{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T14:17:24Z","timestamp":1790605044268,"version":"4.1.0"},"reference-count":28,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"2","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Systemic sclerosis (SSc) is a relatively uncommon connective tissue disorder, commonly manifesting as interstitial lung disease (ILD) and affecting both the lung parenchyma and the modification of the space between endothelium and epithelium. Imaging modalities like computed tomography (CT) scans are essential for diagnosing and revealing specific abnormal findings (ILD patterns) in SSc, such as reticulation and Ground-glass opacity (GGO). To enhance diagnostic precision and minimize human error, we leverage deep learning (DL) techniques.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>In our study, we collected and annotated a new public dataset from 22 individuals, encompassing 2190 lung CT scan slices. After preprocessing and exclusion of slices without abnormalities, 1777 slices from 17 patients were used for model training and validation, and 413 slices from five patients were reserved for independent testing. We use a specialized U-net model to segment these patterns, categorizing them into reticulation or GGO, and employ an automated algorithm to outline lung areas in each CT slice. The model\u2019s objective is to quantify the patient\u2019s lung involvement in SSc by calculating the total identified GGO and reticulation areas across all slices and normalizing this by the total lung surface area.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The U-net model shows promising results in segmenting both reticulation and a combination of GGO and reticulation, as indicated by Dice coefficients of 87.22% and 86.20%, respectively. Furthermore, the automated algorithm effectively outlines the lung region in each slice, enabling accurate measurement of lung involvement in SSc patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>In conclusion, using DL using the U-Net model and an automated algorithm has shown promising results in accurately segmenting and quantifying lung involvement in Scleroderma patients using CT scans.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jrms.jrms_994_25","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:00:35Z","timestamp":1772121635000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["Automatic assessment of lung involvement in systemic sclerosis using deep learning"],"prefix":"10.4103","volume":"31","author":[{"given":"Matin","family":"Esnaashari","sequence":"first","affiliation":[{"name":"Al-Zahra Research Institute, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roya","family":"Arian","sequence":"additional","affiliation":[{"name":"Department of Engineering, Durham University, Durham, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Hajihashemi","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Narges","family":"Saeedizadeh","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Somayeh","family":"Hajiahmadi","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Somayeh","family":"Sadeghi","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Azin","family":"Shayganfar","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahele","family":"Kafieh","sequence":"additional","affiliation":[{"name":"Department of Engineering, Durham University, Durham, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,2]]},"reference":[{"key":"R1-20260226","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1183\/09059180.00009113","article-title":"Interstitial lung disease","volume":"23","author":"Antoniou","year":"2014","journal-title":"Eur Respir Rev"},{"key":"R2-20260226","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/S0140-6736(22)01692-0","article-title":"Systemic sclerosis","volume":"401","author":"Volkmann","year":"2023","journal-title":"Lancet"},{"key":"R3-20260226","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1038\/s41584-023-00909-5","article-title":"State-of-the-art evidence in the treatment of systemic sclerosis","volume":"19","author":"Pope","year":"2023","journal-title":"Nat Rev Rheumatol"},{"key":"R4-20260226","first-page":"961","article-title":"Systemic sclerosis\/scleroderma: A treatable multisystem disease","volume":"78","author":"Hinchcliff","year":"2008","journal-title":"Am Fam Physician"},{"key":"R5-20260226","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1136\/ard.2009.114264","article-title":"Causes and risk factors for death in systemic sclerosis: A study from the EULAR scleroderma trials and research (EUSTAR) database","volume":"69","author":"Tyndall","year":"2010","journal-title":"Ann Rheum Dis"},{"key":"R6-20260226","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1111\/j.1756-185X.2011.01625.x","article-title":"Prognostic factors of mortality and 2-year survival analysis of systemic sclerosis with pulmonary arterial hypertension in Thailand","volume":"14","author":"Foocharoen","year":"2011","journal-title":"Int J Rheum Dis"},{"key":"R7-20260226","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1007\/s00296-016-3475-6","article-title":"Survival and causes of death in systemic sclerosis patients: A single center registry report from Iran","volume":"36","author":"Poormoghim","year":"2016","journal-title":"Rheumatol Int"},{"key":"R8-20260226","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.3899\/jrheum.121380","article-title":"The 15% rule in scleroderma: The frequency of severe organ complications in systemic sclerosis. A systematic review","volume":"40","author":"Muangchan","year":"2013","journal-title":"J Rheumatol"},{"key":"R10-20260226","first-page":"101252","article-title":"Machine learning and neural network approaches for enhanced measuring and prediction of radiation doses","volume":"18","author":"Elhaie","year":"2025","journal-title":"J Radiat Res Appl Sci"},{"key":"R11-20260226","doi-asserted-by":"crossref","first-page":"e394","DOI":"10.5114\/pjr\/206986","article-title":"Machine learning-based classification of multiple sclerosis lesion activity using multi-sequence MRI radiomics: A complete analysis of T1, T2, FLAIR, DWI, and SWI features","volume":"90","author":"Elhaie","year":"2025","journal-title":"Pol J Radiol"},{"key":"R13-20260226","first-page":"S26","article-title":"A computer-aided diagnosis system for quantitative scoring of extent of lung fibrosis in scleroderma patients","volume":"28","author":"Kim","year":"2010","journal-title":"Clin Exp Rheumatol"},{"key":"R14-20260226","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1007\/s00330-011-2223-2","article-title":"Quantitative texture-based assessment of one-year changes in fibrotic reticular patterns on HRCT in scleroderma lung disease treated with oral cyclophosphamide","volume":"21","author":"Kim","year":"2011","journal-title":"Eur Radiol"},{"key":"R15-20260226","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1016\/j.acra.2007.03.009","article-title":"High resolution multidetector CT-aided tissue analysis and quantification of lung fibrosis","volume":"14","author":"Zavaletta","year":"2007","journal-title":"Acad Radiol"},{"key":"R16-20260226","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1016\/S2213-2600(18)30286-8","article-title":"Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: A case-cohort study","volume":"6","author":"Walsh","year":"2018","journal-title":"Lancet Respir Med"},{"key":"R17-20260226","first-page":"1","article-title":"Predicting neoadjuvant chemotherapy response in breast cancer using multiparametric MRI-Based radiomics and machine learning: A systematic review and meta-analysis of 1,469 patients","author":"Elhaie","year":"2025","journal-title":"Indian J Surg Oncol"},{"key":"R18-20260226","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s11748-022-01826-2","article-title":"Ground glass opacity: Can we correlate radiological and histological features to plan clinical decision making?","volume":"70","author":"Ram","year":"2022","journal-title":"Gen Thorac Cardiovasc Surg"},{"key":"R19-20260226","first-page":"200304655","article-title":"Lung infection quantification of COVID-19 in CT images with deep learning","author":"Shan","year":"2020","journal-title":"arXiv"},{"key":"R20-20260226","doi-asserted-by":"crossref","first-page":"22737","DOI":"10.1038\/s41598-023-49337-1","article-title":"COVID-19 infection segmentation using hybrid deep learning and image processing techniques","volume":"13","author":"Antar","year":"2023","journal-title":"Sci Rep"},{"key":"R21-20260226","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/s12880-021-00640-1","article-title":"Three-stage segmentation of lung region from CT images using deep neural networks","volume":"21","author":"Osadebey","year":"2021","journal-title":"BMC Med Imaging"},{"key":"R22-20260226","doi-asserted-by":"crossref","first-page":"100007","DOI":"10.1016\/j.cmpbup.2021.100007","article-title":"COVID TV-Unet: Segmenting COVID-19 chest CT images using connectivity imposed Unet","volume":"1","author":"Saeedizadeh","year":"2021","journal-title":"Comput Methods Programs Biomed Update"},{"key":"R23-20260226","first-page":"16","article-title":"Systemic sclerosis and cardiovascular risk: A systematic review of its association with atherosclerosis","volume":"3","author":"Ganieva","year":"2025","journal-title":"Web Discov J Anal Inventions"},{"key":"R24-20260226","doi-asserted-by":"crossref","first-page":"106039","DOI":"10.1016\/j.compbiomed.2022.106039","article-title":"A data augmentation method for fully automatic brain tumor segmentation","volume":"149","author":"Wang","year":"2022","journal-title":"Comput Biol Med"},{"key":"R28-20260226","article-title":"Term Project II: Image Processing Using Deep Learning","author":"Ray","year":"2023"},{"key":"R29-20260226","doi-asserted-by":"crossref","first-page":"1298","DOI":"10.3390\/jpm13091298","article-title":"Medical image segmentation using automatic optimized U-Net architecture based on genetic algorithm","volume":"13","author":"Khouy","year":"2023","journal-title":"J Pers Med"},{"key":"R30-20260226","first-page":"230906535","article-title":"Automatic quantification of abdominal subcutaneous and visceral adipose tissue in children, through MRI study, using total intensity maps and Convolutional Neural Networks","author":"Su\u00e1rez-Garc\u00eda","year":"2023","journal-title":"arXiv"},{"key":"R31-20260226","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.diin.2017.12.004","article-title":"Lempel-Ziv Jaccard distance, an effective alternative to ssdeep and sdhash","volume":"24","author":"Raff","year":"2018","journal-title":"Digit Investigation"},{"key":"R32-20260226","doi-asserted-by":"crossref","first-page":"2045432","DOI":"10.1155\/2019\/2045432","article-title":"Automatic lung segmentation based on texture and deep features of HRCT images with interstitial lung disease","volume":"2019","author":"Pang","year":"2019","journal-title":"Biomed Res Int"},{"key":"R34-20260226","first-page":"1","article-title":"MCAFNet: multiscale cross-layer attention fusion network for honeycomb lung lesion segmentation","author":"Li","year":"2023","journal-title":"Med Biol Eng Comput"}],"container-title":["Journal of Research in Medical Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/jrms.jrms_994_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T18:29:34Z","timestamp":1787768974000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/jrms.jrms_994_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.4103\/jrms.jrms_994_25","relation":{},"ISSN":["1735-1995","1735-7136"],"issn-type":[{"value":"1735-1995","type":"print"},{"value":"1735-7136","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"2025-09-08","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-14","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}