{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T06:24:18Z","timestamp":1791959058247,"version":"4.3.4"},"reference-count":33,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2020,4,30]],"date-time":"2020-04-30T00:00:00Z","timestamp":1588204800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/P028608\/1"],"award-info":[{"award-number":["EP\/P028608\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000765","name":"University College London","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000765","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["American J Hematol"],"published-print":{"date-parts":[[2020,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Over 200 million malaria cases globally lead to half a million deaths annually. Accurate malaria diagnosis remains a challenge. Automated imaging processing approaches to analyze Thick Blood Films (TBF) could provide scalable solutions, for urban healthcare providers in the holoendemic malaria sub\u2010Saharan region. Although several approaches have been attempted to identify malaria parasites in TBF, none have achieved negative and positive predictive performance suitable for clinical use in the west sub\u2010Saharan region. While malaria parasite object detection remains an intermediary step in achieving automatic patient diagnosis, training state\u2010of\u2010the\u2010art deep\u2010learning object detectors requires the human\u2010expert labor\u2010intensive process of labeling a large dataset of digitized TBF. To overcome these challenges and to achieve a clinically usable system, we show a novel approach. It leverages routine clinical\u2010microscopy labels from our quality\u2010controlled malaria clinics, to train a Deep Malaria Convolutional Neural Network classifier (DeepMCNN) for automated malaria diagnosis. Our system also provides total Malaria Parasite (MP) and White Blood Cell (WBC) counts allowing parasitemia estimation in MP\/\u03bcL, as recommended by the WHO. Prospective validation of the DeepMCNN achieves sensitivity\/specificity of 0.92\/0.90 against expert\u2010level malaria diagnosis. Our approach PPV\/NPV performance is of 0.92\/0.90, which is clinically usable in our holoendemic settings in the densely populated metropolis of Ibadan. It is located within the most populous African country (Nigeria) and with one of the largest burdens of\n                    <jats:italic>Plasmodium falciparum<\/jats:italic>\n                    malaria. Our openly available method is of importance for strategies aimed to scale malaria diagnosis in urban regions where daily assessment of thousands of specimens is required.\n                  <\/jats:p>","DOI":"10.1002\/ajh.25827","type":"journal-article","created":{"date-parts":[[2020,4,13]],"date-time":"2020-04-13T16:21:08Z","timestamp":1586794868000},"page":"883-891","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Expert\u2010level automated malaria diagnosis on routine blood films with deep neural networks"],"prefix":"10.1002","volume":"95","author":[{"given":"Petru","family":"Manescu","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Engineering Sciences University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J.","family":"Shaw","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of 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