{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T20:29:38Z","timestamp":1790627378392,"version":"4.1.0"},"update-to":[{"DOI":"10.1371\/journal.pntd.0013432","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000}}],"reference-count":32,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100020137","name":"Johnson & Johnson Foundation","doi-asserted-by":"crossref","award":["66552075"],"award-info":[{"award-number":["66552075"]}],"id":[{"id":"10.13039\/100020137","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100004331","name":"Johnson & Johnson","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100004331","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Global Health Institute of Merck KGaA, Darmstadt, Germany","award":["13039\/100009945"],"award-info":[{"award-number":["13039\/100009945"]}]}],"content-domain":{"domain":["www.plosntds.org"],"crossmark-restriction":false},"short-container-title":["PLoS Negl Trop Dis"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The World Health Organization (WHO) has emphasised the need for innovative diagnostic tools to support the control and elimination of neglected tropical diseases (NTDs). Microscopy-based diagnostics, the current standard, rely on trained technicians for labour-intensive processes, posing logistical challenges in the low-resource settings where NTDs are most prevalent. This study describes the technical details of an artificial intelligence-powered digital pathology (AI-DP) platform designed to support large-scale deworming programs for two NTDs, alongside its analytical performance and user experience in laboratory and field settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methodology\/principal findings<\/jats:title>\n                    <jats:p>\n                      The AI-DP platform integrates electronic data capture tools, whole-slide imaging scanners, onboard AI analysis, and result verification software to automate microscopy-based screening. Targeting soil-transmitted helminthiasis (STH) and intestinal schistosomiasis (SCH) as initial use cases, the system was deployed in Ethiopia and Uganda, scanning 951 Kato-Katz (KK) thick smears containing 43,919 verified helminth eggs. Using 5-fold cross-validation, precision\/recall\/average precision were 95.4%\/91.7%\/97.1% for\n                      <jats:italic>Ascaris lumbricoides<\/jats:italic>\n                      , 95.9%\/86.7%\/94.8% for\n                      <jats:italic>Trichuris trichiura<\/jats:italic>\n                      , 84.6%\/86.6%\/91.4% for hookworm, and 89.1%\/79.1%\/89.2% for\n                      <jats:italic>Schistosoma mansoni<\/jats:italic>\n                      . Feedback from 14 field users across 30 real-world scenarios indicated the AI-DP platform\u2019s improved usability, particularly in hardware portability and software interfaces, though the average scan time of 12.5 minutes per smear was identified as a limitation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Conclusions\/significance<\/jats:title>\n                    <jats:p>The AI-DP platform demonstrates potential as a tool for efficient monitoring and evaluation of STH and SCH control programs by providing near-real-time data with quality controls. However, further validation studies are needed to assess its clinical diagnostic performance, field usability, and cost-effectiveness in large-scale STH and SCH deworming programs. Given that the platform also provides a pipeline for any microscopy-based diagnosis, its potential for other NTDs also needs further attention.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pntd.0013432","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T17:36:38Z","timestamp":1773855398000},"page":"e0013432","update-policy":"https:\/\/doi.org\/10.1371\/journal.pntd.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["An artificial intelligence-powered digital pathology platform to support large-scale deworming programs against soil-transmitted helminthiasis and intestinal schistosomiasis in resource-limited settings"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6826-9290","authenticated-orcid":true,"given":"Peter K.","family":"Ward","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed 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