[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117049-en":3,"doc-seo-117049-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117049,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",7,"Healthcare","A Machine Learning System to Indicate Diagnosis of Idiopathic Pulmonary Fibrosis Non-Invasively in Challenging Cases","Radiologic usual interstitial pneumonia (UIP) patterns and concordant clinical findings are used to diagnose idiopathic pulmonary fibrosis (IPF), yet limited expert availability and substantial variability between clinicians hinder sensitive, early, pre-invasive differentiation from other interstitial lung diseases. A machine learning–driven software system, Fibresolve, was evaluated in a retrospective analysis of heterogeneous interstitial lung disease work-up data from 300 patients across two US sites. Fibresolve was applied at initial pre-invasive assessment and, compared with an Expert Clinical Panel and clinician panels, achieved higher sensitivity and significantly improved specificity, including a subgroup with thin-slice CT and atypical UIP patterns. The results support Fibresolve as a diagnostic adjunct alongside standard clinical assessment for pre-invasive IPF indication.","UCSF  \nUC San Francisco Previously Published Works  \nTitle  \nA Machine Learning System to Indicate Diagnosis of Idiopathic Pulmonary Fibrosis NonInvasively in Challenging Cases.  \nPermalink  \n[https://escholarship.org/uc/item/10q295vz](https://escholarship.org/uc/item/10q295vz)  \nJournal  \nDiagnostics, 14(8)  \nISSN  \n2075-4418  \nAuthors  \nAhmad, Yousef  \nMooney, Joshua Seaman, Julia et al.  \nPublication Date  \n2024-04-17  \nDOI  \n10.3390/diagnostics14080830  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n diagnostics  \nArticle  \nA Machine Learning System to Indicate Diagnosis of Idiopathic Pulmonary Fibrosis Non-Invasively in Challenging Cases  \nYousef Ahmad 1, *, Joshua Mooney 2, Isabel E. Allen 3, Julia Seaman 4, Angad Kalra 5, Michael Muelly 5 and Joshua Reicher 5, *  \nCitation: Ahmad, Y.; Mooney, J.; Allen, I.E.; Seaman, J.; Kalra, A.; Muelly, M.; Reicher, J. A Machine Learning System to Indicate Diagnosis of Idiopathic Pulmonary Fibrosis Non-Invasively in Challenging Cases. Diagnostics 2024, 14, 830. [https://](https://)[ ](https://)[doi.org/10.3390/diagnostics14080830](doi.org/10.3390/diagnostics14080830)  \nReceived: 19 March 2024  \nRevised: 11 April 2024  \nAccepted: 16 April 2024  \nPublished: 17 April 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Pulmonary and Critical Care, University of Cincinnati Medical Center, 231 Albert Sabin Way, ML 0564, Cincinnati, OH 45267-0564, USA  \n2 Stanford Health Care, Department of Pulmonary, Allergy, and Critical Care Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA  \n3 Department of Epidemiology & Biostatistics, University of California San Francisco, 550 16th Street, 2nd Floor, San Francisco, CA 94158-2549, USA  \n4 Bay View Analytics, 6924 Thornhill Dr, Oakland, CA 94611, USA; [julia@bayviewanalytics.com](julia@bayviewanalytics.com)  \n[5](5 IMVARIA)[ IMVARIA](5 IMVARIA), [2930 Domingo Ave \\#1496](2930 Domingo Ave #1496), [Berkeley](Berkeley), [CA 94705](CA 94705), [USA](USA)  \n* Correspondence: [ahmadyf@ucmail.uc.edu](ahmadyf@ucmail.uc.edu) (Y.A.); [jreicher@imvaria.com](jreicher@imvaria.com) (J.R.)  \nAbstract: Radiologic usual interstitial pneumonia (UIP) patterns and concordant clinical characteristics define a diagnosis of idiopathic pulmonary fibrosis (IPF) . However, limited expert access and high inter-clinician variability challenge early and pre-invasive diagnostic sensitivity and differentiation of IPF from other interstitial lung diseases (ILDs). We investigated a machine learning-driven software system, Fibresolve, to indicate IPF diagnosis in a heterogeneous group of 300 patients with interstitial lung disease work-up in a retrospective analysis of previously and prospectively collected registry data from two US clinical sites. Fibresolve analyzed cases at the initial pre-invasive assessment. An Expert Clinical Panel (ECP) and three panels of clinicians with varying experience analyzed the cases for comparison. Ground Truth was defined by separate multi-disciplinary discussion (MDD) with the benefit of surgical pathology results and follow-up. Fibresolve met both pre-specified co-primary endpoints of sensitivity superior to ECP and significantly greater specificity (p = 0.0007) than thenon-inferior boundary of 80.0% . In the key subgroup of cases with thin-slice CT and atypical UIP patterns (n = 124), Fibresolve’s diagnostic yield was 53.1%[CI: 41.3–64.9](versus 0% pre-invasive clinician diagnostic yield in this group), and its specificity was 85.9%[CI: 76.7–92.6%] . Overall, Fibresolve was found to increase the sensitivity and diagnostic yield for IPF among cas","cbCaiuIeVs19TVlX","https://ap.wps.com/l/cbCaiuIeVs19TVlX","pdf",1747813,1,15,"English","en",105,"# Abstract\n## Clinical Problem and Need\n## Method (Fibresolve and Study Design)\n## Results and Performance Metrics\n## Implications for Pre-Invasive Diagnosis","[{\"question\":\"What clinical challenge does Fibresolve address in IPF diagnosis?\",\"answer\":\"Limited expert access and high variability between clinicians reduce sensitivity and make it difficult to distinguish IPF from other interstitial lung diseases before invasive confirmation.\"},{\"question\":\"How was Fibresolve evaluated in this study?\",\"answer\":\"Fibresolve was tested on a heterogeneous cohort of 300 interstitial lung disease work-up cases using retrospective analysis of registry data from two US sites, with results compared against expert and clinician panels.\"},{\"question\":\"What were the main findings regarding diagnostic performance?\",\"answer\":\"Fibresolve met pre-specified endpoints with sensitivity superior to the expert panel and significantly greater specificity, and it increased diagnostic yield for IPF in the overall work-up setting and in an atypical UIP subgroup.\"}]","A Machine Learning System to Indicate Diagnosis of Idiopathic Pulmonary Fibrosis Non-Invasively in Challenging Cases | 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clinical challenge does Fibresolve address in IPF diagnosis?","Question",{"text":75,"@type":76},"Limited expert access and high variability between clinicians reduce sensitivity and make it difficult to distinguish IPF from other interstitial lung diseases before invasive confirmation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was Fibresolve evaluated in this study?",{"text":80,"@type":76},"Fibresolve was tested on a heterogeneous cohort of 300 interstitial lung disease work-up cases using retrospective analysis of registry data from two US sites, with results compared against expert and clinician panels.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the main findings regarding diagnostic performance?",{"text":84,"@type":76},"Fibresolve met pre-specified endpoints with sensitivity superior to the expert panel and significantly greater specificity, and it increased diagnostic yield for IPF in the overall work-up setting and in an atypical UIP 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