{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T07:16:42Z","timestamp":1788419802247,"version":"build-2803163510"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cervical cancer remains a leading cause of cancer death among women in sub-Saharan Africa, with Tanzania bearing a disproportionate burden. The critical shortage of trained pathologists, coupled with the unprecedented disease burden in low-resource settings, underscores the urgent need for point-of-care screening and diagnosis to enable timely decision-making. We assessed the diagnostic accuracy of AI-driven cytopathological tools to improve diagnostic efficiency and accessibility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective secondary data analysis evaluated five convolutional neural network (CNN) architectures: EfficientNetB7, MobileNet, ResNet50, ResNet152, and InceptionNetV3, for semi-automated classification of cervical cell abnormalities. A total of 11,955 Pap smear cytological images were used from the publicly available Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database, spanning six cellular classes: Normal, ASC-US, LSIL, ASC-H, HSIL, and carcinoma. Performance was assessed on a hold-out test set (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009961) using macro-averaged metrics.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>EfficientNetB7 achieved the highest overall performance, with a macro F1 score of 0.9324 (95% CI: 0.920\u20130.945), an accuracy of 0.9775 (95% CI: 0.968\u20130.987), and a sensitivity of 0.9324 (95% CI: 0.920\u20130.945). ResNet50 ranked second (F1 score: 0.9282 [95% CI: 0.916\u20130.941]) and ResNet152 third (F1 score: 0.9240 [95% CI: 0.911\u20130.937]), showing minimal performance gaps. InceptionNetV3 followed closely (F1 score: 0.9220 [95% CI: 0.909\u20130.935]). MobileNet achieved the lowest F1 score (0.8918 [95% CI: 0.875\u20130.908]), but its lightweight architecture is suitable for edge deployment. The Carcinoma (CA) class achieved near-perfect recall across all models (&amp;gt;\u2009=\u20090.978). Notable interclass confusion was observed between ASC-US and LSIL, attributable to cytomorphological overlap.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>EfficientNetB7 offers promising diagnostic accuracy for automated cervical cancer screening using Pap smear images and shows potential for integration into point-of-care workflows in low-resource settings. Future work should focus on training models on locally representative datasets and exploring whole smear analysis to reduce interclass misclassification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1859980","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:44:12Z","timestamp":1788417852000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Diagnostic accuracy of an artificial intelligence-driven cytopathological tool in detecting cervical pre-cancerous lesions from Pap smear images"],"prefix":"10.3389","volume":"8","author":[{"given":"Sang'udi","family":"Sang'udi","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, School of Biomedical Sciences, Muhimbili University of Health and Allied Sciences","place":["Dar es Salaam, 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