AI-Based Ovarian Phenotyping Using Follicle Size Distribution Patterns for PCOS Assessment

AI-Based Ovarian Phenotyping Using Follicle Size Distribution Patterns for PCOS Assessment | 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 AI-Based Ovarian Phenotyping Using Follicle Size Distribution Patterns for PCOS Assessment Mehtap Agirsoy, Matthew A. Oehlschlaeger This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8534482/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: Polycystic Ovary Syndrome (PCOS) is commonly diagnosed using ultrasound-based follicle counts and size thresholds. However, manual assessment is subjective, time-consuming, and limited to coarse criteria that overlook the broader morphological patterns of follicular development. In particular, follicle size distribution, rather than absolute count alone, remains underutilized as a diagnostic marker. Methods: We present an artificial intelligence–based ovarian phenotyping approach that automatically quantifies follicle size and shape distributions from transvaginal ultrasound images. An AI-driven object detection model was used solely as a measurement engine to extract follicle geometry, enabling downstream analysis of diameter distributions and aspect ratio patterns. Importantly, the proposed methodology operates in a resolution-independent manner, allowing robust morphological analysis even when spatial calibration metadata are unavailable. Results: Across 302 ultrasound images, the derived follicle diameter distributions revealed distinct multimodal patterns, characterized by an over-representation of small antral follicles and secondary size populations consistent with disrupted folliculogenesis in PCOS. Aspect ratio analysis further identified deviations from circular morphology in a subset of follicles. Clustering based on distributional features demonstrated that 67% of cases exhibited high-density, small-follicle dominance, a hallmark of polycystic ovarian morphology. Conclusion: This study introduces AI-based follicle size distribution phenotyping as a complementary and scalable imaging marker for PCOS assessment. By shifting focus from follicle counting to distribution-level morphological signatures, the proposed approach offers a reproducible, operator-independent pathway toward more nuanced ovarian characterization. The framework is designed to be dataset-agnostic and readily extensible to future clinically curated cohorts. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers invited by journal 03 Apr, 2026 Editor assigned by journal 03 Apr, 2026 Editor invited by journal 13 Jan, 2026 Submission checks completed at journal 10 Jan, 2026 First submitted to journal 10 Jan, 2026 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8534482","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":617017000,"identity":"6a32568e-a943-4674-93d1-0a3c8fd80618","order_by":0,"name":"Mehtap Agirsoy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYFACHgjFxsB8AEhJyBCjhbEBooUtAaSFh3gtQIYBkqV4gG577/EHP3fY2fVJn/n86kaNBQ8D++GjG/BpMTtzLrGx90xychtf7jbrnGNAh/Gkpd3Aq+VGjmEDbxtzMhsP7zbjHDagFgkeM/xa7r8xbPzbVg/UwvPMOOcfMVpu8Bg287YdtgNqYX6c20aMljM5hrNlzxxPYONhM2PO7ZPgYSPol+NnDD6+3VFtL9/D/Phzzrc6OX72w8fwagEDYMwkNgBjUwLEYSOoHKrFHkgxfyBK9SgYBaNgFIw4AAC1f0WgpzrXjAAAAABJRU5ErkJggg==","orcid":"","institution":"Rensselaer Polytechnic Institute","correspondingAuthor":true,"prefix":"","firstName":"Mehtap","middleName":"","lastName":"Agirsoy","suffix":""},{"id":617017001,"identity":"a749d49f-438f-453d-9e55-79781f70bff7","order_by":1,"name":"Matthew A. 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However, manual assessment is subjective, time-consuming, and limited to coarse criteria that overlook the broader morphological patterns of follicular development. In particular, follicle size distribution, rather than absolute count alone, remains underutilized as a diagnostic marker.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe present an artificial intelligence\u0026ndash;based ovarian phenotyping approach that automatically quantifies follicle size and shape distributions from transvaginal ultrasound images. An AI-driven object detection model was used solely as a measurement engine to extract follicle geometry, enabling downstream analysis of diameter distributions and aspect ratio patterns. Importantly, the proposed methodology operates in a resolution-independent manner, allowing robust morphological analysis even when spatial calibration metadata are unavailable.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eAcross 302 ultrasound images, the derived follicle diameter distributions revealed distinct multimodal patterns, characterized by an over-representation of small antral follicles and secondary size populations consistent with disrupted folliculogenesis in PCOS. Aspect ratio analysis further identified deviations from circular morphology in a subset of follicles. Clustering based on distributional features demonstrated that 67% of cases exhibited high-density, small-follicle dominance, a hallmark of polycystic ovarian morphology.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThis study introduces AI-based follicle size distribution phenotyping as a complementary and scalable imaging marker for PCOS assessment. By shifting focus from follicle counting to distribution-level morphological signatures, the proposed approach offers a reproducible, operator-independent pathway toward more nuanced ovarian characterization. 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