Toward Scalable Access to Neurodevelopmental Screening: Insights, Implementation, and Challenges

TL;DR

This work tackles the limited scalability of early neurodevelopmental screening by proposing NeuroNest, a mobile and cloud-based platform that fuses low-cost EEG data collection with standardized behavioral screening. The authors detail a three-subsystem architecture—wearables, a point-of-care app, and a cloud platform—supporting devices like Muse 2 and BIOPOT 3, live data streaming, HL7/FHIR integration, and open-source deployment. They demonstrate feasibility in wearable integration, app functionality, and cloud-based analyses while highlighting challenges in EEG data standardization, device interoperability, and bridging behavioral and physiological assessments. The contribution lies in enabling large-scale data collection and machine-learning-based screening through an extensible, open-source ecosystem, with potential for broad impact in low-resource and diverse-cultural settings.

Abstract

Children with neurodevelopmental disorders require timely intervention to improve long-term outcomes, yet early screening remains inaccessible in many regions. A scalable solution integrating standardized assessments with physiological data collection, such as electroencephalogram (EEG) recordings, could enable early detection in routine settings by non-specialists. To address this, we introduce NeuroNest, a mobile and cloud-based platform for large-scale EEG data collection, neurodevelopmental screening, and research. We provide a comprehensive review of existing behavioral and biomarker-based approaches, consumer-grade EEG devices, and emerging machine learning techniques. NeuroNest integrates low-cost EEG devices with digital screening tools, establishing a scalable, open-source infrastructure for non-invasive data collection, automated analysis, and interoperability across diverse hardware. Beyond the system architecture and reference implementation, we highlight key challenges in EEG data standardization, device interoperability, and bridging behavioral and physiological assessments. Our findings emphasize the need for future research on standardized data exchange, algorithm validation, and ecosystem development to expand screening accessibility. By providing an extensible, open-source system, NeuroNest advances machine learning-based early detection while fostering collaboration in screening technologies, clinical applications, and public health.

Figures (5)

• Figure 1: Illustration of a platform for neurodevelopmental data collection. The blue-shaded region highlights the key components of this research: a mobile application designed to collect individual patient data, including (1) patient identifiers and demographic information for the , (2) recordings from a locally connected Bluetooth device, and (3) data from additional sources such as standardized questionnaires. The seamlessly integrates with algorithmic and AI-based assessment and learning systems.
• Figure 2: Subsystem decomposition of a software architecture for a brain and behavioral data collection platform using wearables, a point-of-care application, and cloud infrastructure.
• Figure 3: Activity diagram describing the high-level interaction of a care provider with the NeuroNest application when collecting data for a given patient.
• Figure 4: Screenshots of the NeuroNest iOS application.
• Figure 5: Screenshots of the patient management functionalities of the NeuroNest iOS Application.