{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T06:06:36Z","timestamp":1791957996957,"version":"4.3.4"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T00:00:00Z","timestamp":1674777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T00:00:00Z","timestamp":1674777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Australian Government, Department for Foreign Affairs and Trade, as part of the FIND Malaria Innovation Platform Project"},{"name":"TWAS","award":["2019_19-225 RG\/BIO\/AF\/AC_G"],"award-info":[{"award-number":["2019_19-225 RG\/BIO\/AF\/AC_G"]}]},{"name":"Welcome Trust of Great Britain"},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Malar J"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Microscopic examination is commonly used for malaria diagnosis in the field. However, the lack of well-trained microscopists in malaria-endemic areas impacted the most by the disease is a severe problem. Besides, the examination process is time-consuming and prone to human error. Automated diagnostic systems based on machine learning offer great potential to overcome these problems. This study aims to evaluate Malaria Screener, a smartphone-based application for malaria diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 190 patients were recruited at two sites in rural areas near Khartoum, Sudan. The Malaria Screener mobile application was deployed to screen Giemsa-stained blood smears. Both expert microscopy and nested PCR were performed to use as reference standards. First, Malaria Screener was evaluated using the two reference standards. Then, during post-study experiments, the evaluation was repeated for a newly developed algorithm, PlasmodiumVF-Net.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Malaria Screener reached 74.1% (95% CI 63.5\u201383.0) accuracy in detecting\n                      <jats:italic>Plasmodium falciparum<\/jats:italic>\n                      malaria using expert microscopy as the reference after a threshold calibration. It reached 71.8% (95% CI 61.0\u201381.0) accuracy when compared with PCR. The achieved accuracies meet the WHO Level 3 requirement for parasite detection. The processing time for each smear varies from 5 to 15\u00a0min, depending on the concentration of white blood cells (WBCs). In the post-study experiment, Malaria Screener reached 91.8% (95% CI 83.8\u201396.6) accuracy when patient-level results were calculated with a different method. This accuracy meets the WHO Level 1 requirement for parasite detection. In addition, PlasmodiumVF-Net, a newly developed algorithm, reached 83.1% (95% CI 77.0\u201388.1) accuracy when compared with expert microscopy and 81.0% (95% CI 74.6\u201386.3) accuracy when compared with PCR, reaching the WHO Level 2 requirement for detecting both\n                      <jats:italic>Plasmodium falciparum<\/jats:italic>\n                      and\n                      <jats:italic>Plasmodium vivax<\/jats:italic>\n                      malaria, without using the testing sites data for training or calibration. Results reported for both Malaria Screener and PlasmodiumVF-Net used thick smears for diagnosis. In this paper, both systems were not assessed in species identification and parasite counting, which are still under development.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Malaria Screener showed the potential to be deployed in resource-limited areas to facilitate routine malaria screening. It is the first smartphone-based system for malaria diagnosis evaluated on the patient-level in a natural field environment. Thus, the results in the field reported here can serve as a reference for future studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12936-023-04446-0","type":"journal-article","created":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T11:03:52Z","timestamp":1674817432000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Patient-level performance evaluation of a smartphone-based malaria diagnostic application"],"prefix":"10.1186","volume":"22","author":[{"given":"Hang","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fayad O.","family":"Mohammed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muzamil","family":"Abdel Hamid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasmin M.","family":"Kassim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdelrahim O.","family":"Mohamed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard J.","family":"Maude","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xavier C.","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ewurama D.A.","family":"Owusu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seda","family":"Yerlikaya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabine","family":"Dittrich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Jaeger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,27]]},"reference":[{"key":"4446_CR1","first-page":"2020","volume-title":"20 years of global progress and challenges","author":"WHO. World malaria report","year":"2020","unstructured":"WHO. World malaria report. 20 years of global progress and challenges. Geneva: World Health Organization; 2020. p. 2020."},{"key":"4446_CR2","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.tmaid.2007.10.001","volume":"6","author":"LK Erdman","year":"2008","unstructured":"Erdman LK, Kain KC. Molecular diagnostic and surveillance tools for global malaria control. Travel Med Infect Dis. 2008;6:82\u201399.","journal-title":"Travel Med Infect Dis"},{"key":"4446_CR3","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1038\/nrmicro1474","volume":"4","author":"D Bell","year":"2006","unstructured":"Bell D, Wongsrichanalai C, Barnwell JW. Ensuring quality and access for malaria diagnosis: how can it be achieved? Nat Rev Microbiol. 2006;4:682\u201395.","journal-title":"Nat Rev Microbiol"},{"key":"4446_CR4","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.trsl.2017.12.004","volume":"194","author":"M Poostchi","year":"2018","unstructured":"Poostchi M, Silamut K, Maude RJ, Jaeger S, Thoma G. Image analysis and machine learning for detecting malaria. Transl Res. 2018;194:36\u201355.","journal-title":"Transl Res"},{"key":"4446_CR5","doi-asserted-by":"crossref","unstructured":"Delahunt CB, Mehanian C, Hu L, McGuire SK, Champlin CR, Horning MP, et al. Automated microscopy and machine learning for expert-level malaria field diagnosis. In: 2015 IEEE Global Humanitarian Technology Conference (GHTC) 2015 Oct 8 (pp. 393\u2013399).","DOI":"10.1109\/GHTC.2015.7344002"},{"key":"4446_CR6","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1186\/s12936-022-04146-1","volume":"21","author":"D Das","year":"2022","unstructured":"Das D, Vongpromek R, Assawariyathipat T, Srinamon K, Kennon K, Stepniewska K, et al. Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning. Malar J. 2022;21:122.","journal-title":"Malar J"},{"key":"4446_CR7","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1186\/s12936-021-03631-3","volume":"20","author":"MP Horning","year":"2021","unstructured":"Horning MP, Delahunt CB, Bachman CM, Luchavez J, Luna C, Hu L, et al. Performance of a fully-automated system on a WHO malaria microscopy evaluation slide set. Malar J. 2021;20:110.","journal-title":"Malar J"},{"key":"4446_CR8","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1002\/ajh.25827","volume":"95","author":"P Manescu","year":"2020","unstructured":"Manescu P, Shaw MJ, Elmi M, Neary-Zajiczek L, Claveau R, Pawar V, et al. Expert-level automated malaria diagnosis on routine blood films with deep neural networks. Am J Hematol. 2020;95:883\u201391.","journal-title":"Am J Hematol"},{"key":"4446_CR9","doi-asserted-by":"publisher","first-page":"e201700003","DOI":"10.1002\/jbio.201700003","volume":"11","author":"GP Gopakumar","year":"2018","unstructured":"Gopakumar GP, Swetha M, Sai Siva G, Sai Subrahmanyam GR. Convolutional neural network-based malaria diagnosis from focus stack of blood smear images acquired using custom-built slide scanner. J Biophotonics. 2018;11:e201700003.","journal-title":"J Biophotonics"},{"key":"4446_CR10","doi-asserted-by":"publisher","first-page":"e104855","DOI":"10.1371\/journal.pone.0104855","volume":"9","author":"N Linder","year":"2014","unstructured":"Linder N, Turkki R, Walliander M, M\u00e5rtensson A, Diwan V, Rahtu E, et al. A malaria diagnostic tool based on computer vision screening and visualization of Plasmodium falciparum candidate areas in digitized blood smears. PLoS ONE. 2014;9:e104855.","journal-title":"PLoS ONE"},{"key":"4446_CR11","first-page":"115","volume":"31","author":"JA Quinn","year":"2014","unstructured":"Quinn JA, Andama A, Munabi I, Kiwanuka FN. Automated blood smear analysis for mobile malaria diagnosis. Mobile Point-of-Care Monitors Diagn Device Design. 2014;31:115.","journal-title":"Mobile Point-of-Care Monitors Diagn Device Design"},{"key":"4446_CR12","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.procs.2016.07.024","volume":"90","author":"L Rosado","year":"2016","unstructured":"Rosado L, Da Costa JM, Elias D, Cardoso JS. Automated detection of malaria parasites on thick blood smears via mobile devices. Procedia Comput Sci. 2016;90:138\u201344.","journal-title":"Procedia Comput Sci"},{"key":"4446_CR13","doi-asserted-by":"publisher","first-page":"8895429","DOI":"10.1155\/2020\/8895429","volume":"2020","author":"M Masud","year":"2020","unstructured":"Masud M, Alhumyani H, Alshamrani SS, Cheikhrouhou O, Ibrahim S, Muhammad G, et al. Leveraging deep learning techniques for malaria parasite detection using mobile application. Wireless Commun Mobile Comput. 2020;2020:8895429.","journal-title":"Wireless Commun Mobile Comput"},{"key":"4446_CR14","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1007\/s10278-019-00284-2","volume":"33","author":"S Chibuta","year":"2020","unstructured":"Chibuta S, Acar AC. Real-time malaria parasite screening in thick blood smears for low-resource setting. J Digit Imaging. 2020;33:763\u201375.","journal-title":"J Digit Imaging"},{"key":"4446_CR15","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1186\/s12879-020-05453-1","volume":"20","author":"H Yu","year":"2020","unstructured":"Yu H, Yang F, Rajaraman S, Ersoy I, Moallem G, Poostchi M, et al. Malaria Screener: a smartphone application for automated malaria screening. BMC Infect Dis. 2020;20:825.","journal-title":"BMC Infect Dis"},{"key":"4446_CR16","unstructured":"FIND, Diagnosis for all. Malaria Innovation Platform Project. https:\/\/www.finddx.org\/women-children\/innovation-platform\/."},{"key":"4446_CR17","doi-asserted-by":"crossref","unstructured":"Zhou XH, McClish DK, Obuchowski NA. Statistical methods in diagnostic medicine. John Wiley & Sons; 2011. Chapt. 6, Sample Size Calculation; p.196\u2013218.","DOI":"10.1002\/9780470906514"},{"key":"4446_CR18","volume-title":"Preparation of giemsa working solution","author":"WHO","year":"2016","unstructured":"WHO. Preparation of giemsa working solution. Geneva: World Health Organization; 2016."},{"key":"4446_CR19","unstructured":"The Obare Method Calculator. Worldwide Antimalarial Resistance Network. https:\/\/www.wwarn.org\/tools-resources\/procedures\/obare-method-calculator."},{"key":"4446_CR20","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1109\/JBHI.2019.2939121","volume":"24","author":"F Yang","year":"2019","unstructured":"Yang F, Poostchi M, Yu H, Zhou Z, Silamut K, Yu J, et al. Deep learning for smartphone-based malaria parasite detection in thick blood smears. IEEE J Biomed Health Inform. 2019;24:1427\u201338.","journal-title":"IEEE J Biomed Health Inform"},{"key":"4446_CR21","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.4568","volume":"6","author":"S Rajaraman","year":"2018","unstructured":"Rajaraman S, Antani SK, Poostchi M, Silamut K, Hossain MA, Maude RJ, et al. Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. PeerJ. 2018;6: e4568.","journal-title":"PeerJ"},{"key":"4446_CR22","doi-asserted-by":"publisher","first-page":"1994","DOI":"10.3390\/diagnostics11111994","volume":"11","author":"YM Kassim","year":"2021","unstructured":"Kassim YM, Yang F, Yu H, Maude RJ, Jaeger S. Diagnosing malaria patients with Plasmodium falciparum and vivax using deep learning for thick smear images. Diagnostics. 2021;11:1994.","journal-title":"Diagnostics"},{"key":"4446_CR23","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/0166-6851(93)90077-B","volume":"61","author":"VE Pinheirob","year":"1993","unstructured":"Pinheirob VE, Thaithongc S, Browna KN. High sensitivity of detection of human malaria parasites by the use of nested polymerase chain reaction. Mol Biochem Parasitol. 1993;61:315\u201320.","journal-title":"Mol Biochem Parasitol"},{"key":"4446_CR24","volume-title":"Malaria microscopy quality assurance manual-version 2","author":"WHO","year":"2016","unstructured":"WHO. Malaria microscopy quality assurance manual-version 2. Geneva: World Health Organization; 2016."}],"container-title":["Malaria Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12936-023-04446-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12936-023-04446-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12936-023-04446-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T11:04:45Z","timestamp":1674817485000},"score":1,"resource":{"primary":{"URL":"https:\/\/malariajournal.biomedcentral.com\/articles\/10.1186\/s12936-023-04446-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,27]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["4446"],"URL":"https:\/\/doi.org\/10.1186\/s12936-023-04446-0","relation":{},"ISSN":["1475-2875"],"issn-type":[{"value":"1475-2875","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,27]]},"assertion":[{"value":"8 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study received institutional ethical clearance from the Research Ethics Committee of Medical and Health Sciences, University of Khartoum, number Ref: FM\/DO\/EC, and the National Research Ethics Review Committee of Federal Ministry of Health, number 3\u20132-2020. Written informed consent was obtained from all study subjects. In the case of children, ethical clearance was obtained from the patient parents or guardians.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare that they have no competing interests. SD, EO, SY, and XCD were all employed by FIND, the global alliance for diagnostics.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"33"}}