A novel two-stage deep learning model used to assist in diagnosing neonatal necrotizing enterocolitis and determining the need for surgical treatment

A novel two-stage deep learning model used to assist in diagnosing neonatal necrotizing enterocolitis and determining the need for surgical treatment | 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 A novel two-stage deep learning model used to assist in diagnosing neonatal necrotizing enterocolitis and determining the need for surgical treatment Guoqiang Qi, Jian Ding, Jing Li, Mengyu Duan, Zhicong Liu, Shoujiang Huang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3424472/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 and Aims: Neonatal necrotizing enterocolitis (NEC) is a common life-threatening gastrointestinal disease in newborns. Abdominal X-rays (AXRs) is an important basis for diagnosing NEC and determining the need for surgical treatment. Computer-aided diagnosis (CAD) is extensively utilized in the clinical diagnosis of numerous diseases. Nevertheless, the efficacy of CAD for NEC has not been widely validated. Methods: We proposed for the first time a two-stage multimodal classification method for NEC based on AXRs data. The objective is to achieve early diagnosis of NEC and determine the optimal timing for surgical intervention. This method addresses the problem of insufficient labeled data through transfer learning and introduces coordinate attention to enhance the accuracy of target region localization and identification, thereby improving the capability of image feature extraction. Results: In total, the dataset was sourced from 2 children’s hospital consisted of 3,176 AXRs from 845 newborns diagnosed with NEC. Additionally, there were 1,825 AXRs from 470 newborns without NEC. The task for determining whether newborns has NEC achieved an accuracy of 97.49%, recall of 97.44%, precision of 83.09%, F1-score of 98.02% and AUC of 99.68%. Similarly, for the task of identifying if NEC patients require surgery, the accuracy, recall, precision, and F1-score were 78.96%, 81.50%, 80.30%, 80.89%, and 84.49% respectively. Our method performed better than the four commonly used baseline methods in the two-stage NEC diagnosis task. Conclusions: We have introduced a novel two-stage diagnostic model for NEC in newborns, which can rapidly and accurately identify NEC patients and determine if surgery is necessary. Neonatal Necrotizing Enterocolitis Surgical Intervention Medical Treatment Computer Aide Diagnosis Transfer Learning Full Text 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-3424472","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":242004804,"identity":"7c0bcf3f-989b-460a-b7be-e489aa841df9","order_by":0,"name":"Guoqiang 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Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neonatal Necrotizing Enterocolitis, Surgical Intervention, Medical Treatment, Computer Aide Diagnosis, Transfer Learning","lastPublishedDoi":"10.21203/rs.3.rs-3424472/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3424472/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims:\u003c/h2\u003e \u003cp\u003eNeonatal necrotizing enterocolitis (NEC) is a common life-threatening gastrointestinal disease in newborns. Abdominal X-rays (AXRs) is an important basis for diagnosing NEC and determining the need for surgical treatment. Computer-aided diagnosis (CAD) is extensively utilized in the clinical diagnosis of numerous diseases. Nevertheless, the efficacy of CAD for NEC has not been widely validated.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe proposed for the first time a two-stage multimodal classification method for NEC based on AXRs data. The objective is to achieve early diagnosis of NEC and determine the optimal timing for surgical intervention. This method addresses the problem of insufficient labeled data through transfer learning and introduces coordinate attention to enhance the accuracy of target region localization and identification, thereby improving the capability of image feature extraction.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eIn total, the dataset was sourced from 2 children\u0026rsquo;s hospital consisted of 3,176 AXRs from 845 newborns diagnosed with NEC. Additionally, there were 1,825 AXRs from 470 newborns without NEC. The task for determining whether newborns has NEC achieved an accuracy of 97.49%, recall of 97.44%, precision of 83.09%, F1-score of 98.02% and AUC of 99.68%. Similarly, for the task of identifying if NEC patients require surgery, the accuracy, recall, precision, and F1-score were 78.96%, 81.50%, 80.30%, 80.89%, and 84.49% respectively. Our method performed better than the four commonly used baseline methods in the two-stage NEC diagnosis task.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eWe have introduced a novel two-stage diagnostic model for NEC in newborns, which can rapidly and accurately identify NEC patients and determine if surgery is necessary.\u003c/p\u003e","manuscriptTitle":"A novel two-stage deep learning model used to assist in diagnosing neonatal necrotizing enterocolitis and determining the need for surgical treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-24 18:02:28","doi":"10.21203/rs.3.rs-3424472/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":"f121dea1-0fb4-4eb1-b549-435ea1e14e91","owner":[],"postedDate":"October 24th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-20T14:14:45+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-24 18:02:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3424472","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3424472","identity":"rs-3424472","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}