{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T03:40:01Z","timestamp":1792381201678,"version":"4.4.0"},"reference-count":40,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Lab Hematology"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Morphologic evaluation of peripheral blood (PB) smears and bone marrow aspirates (BMA) remains central to the diagnosis of acute leukemias, particularly for the identification and quantification of blasts. However, this process is time\u2010consuming, operator\u2010dependent, and subject to inter\u2010observer variability. Recent advances in artificial intelligence (AI), particularly deep learning, have enabled the development of automated systems for leukocyte classification and blast detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We performed a narrative review of the literature on AI\u2010based approaches for morphologic evaluation in hematology, focusing on blast recognition and leukemia screening. Both classical machine learning and deep learning methodologies were analyzed, along with their application to PB smears and BMA samples. In addition, currently available commercial digital morphology platforms were reviewed with respect to their performance in blast detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Classical machine learning approaches demonstrated good performance on maturing cells of most lineages, and moderate performance in blast recognition, limited by reliance on manually selected features. Deep learning models, particularly convolutional neural networks, achieved improved accuracy and near\u2010human performance in PB smear analysis, with reported sensitivities and specificities often exceeding 90% for blast detection. However, performance in BMA analysis remains more variable due to increased cellular complexity. Commercial platforms show high concordance with manual microscopy for mature leukocyte classification, but more modest accuracy for immature and neoplastic cells, including blasts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI\u2010based digital morphology systems are promising tools for supporting morphologic evaluation in hematology laboratories. While current platforms improve efficiency and standardization, limitations in blast detection accuracy and generalizability prevent their use as standalone diagnostic tools. Further development, including large\u2010scale validation and improved model interpretability, will be essential for their integration into routine clinical practice. The International Council for Standardization in Haematology (ICSH) presently suggests prudence in the widespread clinical adoption of AI\u2010driven bone marrow analysis systems, except within environments that are properly validated, approved by regulatory authorities, and supported by rigorous quality assurance measures. Their integration into routine practice will require broader validation, regulatory approval, and quality assurance frameworks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ijlh.70174","type":"journal-article","created":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T05:34:08Z","timestamp":1781933648000},"page":"988-999","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Next Generation Digital Morphology: Blast Preclassification in Bone Marrow Aspirates"],"prefix":"10.1111","volume":"48","author":[{"given":"John Donald","family":"Marra","sequence":"first","affiliation":[{"name":"Sezione di Ematologia, Dipartimento di Scienze Ematologiche ed Ematologiche Universit\u00e0 Cattolica del Sacro Cuore  Rome Italy"},{"name":"Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario \u201cA. Gemelli\u201d IRCCS  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0782-294X","authenticated-orcid":false,"given":"Gina","family":"Zini","sequence":"additional","affiliation":[{"name":"Sezione di Ematologia, Dipartimento di Scienze Ematologiche ed Ematologiche Universit\u00e0 Cattolica del Sacro Cuore  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,19]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood.2022015850"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41375\u2010022\u201001613\u20101"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra043442"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/ijlh.13042"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1751\u2010553X.2007.01002.x"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256\u2010019\u20100101\u20109"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2024.1402768"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/hem3.70048"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/ijlh.14082"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cll.2024.04.005"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2005.10.001"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2004.01.012"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcla.22024"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2014.09.002"},{"key":"e_1_2_9_16_1","unstructured":"A.Krizhevsky I.Sutskever andG. 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