{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T03:59:58Z","timestamp":1774843198363,"version":"3.50.1"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Virchows Arch"],"DOI":"10.1007\/s00428-026-04492-4","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T02:34:46Z","timestamp":1774838086000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-platform deep learning enables automated cytomorphologic subtyping of acute leukemia from bone marrow smears"],"prefix":"10.1007","author":[{"given":"Guangqi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanxin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hualong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyu","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qicai","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enmin","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mei","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8675-3550","authenticated-orcid":false,"given":"Zhangbo","family":"Chu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,30]]},"reference":[{"issue":"9","key":"4492_CR1","doi-asserted-by":"publisher","first-page":"1906","DOI":"10.1182\/blood-2008-10-182782","volume":"113","author":"OK Weinberg","year":"2009","unstructured":"Weinberg OK, Seetharam M, Ren L et al (2009) Clinical characterization of acute myeloid leukemia with myelodysplasia-related changes as defined by the 2008 WHO classification system. Blood. 113(9):1906\u20131908. https:\/\/doi.org\/10.1182\/blood-2008-10-182782","journal-title":"Blood."},{"issue":"13","key":"4492_CR2","doi-asserted-by":"publisher","first-page":"2424","DOI":"10.1182\/blood-2012-10-462440","volume":"121","author":"RB Walter","year":"2013","unstructured":"Walter RB, Othus M, Burnett AK et al (2013) Significance of FAB subclassification of \u201cacute myeloid leukemia, NOS\u201d in the 2008 WHO classification: analysis of 5848 newly diagnosed patients. Blood. 121(13):2424\u20132431. https:\/\/doi.org\/10.1182\/blood-2012-10-462440","journal-title":"Blood."},{"issue":"20","key":"4492_CR3","doi-asserted-by":"publisher","first-page":"2391","DOI":"10.1182\/blood-2016-06-721662","volume":"127","author":"DA Arber","year":"2016","unstructured":"Arber DA, Orazi A, Hasserjian R et al (2016) The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 127(20):2391\u20132405. https:\/\/doi.org\/10.1182\/blood-2016-06-721662","journal-title":"Blood."},{"key":"4492_CR4","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.leukres.2018.04.003","volume":"69","author":"K Sasada","year":"2018","unstructured":"Sasada K, Yamamoto N, Masuda H et al (2018) Inter-observer variance and the need for standardization in the morphological classification of myelodysplastic syndrome. Leuk Res. 69:54\u201359. https:\/\/doi.org\/10.1016\/j.leukres.2018.04.003","journal-title":"Leuk Res."},{"issue":"20","key":"4492_CR5","doi-asserted-by":"publisher","first-page":"1917","DOI":"10.1182\/blood.2020010568","volume":"138","author":"C Matek","year":"2021","unstructured":"Matek C, Krappe S, M\u00fcnzenmayer C, Haferlach T, Marr C (2021) Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set. Blood. 138(20):1917\u20131927. https:\/\/doi.org\/10.1182\/blood.2020010568","journal-title":"Blood."},{"issue":"1","key":"4492_CR6","doi-asserted-by":"publisher","first-page":"4378","DOI":"10.1038\/s41467-023-39676-y","volume":"14","author":"JG de Almeida","year":"2023","unstructured":"de Almeida JG, Gudgin E, Besser M et al (2023) Computational analysis of peripheral blood smears detects disease-associated cytomorphologies. Nat Commun. 14(1):4378. https:\/\/doi.org\/10.1038\/s41467-023-39676-y","journal-title":"Nat Commun."},{"issue":"4","key":"4492_CR7","doi-asserted-by":"publisher","DOI":"10.2196\/15963","volume":"8","author":"YY Wu","year":"2020","unstructured":"Wu YY, Huang TC, Ye RH et al (2020) A Hematologist-Level Deep Learning Algorithm (BMSNet) for Assessing the Morphologies of Single Nuclear Balls in Bone Marrow Smears: Algorithm Development. JMIR Med Inform. 8(4):e15963. https:\/\/doi.org\/10.2196\/15963","journal-title":"JMIR Med Inform."},{"key":"4492_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107343","volume":"231","author":"W Wang","year":"2023","unstructured":"Wang W, Luo M, Guo P, Wei Y, Tan Y, Shi H (2023) Artificial intelligence-assisted diagnosis of hematologic diseases based on bone marrow smears using deep neural networks. Comput Methods Programs Biomed. 231:107343. https:\/\/doi.org\/10.1016\/j.cmpb.2023.107343","journal-title":"Comput Methods Programs Biomed."},{"issue":"2","key":"4492_CR9","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","volume":"42","author":"K He","year":"2020","unstructured":"He K, Gkioxari G, Dollar P, Girshick R (2020) Mask R-CNN. IEEE Trans Pattern Anal Mach Intell. 42(2):386\u2013397. https:\/\/doi.org\/10.1109\/TPAMI.2018.2844175","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"4492_CR10","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR). pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"4492_CR11","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR). pp 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"4492_CR12","doi-asserted-by":"publisher","unstructured":"Khoury JD, Solary E, Abla O et al (2022) The 5th edition of the world health organization classification of haematolymphoid tumours: myeloid and histiocytic\/dendritic neoplasms. Leukemia 36(7):1703\u2013719. https:\/\/doi.org\/10.1038\/s41375-022-01613-1","DOI":"10.1038\/s41375-022-01613-1"},{"key":"4492_CR13","doi-asserted-by":"publisher","unstructured":"Bennett JM, Catovsky D, Daniel MT et al (1976) Proposals for the classification of the acute leukaemias. French-American-British (FAB) co-operative group. Br J Haematol 33(4):451\u2013458. https:\/\/doi.org\/10.1111\/j.1365-2141.1976.tb03563.x","DOI":"10.1111\/j.1365-2141.1976.tb03563.x"},{"key":"4492_CR14","doi-asserted-by":"publisher","unstructured":"Bennett JM, Catovsky D, Daniel MT et al (1985) Proposed revised criteria for the classification of acute myeloid leukemia. A report of the French-American-British Cooperative Group. Ann Intern Med 103(4):620\u2013625. https:\/\/doi.org\/10.7326\/0003-4819-103-4-620","DOI":"10.7326\/0003-4819-103-4-620"},{"key":"4492_CR15","doi-asserted-by":"publisher","unstructured":"Xie S, Girshick R, Doll\u00e1r P et al (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR). pp 5987\u20135995. https:\/\/doi.org\/10.1109\/CVPR.2017.634","DOI":"10.1109\/CVPR.2017.634"},{"key":"4492_CR16","unstructured":"Vaswani A et al (2017) Attention is all you need. Advances in neural information processing systems, 30. arXiv:1706.03762"},{"key":"4492_CR17","unstructured":"Tripathi AK, Chuda R (2025) Laboratory evaluation of acute leukemia. In: StatPearls. Treasure Island (FL): StatPearls"},{"issue":"11","key":"4492_CR18","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1182\/blood.2022015850","volume":"140","author":"DA Arber","year":"2022","unstructured":"Arber DA, Orazi A, Hasserjian RP et al (2022) International Consensus classification of myeloid neoplasms and acute leukemias: integrating morphologic, clinical, and genomic data. Blood. 140(11):1200\u20131228. https:\/\/doi.org\/10.1182\/blood.2022015850","journal-title":"Blood."}],"container-title":["Virchows Archiv"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00428-026-04492-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00428-026-04492-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00428-026-04492-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T02:34:49Z","timestamp":1774838089000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00428-026-04492-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,30]]},"references-count":18,"alternative-id":["4492"],"URL":"https:\/\/doi.org\/10.1007\/s00428-026-04492-4","relation":{},"ISSN":["0945-6317","1432-2307"],"issn-type":[{"value":"0945-6317","type":"print"},{"value":"1432-2307","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,30]]},"assertion":[{"value":"8 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Institutional Review Board of Wuhan Union Hospital (Approval ID: UHCT240790).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}