ArticleFrontiers in artificial intelligence2026
A cross-validated deep learning framework for automated detection of DMBA-induced ovarian cancer from histopathological images with CA-125 biomarker support.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Ovarian cancer (OC) remains a major global health challenge due to its asymptomatic progression and the lack of reliable diagnostic tools. Recent advances in artificial intelligence (AI) offer promising opportunities to enhance diagnostic accuracy through automated analysis of biomedical images and integration of multimodal data. Female albino rats were divided into control and cancer-induced groups. OC were chemically induced by 7,12-Dimethylbenz[a]anthracene (DMBA) exposure. Blood samples were collected at baseline and predefined post-induction time points to quantify serum Cancer Antigen 125 (CA-125) levels, which were significantly elevated in the cancer group (438.7 ± 6.4 U/mL) compared with controls (22.5 ± 3.1 U/mL;
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