Explainability of CNN-based Alzheimer’s Disease Detection from Online Handwriting

Explainability of CNN-based Alzheimer’s Disease Detection from Online Handwriting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Explainability of CNN-based Alzheimer’s Disease Detection from Online Handwriting Jana Sweidan, Mounim A. El-Yacoubi, Anne-Sophie Rigaud This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4215056/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer’s disease is a prevalent and challenging neurodegenerative disorder. Despite significant advancements in machine learning techniques for Alzheimer’s disease detection, the widespread adoption of deep learning models raises concerns about their explainability. The lack of explainability in deep learning models for online handwriting analysis is a critical gap in the literature in the context of Alzheimer’s disease detection. This paper addresses this challenge by interpreting predictions from a Convolutional Neural Network applied to multivariate time series data, generated by online handwriting data associated with continuous loop series handwritten on a graphical tablet. Our explainability methods reveal distinct motor behavior characteristics for healthy individuals and those diagnosed with Alzheimer’s. This emphasizes the critical role of explainability in translating complex models into clinically relevant insights. Our research contributes to the enhancement of early diagnosis, providing significant and reliable insights to stakeholders involved in patient care and intervention strategies. Our work bridges the gap between machine learning predictions and clinical insights, fostering a more effective and understandable application of advanced models for Alzheimer’s disease assessment. Alzheimer’s Disease Online handwriting 1D-CNN Explainability Full Text Additional Declarations No competing interests reported. Supplementary Files SRJanaXAIforHWAD5thSupplementary.pdf Cite Share Download PDF Status: Published Journal Publication published 27 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 22 May, 2024 Reviews received at journal 17 May, 2024 Reviews received at journal 14 May, 2024 Reviews received at journal 05 May, 2024 Reviews received at journal 04 May, 2024 Reviewers agreed at journal 29 Apr, 2024 Reviewers agreed at journal 29 Apr, 2024 Reviewers agreed at journal 29 Apr, 2024 Reviewers agreed at journal 29 Apr, 2024 Reviewers invited by journal 28 Apr, 2024 Editor assigned by journal 25 Apr, 2024 Editor invited by journal 13 Apr, 2024 Submission checks completed at journal 13 Apr, 2024 First submitted to journal 03 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4215056","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":292065057,"identity":"70f77d57-c197-43a1-829c-4593598d888e","order_by":0,"name":"Jana Sweidan","email":"","orcid":"","institution":"Institut Polytechnique de Paris","correspondingAuthor":false,"prefix":"","firstName":"Jana","middleName":"","lastName":"Sweidan","suffix":""},{"id":292065058,"identity":"62b513d9-f2b1-479d-9a89-52874e889873","order_by":1,"name":"Mounim A. 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