{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T17:48:49Z","timestamp":1785433729906,"version":"3.56.0"},"reference-count":22,"publisher":"American Society for Microbiology","issue":"7","license":[{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["J Clin Microbiol"],"published-print":{"date-parts":[[2026,7,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title\/>\n                    <jats:p>\n                      Helminth infections are uncommon in the United States, yet accurate detection remains critical. Conventional ova-and-parasite examinations are labor-intensive and prone to error in low-prevalence settings. We evaluated the impact of implementing an artificial intelligence (AI)-based wet mount screening platform (WM-AI) in a high-volume reference laboratory. Detection rates, turnaround times, and specimen characteristics were compared across two 6-month periods before and after WM-AI implementation. Post-implementation, helminth detection increased from 29 to 104 patients, and client-level positivity rose from 4.5% to 12.3% (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.0001).\n                      <jats:italic toggle=\"yes\">Enterobius vermicularis<\/jats:italic>\n                      showed a fivefold increase in positivity (0.03% vs 0.15%,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.0001). Median turnaround time improved from 5.2 to 4.0 days (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.0001). Most infections were identified in the first or only specimen submitted, and over half of positive specimens contained \u22645 eggs or larvae, highlighting the challenge of detecting low-intensity infections. AI-assisted screening enhanced operational efficiency and diagnostic yield, suggesting improved sensitivity for low-burden infections. These findings support the integration of AI tools in parasitology workflows.\n                    <\/jats:p>\n                    <jats:sec>\n                      <jats:title>IMPORTANCE<\/jats:title>\n                      <jats:p>The data presented in our manuscript are observational after adopting a more sensitive assay in our parasitology workflow, specifically the use of artificial intelligence (AI) to pre-screen concentrated stool specimens for parasites. The notable increase in the positivity of helminth cases in our lab, which is a large reference laboratory, indicates that adopting AI as a screening tool in a parasitology laboratory can increase sensitivity of pathogenic parasites, especially helminths.<\/jats:p>\n                    <\/jats:sec>\n                  <\/jats:sec>","DOI":"10.1128\/jcm.00165-26","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T13:04:21Z","timestamp":1781269461000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Diagnosing helminth infections in a large reference laboratory in the United States: a 6-month pre- and post-implementation analysis of AI-augmented screening of concentrated fecal wet mounts"],"prefix":"10.1128","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4366-6166","authenticated-orcid":true,"given":"Cole 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