Can detection and prediction models for Alzheimer’s Disease be applied to Prodromal Parkinson’s Disease using explainable artificial intelligence? A brief report on Digital Neuro Signatures.

Abstract

Parkinson's disease (PD) is the fastest growing neurodegeneration and has a prediagnostic phase with a lot of challenges to identify clinical and laboratory biomarkers for those in the earliest stages or those 'at risk'. Despite the current research effort, further progress in this field hinges on the more effective application of digital biomarker and artificial intelligence applications at the prediagnostic stages of PD. It is of the highest importance to stratify such prediagnostic subjects that seem to have the most neuroprotective benefit from drugs. However, current initiatives to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials are still challenging due to the limited accuracy and explainability of existing prediagnostic detection and progression prediction solutions. In this brief paper, we report on a novel digital neuro signature (DNS) for prodromal-PD based on selected digital biomarkers previously discovered on preclinical Alzheimer's disease. (AD). Our preliminary results demonstrated a standard DNS signature for both preclinical AD and prodromal PD, containing a ranked selection of features. This novel DNS signature was rapidly repurposed out of 793 digital biomarker features and selected the top 20 digital biomarkers that are predictive and could detect both the biological signature of preclinical AD and the biological mechanism of a-synucleinopathy in prodromal PD. The resulting model can provide physicians with a pool of patients potentially eligible for therapy and comes along with information about the importance of the digital biomarkers that are predictive, based on SHapley Additive exPlanations (SHAP). Similar initiatives could clarify the stage before and around diagnosis, enabling the field to push into unchartered territory at the earliest stages of the disease.

Editorial: Advancements of deep learning in medical imaging for neurodegenerative diseases

“…Advances in medical imaging using deep learning have proven effective in diagnosing and managing neurodegenerative diseases like Alzheimer's, Parkinson's, and Multiple Sclerosis (Noor et al, 2019;Myszczynska et al, 2020;Gaur et al, 2023;Ghose et al, 2023;Xu et al, 2023). Deep learning excels in detecting subtle changes in brain structure and function, providing early detection, and significantly influencing patient prognosis and treatment efficacy (Tarnanas et al, 2022;Jyotismita and Marcin, 2023;Modat et al, 2023). This transformative synergy enhances our understanding of disease progression and marks a crucial advancement in neurodegenerative disease diagnosis and management (Breijyeh and Karaman, 2002;Buergel et al, 2022;Gaur et al, 2022a,b;Ghose et al, 2022).…”