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Accurate identification of morphologically similar\n                    <jats:italic>Bithynia<\/jats:italic>\n                    species is critical for effective disease surveillance. This study systematically evaluates four You Only Look Once (YOLO) models\u2014YOLOv5, YOLOv8, YOLOv10, and YOLOv11\u2014for automated detection and classification of\n                    <jats:italic>Bithynia<\/jats:italic>\n                    species. Models were trained on 4,204 images containing 8,559 annotated specimens of\n                    <jats:italic>Bithynia funiculata<\/jats:italic>\n                    ,\n                    <jats:italic>B. siamensis goniomphalos<\/jats:italic>\n                    , and\n                    <jats:italic>B. s. siamensis<\/jats:italic>\n                    . Model performance was assessed through comparative analyses of YOLO architectures and direct benchmarking against five human experts. YOLOv10 demonstrated the highest classification accuracy (98.7%), robust performance across diverse environmental conditions, and computational efficiency (model size: 31.9\u00a0MB; processing speed: 4.54 FPS). Our analysis reveals a fundamental complementarity between AI and human expertise: humans achieved perfect detection accuracy, while the artificial intelligence model provided superior classification of detected specimens (74.5% for YOLOv10 and 48.3% for human experts, respectively). These findings indicate that integrating human detection with automated classification in hybrid human\u2013AI workflows could substantially improve diagnostic accuracy and throughput. This study establishes a technological foundation for scalable surveillance systems to support control of\n                    <jats:italic>O. viverrini<\/jats:italic>\n                    transmission in resource-limited endemic regions.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-43387-x","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T19:03:41Z","timestamp":1774379021000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automated real-time surveillance of Bithynia snails using a comparative YOLO based approach for liver fluke host detection"],"prefix":"10.1038","volume":"16","author":[{"given":"Thannaree","family":"Jenwithee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thirayu","family":"Meererksom","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanin","family":"Limpanont","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Banchob","family":"Sripa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thewarach","family":"Laha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Apiporn T.","family":"Suwannatrai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,24]]},"reference":[{"key":"43387_CR1","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.actatropica.2014.02.009","volume":"141","author":"YC Wang","year":"2015","unstructured":"Wang, Y. 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