{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T03:51:35Z","timestamp":1773373895042,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pntd.0014037","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000}}],"reference-count":24,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Marie Pedersen og Jensine Heibergs Legat","award":["968312"],"award-info":[{"award-number":["968312"]}]}],"content-domain":{"domain":["www.plosntds.org"],"crossmark-restriction":false},"short-container-title":["PLoS Negl Trop Dis"],"abstract":"<jats:p>Female genital schistosomiasis (FGS) is a neglected but highly prevalent disease in sub-Saharan Africa, caused by Schistosoma haematobium egg-induced inflammation in the pelvic region. FGS is characterized by four mucosal lesion types in the lower female genital tract: grainy sandy patches (GSP), homogeneous yellow sandy patches, abnormal blood vessels, and rubbery papules. This study focuses on the segmentation of cervical GSP lesions using a deep-learning convolutional neural network. A total of 583 cervical images from women in a S. haematobium endemic region of Madagascar, all exhibiting FGS-associated lesions, particularly GSP lesions, were used for this study. Weak annotations (non-pixel-wise) were generated using QubiFier software. A U-Net model with a focal loss function, and an Adam optimizer was trained to segment GSP lesions. A 5-fold cross validation was performed, thus resulting in 5 models. The models were evaluated on a dedicated test set, where model predictions were compared to the annotations. The average results of the models after cross validation were a DICE score of 0.61, accuracy of 0.81, sensitivity of 0.84, and specificity of 0.81. While the models performed well, the performance was affected by factors such as weak annotations, limited number of images, and image quality issues in the form of artifacts like specular reflections. These findings highlight the potential of U-Net-based models for automated lesion segmentation of FGS. Integration of such models into smartphone-based diagnostic tools could enable real-time detection and possible diagnosis of FGS in regions lacking specialized medical equipment or expertise. This approach may enhance access to early diagnosis, particularly in rural and underserved areas of sub-Saharan Africa, where FGS remains a significant public health burden. Future work should focus on enhancing model performance, validating using external datasets, and exploring feasibility for mobile integration, offering a cost-effective solution for point-of-care FGS detection.<\/jats:p>","DOI":"10.1371\/journal.pntd.0014037","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T18:41:41Z","timestamp":1772736101000},"page":"e0014037","update-policy":"https:\/\/doi.org\/10.1371\/journal.pntd.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Image segmentation of cervical grainy sandy patches lesions associated with female genital schistosomiasis using deep convolutional neural network with U-NET architecture"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7274-8076","authenticated-orcid":true,"given":"Karl Emil","family":"J\u00f8ker","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter Christian Derek","family":"Leutscher","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristine Br\u00f8ndbjerg","family":"\u00d8by","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karoline","family":"J\u00f8ker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bodo Sahondra","family":"Randrianasolo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maciej","family":"Plocharski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Louise Thomsen Schmidt","family":"Arenholt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"issue":"7","key":"pntd.0014037.ref001","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pntd.0011465","article-title":"Assessing the prevalence of Female Genital Schistosomiasis and comparing the acceptability and performance of health worker-collected and self-collected cervical-vaginal swabs using PCR testing among women in North-Western Tanzania: the ShWAB study","volume":"17","author":"T Ursini","year":"2023","journal-title":"PLoS Negl Trop Dis"},{"key":"pntd.0014037.ref002","article-title":"Infection with schistosomes (Schistosoma haematobium, Schistosoma mansoni and Schistosoma japonicum).","volume-title":"Schistosomes, liver flukes and Helicobacter pylori","author":"I A R C Working Group on the Evaluation of Carcinogenic Risks to Humans","year":"1994"},{"issue":"4","key":"pntd.0014037.ref003","doi-asserted-by":"crossref","first-page":"382","DOI":"10.3390\/parasitologia4040034","article-title":"Female genital schistosomiasis (FGS) prevalence and burden across endemic countries, timelines, and age groups: a retrospective study","volume":"4","author":"N Kaur","year":"2024","journal-title":"Parasitologia"},{"issue":"3","key":"pntd.0014037.ref004","doi-asserted-by":"crossref","first-page":"458","DOI":"10.3390\/microorganisms12030458","article-title":"Female genital schistosomiasis: a neglected among the neglected tropical diseases","volume":"12","author":"B Rossi","year":"2024","journal-title":"Microorganisms"},{"key":"pntd.0014037.ref005","unstructured":"World Health Organization. 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