{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T12:35:44Z","timestamp":1791894944799,"version":"4.3.4"},"reference-count":14,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T00:00:00Z","timestamp":1693180800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Lab Hematology"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>Implementing artificial intelligence\u2010based instruments in hematology laboratories requires evidence of efficiency in classifying pathological cells. In two\u2010Universities, we assessed the performance of the Mindray\u00ae MC\u201080 for hematology patients with frequent leukemic and dysplastic cells.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The Mindray MC\u201080\u00ae locates and pre\u2010classifies cells in blood films. In a two\u2010university study, four films were prepared from 591 samples, two each for the analyser MC\u201080 and the microscope reference method, using reagents from two different manufacturers. We used Microsoft Excel\u00ae statistics for imprecision and distributional inaccuracy and a matrix table model (H20\u2010A2 CLSI standard) for sensitivity, specificity and predictive value for atypical cells.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The results indicate minimal within\u2010run imprecision (ICSH method) and good intra\u2010method consistency even on duplicate analysis of 413 samples with a high incidence of hematological abnormalities (<jats:italic>r<\/jats:italic>\u2009=\u20090.942 or more, except for basophils, <jats:italic>r<\/jats:italic>\u2009=\u20090.841, and reactive lymphocytes, <jats:italic>r<\/jats:italic>\u2009=\u20090.847). Distributional inaccuracy was also very low compared to the microscope reference, with a pass rate higher than 80% for pathological cells (except 75.1% for reactive lymphocytes). The primary causes of discrepancy were bizarre shapes of dysplastic neutrophils and inconsistent nomenclature for lymphoma cells. Sensitivity for critical samples containing cells typically absent in circulating blood (immature or malignant) was 98.8% for immature granulocytes, 83.8% for all types of neoplastic cells, 93.6% for reactive lymphocytes and 97.5% for nucleated red blood cells. The negative predictive values of MC\u201080 were 98.8% for immature granulocytes, 88.4% for the different types of neoplastic cells, 97.8% for reactive lymphocytes, and 96.9% for nucleated red blood cells.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our study highlights the outstanding diagnostic performance of this artificial intelligence\u2013based blood film analyzer for hematology patients with circulating abnormal cells. We appreciated the morphological harmonization of cells observed on the screen and those seen in the microscope.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ijlh.14160","type":"journal-article","created":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T05:29:00Z","timestamp":1693286940000},"page":"881-889","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Artificial intelligence and the blood film: Performance of the <scp>MC<\/scp>\u201080 digital morphology analyzer in samples with neoplastic and reactive cell types"],"prefix":"10.1111","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0782-294X","authenticated-orcid":false,"given":"Gina","family":"Zini","sequence":"first","affiliation":[{"name":"Catholic University of Sacred Heart Hematology Service  Rome Italy"},{"name":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Mancini","sequence":"additional","affiliation":[{"name":"Dipartimento di Medicina Traslazionale e di Precisione, Divisione di Ematologia Universit\u00e0 La Sapienza  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"Rossi","sequence":"additional","affiliation":[{"name":"Catholic University of Sacred Heart Hematology Service  Rome Italy"},{"name":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefania","family":"Landucci","sequence":"additional","affiliation":[{"name":"Dipartimento di Medicina Traslazionale e di Precisione, Divisione di Ematologia Universit\u00e0 La Sapienza  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1948-3092","authenticated-orcid":false,"given":"Giuseppe","family":"d'Onofrio","sequence":"additional","affiliation":[{"name":"Catholic University of Sacred Heart Hematology Service  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,8,28]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/ijlh.13042"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.460"},{"key":"e_1_2_10_4_1","volume-title":"WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues","author":"Swerdlow SH","year":"2017"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/ijlh.12201"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1177\/096228029900800204"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.11613\/BM.2015.015"},{"key":"e_1_2_10_8_1","volume-title":"Reference Leukocyte (WBC) Differential Count (Proportional) and Evaluation of Instrumental Methods; Approved Standard","author":"CLSI","year":"2007"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/ijlh.12503"},{"key":"e_1_2_10_10_1","volume-title":"Predictive Value and Efficiency of Medical Diagnoses","author":"Galen RS","year":"1975"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1309\/XMB9K0J41LHLATAY"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/ijlh.12044"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1136\/jclinpath-2013-201737"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12288-017-0835-7"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1136\/jcp.2005.033787"}],"container-title":["International Journal of Laboratory Hematology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/ijlh.14160","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T05:38:00Z","timestamp":1700113080000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/ijlh.14160"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,28]]},"references-count":14,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["10.1111\/ijlh.14160"],"URL":"https:\/\/doi.org\/10.1111\/ijlh.14160","archive":["Portico"],"relation":{},"ISSN":["1751-5521","1751-553X"],"issn-type":[{"value":"1751-5521","type":"print"},{"value":"1751-553X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,28]]},"assertion":[{"value":"2023-03-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-06","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}