIS², ALCAÑIZ, MARIANO¹ malcaniz@i3b.upv.es
Source:	Medicina (Buenos Aires). 2024 Supplement, Vol. 84, p57-64. 8p.
Abstract (English):	Introduction: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition which traditional assessment procedures encounter certain limitations. The current ASD research ield is exploring and endorsing innovative methods to assess the disorder early on, based on the automatic detection of biomarkers. However, many of these procedures lack ecological validity in their measurements. In this context, virtual reality (VR) shows promise for objectively recording biosignals while users experience ecological situations. Methods: This study outlines a novel and playful VR procedure for the early assessment of ASD, relying on multimodal biosignal recording. During a VR experience featuring 12 virtual scenes, eye gaze, motor skills, electrodermal activity and behavioural performance were measured in 39 children with ASD and 42 control peers. Machine learning models were developed to identify digital biomarkers and classify autism. Results: Biosignals reported varied performance in detecting ASD, while the combined model resulting from the combination of speciic-biosignal models demonstrated the ability to identify ASD with an accuracy of 83% (SD = 3%) and an AUC of 0.91 (SD = 0.04). Discussion: This screening tool may support ASD diagnosis by reinforcing the outcomes of traditional assessment procedures. [ABSTRACT FROM AUTHOR]
Abstract (Spanish):	Introducción: El Trastorno del Espectro Autista (TEA) es un trastorno del neurodesarrollo, y sus procedimientos tradicionales de evaluación encuentran ciertas limitaciones. El actual campo de investigación sobre TEA está explorando y respaldando métodos innovadores para evaluar el trastorno tempranamente, basándose en la detección automática de biomarcadores. Sin embargo, muchos de estos procedimientos carecen de validez ecológica en sus mediciones. En este contexto, la realidad virtual (RV) presenta un prometedor potencial para registrar objetivamente bioseñales mientras los usuarios experimentan situaciones ecológicas. Métodos: Este estudio describe un novedoso y lúdico procedimiento de RV para la evaluación temprana del TEA, basado en la grabación multimodal de bioseñales. Durante una experiencia de RV con 12 escenas virtuales, se midieron la mirada, las habilidades motoras, la actividad electrodermal y el rendimiento conductual en 39 niños con TEA y 42 compañeros de control. Se desarrollaron modelos de aprendizaje automático para identificar biomarcadores digitales y clasificar el autismo. Resultados: Las bioseñales reportaron un rendimiento variado en la detección del TEA, mientras que el modelo resultante de la combinación de los modelos de las bioseñales demostró la capacidad de identiicar el TEA con una precisión del 83% (DE = 3%) y un AUC de 0.91 (DE = 0.04). Discusión: Esta herramienta de detección puede respaldar el diagnóstico del TEA al reforzar los resultados de los procedimientos tradicionales de evaluación. [ABSTRACT FROM AUTHOR]
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Database:	MedicLatina

Description
Abstract:	Introduction: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition which traditional assessment procedures encounter certain limitations. The current ASD research ield is exploring and endorsing innovative methods to assess the disorder early on, based on the automatic detection of biomarkers. However, many of these procedures lack ecological validity in their measurements. In this context, virtual reality (VR) shows promise for objectively recording biosignals while users experience ecological situations. Methods: This study outlines a novel and playful VR procedure for the early assessment of ASD, relying on multimodal biosignal recording. During a VR experience featuring 12 virtual scenes, eye gaze, motor skills, electrodermal activity and behavioural performance were measured in 39 children with ASD and 42 control peers. Machine learning models were developed to identify digital biomarkers and classify autism. Results: Biosignals reported varied performance in detecting ASD, while the combined model resulting from the combination of speciic-biosignal models demonstrated the ability to identify ASD with an accuracy of 83% (SD = 3%) and an AUC of 0.91 (SD = 0.04). Discussion: This screening tool may support ASD diagnosis by reinforcing the outcomes of traditional assessment procedures. [ABSTRACT FROM AUTHOR]
ISSN:	00257680