An Optimal Fusion Strategy for Automated Appendiceal Ultrasound Diagnosis and Reporting

An Optimal Fusion Strategy for Automated Appendiceal Ultrasound Diagnosis and Reporting | 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 Research Article An Optimal Fusion Strategy for Automated Appendiceal Ultrasound Diagnosis and Reporting Min Zhang, ian Li, Linyuan Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7527372/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Ultrasound diagnosis of appendicitis is challenged by operator-dependent variability, leading to inconsistent interpretation and reporting. Automated approaches leveraging deep learning have potential to improve diagnostic accuracy and standardize reporting but often lack integration of multiple relevant diagnostic features. Methods We developed the Full Model Selection and Fusion System (FMS), an automated framework that identifies nine critical diagnostic features from appendiceal ultrasound images. FMS integrates feature-specific deep learning models trained on 10,445 annotated images and uses an independent stratified validation set of 184 cases to optimally select and fuse the best-performing model snapshots for each feature. The system was evaluated on a test cohort of 3,214 pathologically confirmed cases collected from 2019 to 2023. Results FMS significantly reduced the rate of unacceptable reports—defined as discrepancies in four or more features compared to ground truth—to 16.6% (95% CI, 15.2–18.1%), versus 35.4% (95% CI, 33.4–37.5%) for conventional early stopping and 32.6% (95% CI, 30.6–34.6%) for non-selective fusion (P < 0.0001). This modular fusion strategy effectively mitigates operator variability and improves diagnostic consistency in appendiceal ultrasound imaging. Conclusions Our study demonstrates that a modular, feature-specific deep learning fusion framework can enhance the accuracy and consistency of automated appendiceal ultrasound diagnosis and reporting. FMS offers a scalable solution to overcome operator variability, facilitating reliable clinical decision support in complex ultrasound diagnostics. Biomedical Engineering Appendicitis Ultrasound Deep Learning Feature Fusion Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SUPPLEMENTARYMATERIAL.pdf Cite Share Download PDF Status: Posted Version 1 posted 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-7527372","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509721126,"identity":"8ac5f21a-2f3e-49dd-afd3-8efd08f954af","order_by":0,"name":"Min 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Reporting\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Changsha Central Hospital, University of South China","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Appendicitis, Ultrasound, Deep Learning, Feature Fusion","lastPublishedDoi":"10.21203/rs.3.rs-7527372/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7527372/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUltrasound diagnosis of appendicitis is challenged by operator-dependent variability, leading to inconsistent interpretation and reporting. Automated approaches leveraging deep learning have potential to improve diagnostic accuracy and standardize reporting but often lack integration of multiple relevant diagnostic features.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe developed the Full Model Selection and Fusion System (FMS), an automated framework that identifies nine critical diagnostic features from appendiceal ultrasound images. FMS integrates feature-specific deep learning models trained on 10,445 annotated images and uses an independent stratified validation set of 184 cases to optimally select and fuse the best-performing model snapshots for each feature. The system was evaluated on a test cohort of 3,214 pathologically confirmed cases collected from 2019 to 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFMS significantly reduced the rate of unacceptable reports—defined as discrepancies in four or more features compared to ground truth—to 16.6% (95% CI, 15.2–18.1%), versus 35.4% (95% CI, 33.4–37.5%) for conventional early stopping and 32.6% (95% CI, 30.6–34.6%) for non-selective fusion (P \u0026lt; 0.0001). This modular fusion strategy effectively mitigates operator variability and improves diagnostic consistency in appendiceal ultrasound imaging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study demonstrates that a modular, feature-specific deep learning fusion framework can enhance the accuracy and consistency of automated appendiceal ultrasound diagnosis and reporting. FMS offers a scalable solution to overcome operator variability, facilitating reliable clinical decision support in complex ultrasound diagnostics.\u003c/p\u003e","manuscriptTitle":"An Optimal Fusion Strategy for Automated Appendiceal Ultrasound Diagnosis and Reporting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 07:40:58","doi":"10.21203/rs.3.rs-7527372/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ccf02d2b-c958-4159-98bf-1fd6e7e341bc","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54139853,"name":"Biomedical Engineering"}],"tags":[],"updatedAt":"2025-09-04T07:40:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-04 07:40:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7527372","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7527372","identity":"rs-7527372","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}