{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T16:59:44Z","timestamp":1773334784247,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Anhui Sanlian University Research Team","award":["XJTD2025002"],"award-info":[{"award-number":["XJTD2025002"]}]},{"name":"Anhui Sanlian University Research Project","award":["KJZD2025004"],"award-info":[{"award-number":["KJZD2025004"]}]},{"name":"Anhui Provincial Department of Education","award":["2023jyxm0891"],"award-info":[{"award-number":["2023jyxm0891"]}]},{"DOI":"10.13039\/501100018628","name":"Scientific Research Program of Education Department of Anhui Province of China","doi-asserted-by":"publisher","award":["2025AHGXZK30741"],"award-info":[{"award-number":["2025AHGXZK30741"]}],"id":[{"id":"10.13039\/501100018628","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018628","name":"Scientific Research Program of Education Department of Anhui Province of China","doi-asserted-by":"publisher","award":["2025AHGXZK30650"],"award-info":[{"award-number":["2025AHGXZK30650"]}],"id":[{"id":"10.13039\/501100018628","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Veterinary Sciences"],"abstract":"<jats:p>Parasitic infections in veterinary medicine are commonly diagnosed through microscopic examination of fecal samples, yet traditional manual methods are labor-intensive and subject to diagnostic variability. This study investigates YOLOv8 for automated identification of parasitic elements in fecal microscopy images. Six parasitic taxa were analyzed at 1000\u00d7, 2500\u00d7, and 10,000\u00d7 magnifications: Spirometra eggs, Dipylidium egg packets, hookworm eggs, Ascaris eggs, Giardia cysts, and Trichomonas trophozoites. The dataset comprised 326 images with 3710 annotated objects, split at the sample level into training (70%), validation (15%), and testing (15%) sets. The YOLOv8n model achieved mean average precision (mAP@0.5) of 0.982 \u00b1 0.015 across 5-fold cross-validation. Per-class AP exceeded 0.97 for five taxa, with Trichomonas achieving 0.952. Inference time averaged under 60 ms per image on a standard CPU. These results demonstrate that YOLOv8 provides accurate and efficient detection of diverse parasitic elements, supporting its potential as a clinical screening tool.<\/jats:p>","DOI":"10.3390\/vetsci13030257","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T14:45:39Z","timestamp":1773153939000},"page":"257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Detection of Parasitic Elements in Veterinary Fecal Samples Using a Deep Learning-Based Object Detection Framework"],"prefix":"10.3390","volume":"13","author":[{"given":"Jing","family":"Yang","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Anhui Sanlian University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingxiang","family":"You","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Changzhou Institute of Technology, Changzhou 213032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9060-7305","authenticated-orcid":false,"given":"Zhenqing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshinori","family":"Yamaguchi","sequence":"additional","affiliation":[{"name":"Comprehensive Research Organization, Waseda University, Tokyo 162-0041, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.future.2016.12.006","article-title":"Monitoring of pet animal in smart cities using animal biometrics","volume":"83","author":"Kumar","year":"2018","journal-title":"Future Gener. 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