{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T09:05:27Z","timestamp":1789290327259,"version":"build-2803163510"},"reference-count":89,"publisher":"Walter de Gruyter GmbH","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_cclm-2026-1265_abs_001\">\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Morphologic examination of peripheral blood and bone marrow remains central to the diagnosis and classification of hematologic disorders. Conventional optical microscopy, however, is labor-intensive, dependent on operator expertise, and affected by interobserver variability. Digital morphology has developed from automated image acquisition and cell pre-classification into a broader field that includes whole-slide imaging, remote review, quantitative morphometry, and artificial intelligence-based analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_cclm-2026-1265_abs_002\">\n                    <jats:title>Content<\/jats:title>\n                    <jats:p>This review examines current applications of digital morphology in peripheral blood, bone marrow aspirates, malaria detection, and body-fluid analysis. Commercial platforms are evaluated with particular attention to the distinction between raw automated pre-classification, expert digital post-classification, and comparison with independent optical microscopy. Digital systems generally perform well for common mature leukocyte populations but remain less reliable for rare or diagnostically critical cells, including blasts, abnormal lymphoid cells, plasma cells, and intermediate maturation stages. Research systems increasingly extend analysis from individual-cell classification to whole-slide, specimen-level, and patient-level assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_cclm-2026-1265_abs_003\">\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>Digital morphology can improve standardization, image traceability, remote consultation, education, proficiency testing, quality assurance, and selected aspects of laboratory workflow. Its clinical value depends on appropriate validation, transparent reporting of reference methods, recognition of algorithm-specific failure modes, and clearly defined criteria for expert review and conventional microscopy. Human expertise remains essential not only for validating results but also for adapting cell taxonomies and interpretive rules to evolving classifications of hematologic diseases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_cclm-2026-1265_abs_004\">\n                    <jats:title>Outlook<\/jats:title>\n                    <jats:p>Future progress will require representative multicenter datasets, harmonized morphologic terminology, external validation, interoperability with laboratory information systems, and continuous monitoring after software or hardware updates. Integration of morphology with quantitative hematology, flow cytometry, cytogenetics, genomics, and clinical data may support more comprehensive computational diagnosis. Digital platforms may also broaden access to specialist expertise, training, and quality programs in resource-limited institutions and regions, provided that infrastructure, governance, and professional competency are adequately supported.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/cclm-2026-1265","type":"journal-article","created":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T08:56:27Z","timestamp":1789289787000},"source":"Crossref","is-referenced-by-count":0,"title":["Digital and computational morphology in hematology: current platforms, clinical evidence, and future requirements"],"prefix":"10.1515","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0782-294X","authenticated-orcid":false,"given":"Gina","family":"Zini","sequence":"first","affiliation":[{"name":"Sezione di Ematologia, Dipartimento di Scienze Radiologiche 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"}]}],"member":"374","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"2026091308562395756_j_cclm-2026-1265_ref_001","doi-asserted-by":"crossref","unstructured":"Zini, G. Hematological cytomorphology: where we are. Int J Lab Hematol 2024;46:789\u201394. https:\/\/doi.org\/10.1111\/ijlh.14330.","DOI":"10.1111\/ijlh.14330"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_002","doi-asserted-by":"crossref","unstructured":"Kratz, A, Lee, SH, Zini, G, Riedl, JA, Hur, M, Machin, SJ. Digital morphology analyzers in hematology: ICSH review and recommendations. Int J Lab Hematol 2019;41:437\u201347. https:\/\/doi.org\/10.1111\/ijlh.13042.","DOI":"10.1111\/ijlh.13042"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_003","doi-asserted-by":"crossref","unstructured":"Lawrence, P, Brown, C. Comparing human-level and machine learning model performance in white blood cell morphology assessment. Eur J Haematol 2025;114:115\u201319. https:\/\/doi.org\/10.1111\/ejh.14318.","DOI":"10.1111\/ejh.14318"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_004","doi-asserted-by":"crossref","unstructured":"Kim, H, Hur, M, d\u2019Onofrio, G, Zini, G. Real-world application of digital morphology analyzers: practical issues and challenges in clinical laboratories. Diagnostics 2025;15:677. https:\/\/doi.org\/10.3390\/diagnostics15060677.","DOI":"10.3390\/diagnostics15060677"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_005","doi-asserted-by":"crossref","unstructured":"Fan, BE, Yong, BSJ, Li, R, Wang, SSY, Aw, MYN, Chia, MF, et al.. From microscope to micropixels: a rapid review of artificial intelligence for the peripheral blood film. 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Int J Lab Hematol 2023;45:691\u20139. https:\/\/doi.org\/10.1111\/ijlh.14119.","DOI":"10.1111\/ijlh.14119"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_043","doi-asserted-by":"crossref","unstructured":"Zini, G, Mancini, F, Rossi, E, Landucci, S, d\u2019Onofrio, G. Artificial intelligence and the blood film: performance of the MC-80 digital morphology analyzer in samples with neoplastic and reactive cell types. Int J Lab Hematol 2023;45:881\u20139. https:\/\/doi.org\/10.1111\/ijlh.14160.","DOI":"10.1111\/ijlh.14160"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_044","doi-asserted-by":"crossref","unstructured":"Merino, A, Laguna, J, Rodriguez-Garcia, M, Julian, J, Casanova, A, Molina, A. Performance of the new MC-80 automated digital cell morphology analyzer in detection of normal and abnormal blood cells: comparison with the CellaVision DM9600. Int J Lab Hematol 2024;46:72\u201382. https:\/\/doi.org\/10.1111\/ijlh.14178.","DOI":"10.1111\/ijlh.14178"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_045","doi-asserted-by":"crossref","unstructured":"Jiang, H, Xu, W, Chen, W, He, J, Jiang, H, Mao, Z, et al.. Performance of the digital cell morphology analyzer MC-100i in a multicenter study in tertiary hospitals in China. Clin Chim Acta 2024;555:117801. https:\/\/doi.org\/10.1016\/j.cca.2024.117801.","DOI":"10.1016\/j.cca.2024.117801"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_046","doi-asserted-by":"crossref","unstructured":"Chen, JJ, Zhou, HH, Sun, F, Wang, Z, Ding, L, Yang, F, et al.. Evaluation of MC-80 automatic blood cell morphology analyzer in identifying the morphology of blood cells in patients with hematologic diseases and normal samples. Medicine (Baltim) 2025;104:e43323. https:\/\/doi.org\/10.1097\/md.0000000000043323.","DOI":"10.1097\/MD.0000000000043323"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_047","doi-asserted-by":"crossref","unstructured":"Zhang, S, He, Y, Wu, W, Tan, H, Xie, S, Liu, M, et al.. Comparison of the performance of two automatic cell morphology analyzers for peripheral-blood leukocyte morphology analysis: Mindray MC-100i and Sysmex DI-60. Int J Lab Hematol 2023;45:860\u20138. https:\/\/doi.org\/10.1111\/ijlh.14145.","DOI":"10.1111\/ijlh.14145"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_048","doi-asserted-by":"crossref","unstructured":"Ye, X, Fang, L, Chen, Y, Tong, J, Ning, X, Feng, L, et al.. Performance comparison of two automated digital morphology analyzers for leukocyte differential in patients with malignant hematologic diseases: Mindray MC-80 and Sysmex DI-60. 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Integration of an MC-80 digital image analyzer with an automated BC-6800Plus hematology analyzer enables accurate platelet counting in samples with EDTA-induced pseudothrombocytopenia. Ann Lab Med 2024;44:478\u201386. https:\/\/doi.org\/10.3343\/alm.2023.0460.","DOI":"10.3343\/alm.2023.0460"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_052","doi-asserted-by":"crossref","unstructured":"Bruegel, M, George, TI, Feng, B, Allen, TR, Bracco, D, Zahniser, DJ, et al.. Multicenter evaluation of the cobas m 511 integrated hematology analyzer. Int J Lab Hematol 2018;40:672\u201382. https:\/\/doi.org\/10.1111\/ijlh.12903.","DOI":"10.1111\/ijlh.12903"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_053","doi-asserted-by":"crossref","unstructured":"Chen, P, Zhang, L, Cao, X, Jin, X, Chen, N, Zhang, L, et al.. Detection of circulating plasma cells in peripheral blood using deep learning-based morphologic analysis. 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Performance of the automated digital cell image analyzer UIMD PBIA in white blood cell classification: a comparative study with Sysmex DI-60. Clin Chem Lab Med 2025;63:1409\u201318. https:\/\/doi.org\/10.1515\/cclm-2024-1323.","DOI":"10.1515\/cclm-2024-1323"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_059","doi-asserted-by":"crossref","unstructured":"Sacchetti, S, Bellia, M, Vidali, M, Zanotti, V, Giacomini, L, Gaidano, G, et al.. Comparative analysis of the performance of automated digital cell morphology analyzers for leukocyte differentiation in hematologic malignancies: Mindray MC-80 versus west medica vision hema. Int J Lab Hematol 2025;47:643\u201350. https:\/\/doi.org\/10.1111\/ijlh.14470.","DOI":"10.1111\/ijlh.14470"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_060","doi-asserted-by":"crossref","unstructured":"Wang, G, Zheng, L, Fang, Z, Luo, G, Chen, Q, Zhang, QQ, et al.. Advantages of AI-based whole blood film scanning for blast detection in markedly leukopenic blood films. Ann Hematol 2025;104:5415\u201322. https:\/\/doi.org\/10.1007\/s00277-025-06473-0.","DOI":"10.1007\/s00277-025-06473-0"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_061","doi-asserted-by":"crossref","unstructured":"Campuzano-Zuluaga, G, Hanscheid, T, Grobusch, MP. Automated haematology analysis to diagnose malaria. Malar J 2010;9:346. https:\/\/doi.org\/10.1186\/1475-2875-9-346.","DOI":"10.1186\/1475-2875-9-346"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_062","doi-asserted-by":"crossref","unstructured":"Racsa, LD, Gander, RM, Southern, PM, McElvania TeKippe, E, Doern, C, Luu, HS. Detection of intracellular parasites by use of the CellaVision DM96 analyzer during routine screening of peripheral blood smears. 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Int J Lab Hematol 2026;48:988\u201399. https:\/\/doi.org\/10.1111\/ijlh.70174.","DOI":"10.1111\/ijlh.70174"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_080","unstructured":"Furundarena, JR, Uranga, A, Urrutia, S, Aguirrezabala, L, Arambarri, A, Uresandi, N, et al.. Evaluation of the new CellaVision bone marrow aspirate application for morphologic examination and classification of bone marrow cells. Int J Lab Hematol 2026;48. ISLH abstract."},{"key":"2026091308562395756_j_cclm-2026-1265_ref_081","doi-asserted-by":"crossref","unstructured":"Kim, HN, Lee, JH, Yoon, J, Kwon, JA, Yoon, SY. Comprehensive performance assessment of the BMIA-12a system for bone marrow cell quantification in normal and hematological malignancy samples. 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PLoS One 2015;10:e0130805. https:\/\/doi.org\/10.1371\/journal.pone.0130805.","DOI":"10.1371\/journal.pone.0130805"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_084","doi-asserted-by":"crossref","unstructured":"Choi, JW, Ku, Y, Yoo, BW, Kim, JA, Lee, DS, Chai, YJ, et al.. White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks. PLoS One 2017;12:e0189259. https:\/\/doi.org\/10.1371\/journal.pone.0189259.","DOI":"10.1371\/journal.pone.0189259"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_085","doi-asserted-by":"crossref","unstructured":"Chandradevan, R, Aljudi, AA, Drumheller, BR, Kunananthaseelan, N, Amgad, M, Gutman, DA, et al.. Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells. Lab Invest 2020;100:98\u2013109. https:\/\/doi.org\/10.1038\/s41374-019-0325-7.","DOI":"10.1038\/s41374-019-0325-7"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_086","doi-asserted-by":"crossref","unstructured":"Matek, C, Krappe, S, Munzenmayer, C, Haferlach, T, Marr, C. Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set. Blood 2021;138:1917\u201327. https:\/\/doi.org\/10.1182\/blood.2020010568.","DOI":"10.1182\/blood.2020010568"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_087","doi-asserted-by":"crossref","unstructured":"Saft, L, Vaara, E, Ljung, E, Kwiecinska, A, Kumar, D, Timar, B. A deep-learning algorithm (AIFORIA) for classification of hematopoietic cells in bone marrow aspirate smears based on nine cell classes: a feasible approach for routine screening? J\u00a0Hematop 2025;18:12. https:\/\/doi.org\/10.1007\/s12308-025-00625-x.","DOI":"10.1007\/s12308-025-00625-x"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_088","doi-asserted-by":"crossref","unstructured":"Sun, S, Yin, Z, Van Cleave, JG, Wang, L, Fried, B, Bilal, KH, et al.. DeepHeme, a high-performance, generalizable deep ensemble for bone marrow morphometry and hematologic diagnosis. Sci Transl Med 2025;17:eadq2162. https:\/\/doi.org\/10.1126\/scitranslmed.adq2162.","DOI":"10.1126\/scitranslmed.adq2162"},{"key":"2026091308562395756_j_cclm-2026-1265_ref_089","doi-asserted-by":"crossref","unstructured":"Lewis, JE, Shebelut, CW, Drumheller, BR, Zhang, X, Shanmugam, N, Attieh, M, et al.. An automated pipeline for differential cell counts on whole-slide bone marrow aspirate smears. 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