Explore Altoida’s latest research advancing the early detection and precision diagnosis of Alzheimer’s disease and related neurodegenerative conditions. Presented at leading global conferences in 2025, these publications and posters highlight how multi-modal digital biomarkers powered by augmented reality and AI can deliver earlier, more sensitive and more scalable clinical insights.
By M. Florencia Iulita, PhD, Emmanuel Streel, PhD, John Harrison, PhD.
This paper outlines the limitations of traditional pen-and-paper cognitive assessments, which often lack the sensitivity to detect standard and clinically meaningful differences in Alzheimer’s disease, highlighting how digital health technologies overcome these limitations by enabling continuous monitoring, individualized baselines and longitudinal data capture. These innovations make it possible to track functional changes with greater precision throughout an individual’s care pathway.
By Jérôme Braudeau, PhD, Benoît Souchet, PhD, Emmanuel Streel, PhD.
This paper examines the limitations of traditional biomarkers including neuroinflammation, amyloid and tau, and explores how multiomics approaches that integrate cognitomics, genomics, proteomics, and metabolomics can offer a more comprehensive understanding of the biological and functional changes that drive Alzheimer’s development and progression. Digital biomarkers, enabled by technologies like augmented reality (AR) and machine learning (ML), have the capability to capture complex, real-time data at the earliest disease stages.
Presented at MDS 2025 by Emmanuel Streel, PhD.
In this ongoing study, 18 patients were studied, with data presented from 11. Despite low participant numbers, this data demonstrates the feasibility of using a machine learning-based digital cognitive assessment for sensitive detection of MCI in patients with Parkinson’s disease for the first time, with large scale studies required for further confirmation.
Presented at CTAD 2025 By Nicholas Griffin, PhD.
This poster, made in collaboration with partners at Quanterix and Johnson & Johnson Innovative Medicine, discusses the predictive ability of Altoida’s NeuroMarker platform in early detecting MCI and ptau217 concentration. Physiological changes associated with cognitive impairment such as with blood biomarkers are not complex enough to account for varied pathophysiology of the disease. Altoida’s platform is designed to address this gap by joining biological and behavioural effects for a more comprehensive view.
This poster will be released January 2026
Presented at AD/PD 2025 by Emmanuel Streel, PhD.
This poster presents the Altoida NeuroMarker Platform in a diverse cohort, demonstrating that AR-based digital biomarkers distinguish cognitively normal individuals from those with MCI and may further differentiate MCI subtypes, including those with underlying AD pathology. These findings highlight the platform’s potential for earlier, more precise and accessible detection of neurodegenerative diseases.
Each study reflects our commitment to advancing Alzheimer's research through the Altoida NeuroMarker Platform to help enable an early diagnosis. Our platform is designed to leverage digital biomarkers and machine learning to provide accurate, early detection of cognitive impairment.
