ArticleFrontiers in digital health2026
Diagnostic accuracy of an artificial intelligence-driven cytopathological tool in detecting cervical pre-cancerous lesions from Pap smear images.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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11 authors.
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Abstract
Background: 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. Methods: 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 ( Results: EfficientNetB7 achieved the highest overall performance, with a macro F1 score of 0.9324 (95% CI: 0.920-0.945), an accuracy of 0.9775 (95% CI: 0.968-0.987), and a sensitivity of 0.9324 (95% CI: 0.920-0.945). ResNet50 ranked second (F1 score: 0.9282 [95% CI: 0.916-0.941]) and ResNet152 third (F1 score: 0.9240 [95% CI: 0.911-0.937]), showing minimal performance gaps. InceptionNetV3 followed closely (F1 score: 0.9220 [95% CI: 0.909-0.935]). MobileNet achieved the lowest F1 score (0.8918 [95% CI: 0.875-0.908]), but its lightweight architecture is suitable for edge deployment. The Carcinoma (CA) class achieved near-perfect recall across all models (> = 0.978). Notable interclass confusion was observed between ASC-US and LSIL, attributable to cytomorphological overlap. Conclusion: 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.
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