The future is precision medicine-guided diagnoses, preventions and treatments for neurodegenerative diseases

Abstract

Recent advances in machine-learning algorithms and artificial intelligence (AI) have provided a much-needed framework for data mining and compilation that will lead to the development of the most accurate diagnostics and therapeutic approaches.AI-driven predictive analytics based on neurodegenerative diagnostic measures together with health status (co-morbidities), genetics, environmental exposures, and lifestyle factors would provide an adaptive and harmonized toolbox for healthcare providers to most effectively treat complex, multi-factorial diseases.These algorithms can also mine massive datasets to predict response to treatment, as well as the risk of disease progression.Significant hurdles to overcome include data harmonization and sharing, as well as inclusivity in clinical trials.Numerous initiatives have been undertaken to bridge inconsistencies in nomenclature and provide easily accessible datasets.Now standard to the field is the NIA-AA Research Framework that biologically defines and clinically stages Alzheimer's disease (Jack et al., 2018).This provided consistent reporting across studies to generate more meaningful information and outcomes.Similar undertakings have occurred in the sub-sectors of reserve and resilience (Stern et al., 2022), astrocyte (Escartin, 2022) and microglial (Paolicelli et al., 2022) nomenclature, and omics taxonomy in Alzheimer's disease (Iturria-Medina et al., 2022), to name a few.Data accessibility has been streamlined through disease data sharing initiatives like the Alzheimer's Disease Neuroimaging Initiative (ADNI) consortium of universities and medical centers in the United States and Canada (Petersen et al., 2010; Alzheimer's Disease Neuroimaging Initiative, 2023), and the National Eye Institute (NEI) Data Commons (NEI Data Commons, 2023), a virtual platform to enable sharing and accessing vision research data and tools for data processing and analysis.While these data repositories are incredibly useful to drive new research findings, a focus on future integration and compilation across resources is essential.A useful model is standardization and harmonization of data collection and protocols across Alzheimer's Disease Research Centers (ADRCs).In a similar data harmonization effort, the

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