{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T06:34:38Z","timestamp":1791441278509,"version":"4.3.3"},"reference-count":15,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T00:00:00Z","timestamp":1640822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Academy of Clinical Laboratory Physicians and Scientists: Paul E. Strandjord Young Investigator Research Grant"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Clinical babesiosis is diagnosed, and parasite burden is determined, by microscopic inspection of a thick or thin Giemsa-stained peripheral blood smear. However, quantitative analysis by manual microscopy is subject to error. As such, methods for the automated measurement of percent parasitemia in digital microscopic images of peripheral blood smears could improve clinical accuracy, relative to the predicate method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Individual erythrocyte images were manually labeled as \u201cparasite\u201d or \u201cnormal\u201d and were used to train a model for binary image classification. The best model was then used to calculate percent parasitemia from a clinical validation dataset, and values were compared to a clinical reference value. Lastly, model interpretability was examined using an integrated gradient to identify pixels most likely to influence classification decisions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The precision and recall of the model during development testing were 0.92 and 1.00, respectively. In clinical validation, the model returned increasing positive signal with increasing mean reference value. However, there were 2 highly erroneous false positive values returned by the model. Further, the model incorrectly assessed 3 cases well above the clinical threshold of 10%. The integrated gradient suggested potential sources of false positives including rouleaux formations, cell boundaries, and precipitate as deterministic factors in negative erythrocyte images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>While the model demonstrated highly accurate single cell classification and correctly assessed most slides, several false positives were highly incorrect. This project highlights the need for integrated testing of machine learning-based models, even when models in the development phase perform well.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/clinchem\/hvab237","type":"journal-article","created":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T05:11:20Z","timestamp":1634620280000},"page":"218-229","source":"Crossref","is-referenced-by-count":17,"title":["Applications of Digital Microscopy and Densely Connected Convolutional Neural Networks for Automated Quantification of Babesia-Infected Erythrocytes"],"prefix":"10.1093","volume":"68","author":[{"given":"Thomas J S","family":"Durant","sequence":"first","affiliation":[{"name":"Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah N","family":"Dudgeon","sequence":"additional","affiliation":[{"name":"Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA"},{"name":"Biological and Biomedical Sciences, Yale University, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"McPadden","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anisia","family":"Simpson","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Yale New Haven Hospital, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan","family":"Price","sequence":"additional","affiliation":[{"name":"Center for Computational Health, Yale New Haven Hospital, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wade L","family":"Schulz","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA"},{"name":"Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA"},{"name":"Center for Computational Health, Yale New Haven Hospital, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Torres","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eben M","family":"Olson","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,12,30]]},"reference":[{"key":"2021123022385009400_hvab237-B1","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1093\/cid\/ciy381","article-title":"A guide to utilization of the microbiology laboratory for diagnosis of infectious diseases: 2018 update by the infectious diseases Society of America and the American Society for Microbiology","volume":"67","author":"Miller","year":"2018","journal-title":"Clin Infect Dis"},{"key":"2021123022385009400_hvab237-B2","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1002\/jca.21705","article-title":"Guidelines on the use of therapeutic apheresis in clinical practice - evidence-based approach from the Writing Committee of the American Society for Apheresis: The Eighth Special Issue","volume":"34","author":"Padmanabhan","year":"2019","journal-title":"J Clin Apher"},{"key":"2021123022385009400_hvab237-B3","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1086\/508667","article-title":"The clinical assessment, treatment, and prevention of lyme disease, human granulocytic anaplasmosis, and babesiosis: clinical practice guidelines by the Infectious Diseases Society of America","volume":"43","author":"Wormser","year":"2006","journal-title":"Clin Infect Dis"},{"key":"2021123022385009400_hvab237-B4","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/S0272-2712(03)00075-1","article-title":"Peripheral blood film review. 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