{"paper_id":"0beeea1b-fe4e-4d41-8326-4945d0d85ef4","body_text":"On the Improvement of Schizophrenia Detection with Optical Coherence Tomography Data Using Deep Neural Networks and Aggregation Functions | 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 On the Improvement of Schizophrenia Detection with Optical Coherence Tomography Data Using Deep Neural Networks and Aggregation Functions Paweł Karczmarek, Małgorzata Plechawska-Wójcik, Adam Kiersztyn, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4586579/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Schizophrenia is a serious mental disorder with a complex neurobiological background and a well-defined psychopathological picture. Despite many efforts, a definitive disease biomarker has still not been identified. One of the promising candidate for such biomarker might be the retinal morphology. In this study Optical Coherence Tomography (OCT) data is applied to assess the different layers of the retina. OCT data were applied in the process of automatic differentiation of schizophrenic patients and healthy controls. Numerical experiments covered applying several individual 1D Convolutional Neural Network based models as well as further using the aggregation of classification results to improve the initial classification results. The main goal of the study was to check how methods based on the aggregation of classification results work in classifying neuroanatomical features of schizophrenia. Among over 300, 000 different variants of tested aggregation operators, a few versions provided satisfactory results. Biological sciences/Computational biology and bioinformatics Biological sciences/Neuroscience/Computational neuroscience Biological sciences/Neuroscience/Diseases of the nervous system Biological sciences/Neuroscience/Visual system Health sciences/Diseases/Psychiatric disorders/Schizophrenia Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 28 Aug, 2024 Reviews received at journal 08 Aug, 2024 Reviewers agreed at journal 17 Jul, 2024 Reviews received at journal 03 Jul, 2024 Reviewers agreed at journal 03 Jul, 2024 Reviewers invited by journal 29 Jun, 2024 Editor assigned by journal 29 Jun, 2024 Editor invited by journal 19 Jun, 2024 Submission checks completed at journal 17 Jun, 2024 First submitted to journal 15 Jun, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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