Automated Computer Vision and Dose-Response Modeling Improve Throughput and Accuracy of an Ex Vivo Functional Precision Medicine Platform

Automated Computer Vision and Dose-Response Modeling Improve Throughput and Accuracy of an Ex Vivo Functional Precision Medicine Platform | 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 Automated Computer Vision and Dose-Response Modeling Improve Throughput and Accuracy of an Ex Vivo Functional Precision Medicine Platform Noah Bell, Andrew Buckley, Breanna Mann, Xiaopei Zhang, Adebimpe Adefolaju, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7303402/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Functional Precision Medicine platforms, which investigate the dynamic behavior of a patient’s tumor ex vivo to inform personalized treatment, face unique obstacles to clinical translation. These include limited access to patient tissue and stringent demands for intra-platform accuracy and consistency. In this study, an automated data analysis pipeline addresses these concerns for an organotypic brain slice culture-based functional assay by combining computer vision and dose-response modeling approaches. A 99% reduction in analysis time increases the amount of patient tissue that can be processed on the platform. Comparing automated measurements to previously published manual results revealed that automation increased consistency both within experiments and across replicate experiments. This pipeline also explores implementing complex CV with limited resources, modeling a unique and diverse dataset, and validating automated analysis when no gold standard measurements exist, obstacles that hinder automation efforts across scientific disciplines. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Medical research Health sciences/Oncology Computer vision automation machine learning functional precision medicine patient-derived Full Text Additional Declarations Competing interest reported. N.B., A.B., B.M., A.A., R. Dasari, X.Z., S.H., and A.B.S. have submitted a patent application based on the OBSC platform. All other authors declare no competing interest. Supplementary Files BelletalSupplementalTables.pdf SupplementalFigures.docx BelletalSupplementaryMethods.docx Cite Share Download PDF Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 Sep, 2025 Reviews received at journal 15 Sep, 2025 Reviews received at journal 15 Sep, 2025 Reviews received at journal 11 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers agreed at journal 02 Sep, 2025 Reviewers agreed at journal 02 Sep, 2025 Reviewers agreed at journal 02 Sep, 2025 Reviewers invited by journal 02 Sep, 2025 Editor assigned by journal 26 Aug, 2025 Editor invited by journal 25 Aug, 2025 Submission checks completed at journal 20 Aug, 2025 First submitted to journal 20 Aug, 2025 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-7303402","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":510517126,"identity":"7683259e-61a8-4bf2-8018-722fcd835952","order_by":0,"name":"Noah Bell","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Noah","middleName":"","lastName":"Bell","suffix":""},{"id":510517127,"identity":"7fb32765-64ec-4d26-9297-025458f40c8b","order_by":1,"name":"Andrew Buckley","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Buckley","suffix":""},{"id":510517129,"identity":"6a4d1434-4a57-4bbd-afc4-4bfc43f54dc9","order_by":2,"name":"Breanna Mann","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Breanna","middleName":"","lastName":"Mann","suffix":""},{"id":510517130,"identity":"260e1817-f92c-4ea8-adf9-4382c8e31e6d","order_by":3,"name":"Xiaopei Zhang","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Xiaopei","middleName":"","lastName":"Zhang","suffix":""},{"id":510517131,"identity":"89a01631-d5a2-4930-8cb6-41b6d021e078","order_by":4,"name":"Adebimpe Adefolaju","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Adebimpe","middleName":"","lastName":"Adefolaju","suffix":""},{"id":510517135,"identity":"c703d0b5-787f-451c-adda-ee21bc22b3e0","order_by":5,"name":"Rajaneekar Dasari","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Rajaneekar","middleName":"","lastName":"Dasari","suffix":""},{"id":510517139,"identity":"8422c829-5816-41dd-b99b-2e42fff1c62a","order_by":6,"name":"Rami Darwasheh","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Rami","middleName":"","lastName":"Darwasheh","suffix":""},{"id":510517144,"identity":"23276779-a97e-4a64-b5d5-24d2826518b1","order_by":7,"name":"David E. 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