{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T10:52:32Z","timestamp":1787827952925,"version":"build-2784847793"},"reference-count":33,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100001003","name":"Boehringer Ingelheim","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001003","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["acrjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Arthritis Care &amp;amp; Research"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Lung ultrasound (LUS) is a sensitive, low\u2010cost, and radiation\u2010free modality for interstitial lung disease (ILD) detection. We previously developed and validated LUS interpretation criteria in systemic sclerosis (SSc) and idiopathic inflammatory myopathy (IIM) showing excellent diagnostic performance and correlations with ILD severity. In this study, we applied deep learning to evaluate whether convolutional neural networks (CNNs) can accurately detect ILD and its severity on LUS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Patients with SSc or IIM \u00b1 ILD and paired LUS and chest computed tomography (CT) were included. LUS images were labeled using CT results and human LUS\u2010ILD 2024 (LUS\u2010ILD\u201024) interpretation. Three pretrained CNN architectures (InceptionV3, ResNet\u201050, VGG\u201016) were fine\u2010tuned via transfer learning, and a de novo lightweight architecture (LUS\u2010Net) was developed. Model performance for ILD detection was assessed at image and patient levels using area under the curve (AUC), sensitivity, specificity, and agreement with expert interpretation. CNN outputs were correlated with pulmonary function tests (PFTs) and CT\u2010based Computer\u2010Aided Lung Informatics for Pathology Evaluation and Rating indices. Gradient\u2010weighted Class Activation Mapping (Grad\u2010CAM) visualized regions driving predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 140 patients representing 3,920 LUS images were included and split into development (74) and independent test sets (66). VGG\u201016 achieved the best patient\u2010level performance (AUC 0.972, sensitivity 97.4%, specificity 92.6%) showing strong correlations with PFTs and CT severity. Grad\u2010CAM highlighted pleural features as the primary regions influencing model predictions. CNN performance matched or exceeded LUS\u2010ILD\u201024 interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Deep learning applied to LUS enables accurate ILD detection in connective tissue disease and can enhance expert interpretation. Explainable artificial intelligence suggests pleural features, even when B\u2010lines are infrequent, are sufficient for reliable ILD recognition.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acr.80054","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:36:18Z","timestamp":1775471778000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence\u2013Aided Lung Ultrasound Detection of Interstitial Lung Disease in Systemic Sclerosis and Inflammatory Myopathy"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2352-3368","authenticated-orcid":false,"given":"Robert M.","family":"Fairchild","sequence":"first","affiliation":[{"name":"Stanford University School of Medicine  Stanford California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariani 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