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We assembled an STH and <jats:italic>S. mansoni<\/jats:italic> dataset comprising over 3,000 field-of-view (FOV) images containing parasite eggs, extracted from more than 300 fecal smear prepared using the Kato-Katz technique. These images were acquired using Schistoscope\u2014a cost-effective automated digital microscope. After annotating the STH and <jats:italic>S. mansoni<\/jats:italic> eggs, we employed a transfer learning approach to train an EfficientDet deep learning model, using 70% of the dataset for training, 20% for validation, and 10% for testing. The developed model successfully identified STH and <jats:italic>S. mansoni<\/jats:italic> eggs in the FOV images, achieving weighted average scores of <jats:inline-formula>\n              <jats:tex-math>$$95.9\\%(\\pm 1.1\\%)$$<\/jats:tex-math>\n            <\/jats:inline-formula> Precision, <jats:inline-formula>\n              <jats:tex-math>$$92.1\\%(\\pm 3.5\\%)$$<\/jats:tex-math>\n            <\/jats:inline-formula> Sensitivity, <jats:inline-formula>\n              <jats:tex-math>$$98.0\\%(\\pm 0.76\\%)$$<\/jats:tex-math>\n            <\/jats:inline-formula> Specificity, and <jats:inline-formula>\n              <jats:tex-math>$$94.0\\%(\\pm 1.98\\%)$$<\/jats:tex-math>\n            <\/jats:inline-formula> F-Score across four classes of helminths (<jats:italic>A. lumbricoides<\/jats:italic>, <jats:italic>T. trichiura<\/jats:italic>, hookworm, and <jats:italic>S. mansoni<\/jats:italic>). Our system highlights the potential of the Schistoscope, enhanced with artificial intelligence, for detecting STH and <jats:italic>S. mansoni<\/jats:italic> infections in remote, resource-limited settings and for supporting the monitoring and evaluation of neglected tropical disease (NTD) control programs.<\/jats:p>","DOI":"10.1038\/s41598-025-02755-9","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T17:45:04Z","timestamp":1751391904000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep learning-based automated detection and multiclass classification of soil-transmitted helminths and Schistosoma mansoni eggs in fecal smear images"],"prefix":"10.1038","volume":"15","author":[{"given":"Prosper","family":"Oyibo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brice","family":"Meulah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tope","family":"Agbana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisette","family":"van Lieshout","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wellington","family":"Oyibo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gleb","family":"Vdovin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan-Carel","family":"Diehl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"2755_CR1","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/S0140-6736(17)31930-X","volume":"391","author":"P Jourdan","year":"2018","unstructured":"Jourdan, P., Lamberton, P., Fenwick, A. & Addiss, D. 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