{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:05:53Z","timestamp":1785405953982,"version":"3.56.0"},"reference-count":32,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Distinguishing idiopathic pulmonary fibrosis (IPF) from other interstitial lung diseases is prognostically and therapeutically important. We developed Fibresolve, a machine learning system that analyzes chest computed tomography (CT) to support IPF diagnosis. Fibresolve version 1 (v1) was validated in the PUFAIR study, met its co-primary endpoints, and received U.S. FDA authorization. Fibresolve version 2 (v2) was subsequently developed under the FDA\u2019s Predetermined Change Control Plan (PCCP) framework and, here, is evaluated against the authorized v1 model. Methods: v2 augments the original CT-only v1 classifier by incorporating age, sex, and forced vital capacity into an ensemble, while retaining the v1 CT-only model as a fallback when these variables are unavailable. Both models were evaluated in the same 300-patient PUFAIR cohort (83 IPF, 217 non-IPF) from two U.S. centers. Reference-standard diagnoses were established by institutional multidisciplinary discussion and supported by surgical pathology in 94.7% of cases. McNemar test, Cohen \u03ba, and bootstrap noninferiority analyses were used for paired model comparisons. Results: In the key thin-slice diagnostic CT subgroup (N = 137), v2 achieved a sensitivity of 57.5% (95% CI, 42.2\u201371.5) and specificity of 84.5% (95% CI, 76.0\u201390.4), compared with 55.0% (95% CI, 39.8\u201369.3) and 82.5% (95% CI, 73.7\u201388.8) for v1. Performance improvements were directionally consistent across most subgroups, with no evidence of inferior performance. Conclusions: Fibresolve v2 preserved the performance of the FDA-authorized v1 model while providing modest, directionally favorable improvements. These findings support v2 as the primary model, with v1 retained as a robust fallback when clinical variables are unavailable.<\/jats:p>","DOI":"10.3390\/diagnostics16152398","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:32:13Z","timestamp":1785403933000},"page":"2398","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Paired Validation of a Multimodal Vision-Transformer System for the Non-Invasive Diagnosis of Idiopathic Pulmonary Fibrosis"],"prefix":"10.3390","volume":"16","author":[{"given":"Omid","family":"Tahamtani-Omran","sequence":"first","affiliation":[{"name":"Department of Chemistry and Biochemistry, University of California, Los Angeles, 607 Charles E. Young Drive East, Los Angeles, CA 90095, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angad","family":"Kalra","sequence":"additional","affiliation":[{"name":"IMVARIA Inc., 2930 Domingo Ave #1496, Berkeley, CA 94705, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Muelly","sequence":"additional","affiliation":[{"name":"IMVARIA Inc., 2930 Domingo Ave #1496, Berkeley, CA 94705, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8223-2264","authenticated-orcid":false,"given":"Joshua","family":"Reicher","sequence":"additional","affiliation":[{"name":"IMVARIA Inc., 2930 Domingo Ave #1496, Berkeley, CA 94705, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5547-3823","authenticated-orcid":false,"given":"Roaa","family":"Ahmed","sequence":"additional","affiliation":[{"name":"School of Medicine, Ahfad University for Women, P.O. Box 167, Omdurman 14411, Sudan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"ref_1","unstructured":"U.S. Food and Drug Administration (2026, June 09). Evaluation of Automatic Class III Designation for Fibresolve: De Novo Classification Request (DEN220040), Available online: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf22\/DEN220040.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107428","DOI":"10.1016\/j.rmed.2023.107428","article-title":"Development and validation of a CT-based deep learning algorithm to augment non-invasive diagnosis of idiopathic pulmonary fibrosis","volume":"219","author":"Maddali","year":"2023","journal-title":"Respir. Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ahmad, Y., Mooney, J., Allen, I.E., Seaman, J., Kalra, A., Muelly, M., and Reicher, J. (2024). 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