{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:49:29Z","timestamp":1791395369275,"version":"4.3.3"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":84,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"IITP","award":["RS-2024-00337489"],"award-info":[{"award-number":["RS-2024-00337489"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-58980-3","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T12:16:16Z","timestamp":1782130576000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A multimodal deep learning framework for precise silicosis detection on radiographic images"],"prefix":"10.1038","volume":"16","author":[{"given":"Ramesh N. S. V. S. C.","family":"Sripada","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pothuri Surendra","family":"Varma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E. Laxmi","family":"Lydia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Radhika","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jang Mook","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5446-288X","authenticated-orcid":false,"given":"Gyanendra Prasad","family":"Joshi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheolhee","family":"Yoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"issue":"1","key":"58980_CR1","first-page":"3769134","volume":"2022","author":"T Li","year":"2022","unstructured":"Li, T., Yang, X., Xu, H. & Liu, H. Early identification, accurate diagnosis, and treatment of silicosis. Can. Respir. J. 2022 (1), 3769134 (2022).","journal-title":"Can. Respir. J."},{"key":"58980_CR2","doi-asserted-by":"crossref","unstructured":"Zhang, L. et al. A deep learning-based model for screening and staging pneumoconiosis. Sci. Rep. 11 (1), 2201 (2021).","DOI":"10.1038\/s41598-020-77924-z"},{"key":"58980_CR3","doi-asserted-by":"publisher","first-page":"104607","DOI":"10.1016\/j.bspc.2023.104607","volume":"83","author":"Y Wang","year":"2023","unstructured":"Wang, Y. et al. Automated identification of the preclinical stage of coal workers\u2019 pneumoconiosis from digital chest radiography using three-stage cascaded deep learning model. Biomed. Signal Process. Control. 83, 104607 (2023).","journal-title":"Biomed. Signal Process. Control"},{"issue":"10","key":"58980_CR4","first-page":"842","volume":"34","author":"LI Xiao","year":"2021","unstructured":"Xiao, L. I. et al. Deep learning in chest radiography: detection of pneumoconiosis. Biomed. Environ. Sci. 34 (10), 842\u2013845 (2021).","journal-title":"Biomed. Environ. Sci."},{"key":"58980_CR5","doi-asserted-by":"crossref","unstructured":"Wang, Z. et al. Deep learning for computer-aided diagnosis of pneumoconiosis. (2021).","DOI":"10.21203\/rs.3.rs-460896\/v1"},{"issue":"1","key":"58980_CR6","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s12995-023-00379-1","volume":"18","author":"MP Rupani","year":"2023","unstructured":"Rupani, M. P. Challenges and opportunities for silicosis prevention and control: need for a national health program on silicosis in India. J. Occup. Med. Toxicol. 18 (1), 11 (2023).","journal-title":"J. Occup. Med. Toxicol."},{"key":"58980_CR7","doi-asserted-by":"crossref","unstructured":"Shivaanivarsha, N., Kavipriya, P. & Vigita, S. October. A deep transfer learning approach for the detection of silicosis using chest radiography. In National Conference on Control Instrumentation System Conference (pp. 575\u2013589). (Singapore: Springer Nature Singapore, 2023).","DOI":"10.1007\/978-981-97-5866-1_41"},{"issue":"1","key":"58980_CR8","doi-asserted-by":"publisher","first-page":"58","DOI":"10.5334\/aogh.3206","volume":"87","author":"JM Spiegel","year":"2021","unstructured":"Spiegel, J. M. et al. Using artificial intelligence for high-volume identification of silicosis and tuberculosis: A bio-ethics approach. Ann. Global Health. 87 (1), 58 (2021).","journal-title":"Ann. Global Health"},{"issue":"19","key":"58980_CR9","doi-asserted-by":"publisher","first-page":"12402","DOI":"10.3390\/ijerph191912402","volume":"19","author":"R Ehrlich","year":"2022","unstructured":"Ehrlich, R. et al. Accuracy of computer-aided detection of occupational lung disease: silicosis and pulmonary tuberculosis in ex-miners from the South African gold mines. Int. J. Environ. Res. Public Health. 19 (19), 12402 (2022).","journal-title":"Int. J. Environ. Res. Public Health"},{"issue":"17","key":"58980_CR10","doi-asserted-by":"publisher","first-page":"9091","DOI":"10.3390\/ijerph18179091","volume":"18","author":"C Hao","year":"2021","unstructured":"Hao, C. et al. Balanced convolutional neural networks for pneumoconiosis detection. Int. J. Environ. Res. Public Health. 18 (17), 9091 (2021).","journal-title":"Int. J. Environ. Res. Public Health"},{"issue":"1","key":"58980_CR11","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1186\/s12911-022-02021-2","volume":"22","author":"AD de Lima","year":"2022","unstructured":"de Lima, A. D., Lopes, A. J., do Amaral, J. L. M. & de Melo, P. L. Explainable machine learning methods and respiratory oscillometry for the diagnosis of respiratory abnormalities in sarcoidosis. BMC Med. Inf. Decis. Mak. 22 (1), 274 (2022).","journal-title":"BMC Med. Inf. Decis. Mak."},{"issue":"1","key":"58980_CR12","first-page":"2037019","volume":"2022","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Hu, M., Zeng, M. & Wang, G. Intelligent image diagnosis of pneumoconiosis based on wavelet transform-derived texture features. Comput. Math. Methods Med. 2022 (1), 2037019 (2022).","journal-title":"Comput. Math. Methods Med."},{"key":"58980_CR13","unstructured":"Bui, B. Q., Nguyen, T. T., Le, D. M., Tran, C. & Pham, C. Innovative silicosis and pneumonia classification: leveraging graph transformer post-hoc modeling and ensemble techniques. arXiv preprint arXiv:2501.00520. (2024)."},{"key":"58980_CR14","doi-asserted-by":"crossref","unstructured":"Sharma, G. K., Harjule, P., Agarwal, B. & Kumar, R. December. Silicosis detection using extended transfer learning model. In International Conference on Recent Trends in Image Processing and Pattern Recognition, pp. 111\u2013126. (Cham: Springer Nature Switzerland, 2023).","DOI":"10.1007\/978-3-031-53085-2_10"},{"key":"58980_CR15","doi-asserted-by":"crossref","unstructured":"Xuan, W., Han, Z. & Zheng, L. Noninvasive detection of silicosis based on array sensing and pattern recognition. In E3S Web of Conferences (Vol. 271, p. 03030). (EDP Sciences, 2021).","DOI":"10.1051\/e3sconf\/202127103030"},{"issue":"3","key":"58980_CR16","first-page":"2172","volume":"17","author":"G Liu","year":"2019","unstructured":"Liu, G. et al. Application of protein chip combined with SELDI-TOF-MS detection to investigate serum protein expression in patients with silicosis fibrosis. Experiment. Therapeut. Med. 17 (3), 2172\u20132184 (2019).","journal-title":"Experiment. Therapeut. Med."},{"issue":"6","key":"58980_CR17","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1093\/occmed\/kqac010","volume":"72","author":"M Akg\u00fcn","year":"2022","unstructured":"Akg\u00fcn, M. et al. Pitfalls of using the ILO classification for silicosis compensation claims. Occup. Med. 72 (6), 372\u2013377 (2022).","journal-title":"Occup. Med."},{"issue":"4","key":"58980_CR18","doi-asserted-by":"publisher","first-page":"2403227","DOI":"10.1002\/adhm.202403227","volume":"14","author":"Z Chen","year":"2025","unstructured":"Chen, Z. et al. Engineering macrophage-derived exosome to deliver pirfenidone: A novel approach to combat Silicotic pulmonary fibrosis. Adv. Healthc. Mater. 14 (4), 2403227 (2025).","journal-title":"Adv. Healthc. Mater."},{"key":"58980_CR19","doi-asserted-by":"crossref","unstructured":"Akhter, Y., Ranjan, R., Singh, R. & Vatsa, M. August. SHIELD: A Self-supervised, silicosis-focused Hierarchical Imaging framework for occupational lung disease Diagnosis. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (pp. 9538\u20139546). (2025).","DOI":"10.24963\/ijcai.2025\/1060"},{"issue":"3","key":"58980_CR20","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1108\/SASBE-04-2021-0066","volume":"10","author":"R Xiong","year":"2021","unstructured":"Xiong, R. & Tang, P. Machine learning using synthetic images for detecting dust emissions on construction sites. Smart Sustain. Built. Environ. 10 (3), 487\u2013503 (2021).","journal-title":"Smart Sustain. Built. Environ."},{"key":"58980_CR21","unstructured":"https:\/\/iab-rubric.org\/resources\/healthcare-datasets\/silico-data"},{"key":"58980_CR22","doi-asserted-by":"publisher","first-page":"112086","DOI":"10.1016\/j.matdes.2023.112086","volume":"232","author":"X Pei","year":"2023","unstructured":"Pei, X. et al. Robustness of machine learning to color, size change, normalisation, and image enhancement on micrograph datasets with large sample differences. Mater. Design. 232, 112086 (2023).","journal-title":"Mater. Design"},{"issue":"14","key":"58980_CR23","doi-asserted-by":"publisher","first-page":"2407","DOI":"10.3390\/math10142407","volume":"10","author":"LV Lozano-V\u00e1zquez","year":"2022","unstructured":"Lozano-V\u00e1zquez, L. V., Miura, J., Rosales-Silva, A. J. & Luviano-Ju\u00e1rez, A. M\u00fajica-Vargas, D. Analysis of different image enhancement and feature extraction methods. Mathematics 10 (14), 2407 (2022).","journal-title":"Mathematics"},{"issue":"10","key":"58980_CR24","doi-asserted-by":"publisher","first-page":"374","DOI":"10.3390\/a15100374","volume":"15","author":"C Anam","year":"2022","unstructured":"Anam, C., Naufal, A., Sutanto, H., Adi, K. & Dougherty, G. Impact of iterative bilateral filtering on the noise power spectrum of computed tomography images. Algorithms 15 (10), 374 (2022).","journal-title":"Algorithms"},{"key":"58980_CR25","doi-asserted-by":"publisher","first-page":"37203","DOI":"10.1109\/ACCESS.2023.3266511","volume":"11","author":"A Batool","year":"2023","unstructured":"Batool, A. & Byun, Y. C. Lightweight EfficientNetB3 model based on depthwise separable convolutions for enhancing classification of leukemia white blood cell images. IEEE Access. 11, 37203\u201337215 (2023).","journal-title":"IEEE Access"},{"key":"58980_CR26","doi-asserted-by":"crossref","unstructured":"Talafha, S., Wu, D., Rekabdar, B., Li, R. & Wang, G. September. Classification and feature extraction for hydraulic structures data using advanced CNN architectures. In 2021 third international conference on transdisciplinary AI (TransAI) (pp. 137\u2013146). IEEE. (2021).","DOI":"10.1109\/TransAI51903.2021.00032"},{"key":"58980_CR27","doi-asserted-by":"crossref","unstructured":"Islam, M. Z. et al. Classification of road transport earth-retaining structures based on improved ConvNeXt V2. Transp. Eng. 100405. (2025).","DOI":"10.1016\/j.treng.2025.100405"},{"key":"58980_CR28","doi-asserted-by":"publisher","first-page":"2676","DOI":"10.1016\/j.egyr.2024.08.078","volume":"12","author":"S Luo","year":"2024","unstructured":"Luo, S., Wang, B., Gao, Q., Wang, Y. & Pang, X. Stacking integration algorithm based on CNN-BiLSTM-Attention with XGBoost for short-term electricity load forecasting. Energy Rep. 12, 2676\u20132689 (2024).","journal-title":"Energy Rep."},{"key":"58980_CR29","doi-asserted-by":"crossref","unstructured":"Akhter, Y. et al. Silicodata: An annotated benchmark CXR dataset for silicosis detection. Sci. Data, 12(1), p.1559. (2025).","DOI":"10.1038\/s41597-025-05595-4"},{"key":"58980_CR30","doi-asserted-by":"publisher","first-page":"100166","DOI":"10.1016\/j.cmpbup.2024.100166","volume":"6","author":"H Aksoy","year":"2024","unstructured":"Aksoy, H., Atila, \u00dc. & Arslan, S. Deep learning based detection of silicosis from computed tomography images. Comput. Methods Programs Biomed. Update. 6, 100166 (2024).","journal-title":"Comput. Methods Program. Biomed. Update"},{"issue":"22","key":"58980_CR31","doi-asserted-by":"publisher","first-page":"7971","DOI":"10.3390\/jcm14227971","volume":"14","author":"J Filarecki","year":"2025","unstructured":"Filarecki, J. et al. Sustainable ultralightweight U-Net-based architecture for myocardium segmentation. J. Clin. Med. 14 (22), 7971 (2025).","journal-title":"J. Clin. Med."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58980-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58980-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58980-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T11:02:52Z","timestamp":1789383772000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58980-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["58980"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-58980-3","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,22]]},"assertion":[{"value":"8 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not involve any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"28519"}}