biostudies-literatureUnknown17(23)Andreassen AK<b>Background/Objectives</b>: Histopathologic grading of cervical biopsies is subject to interobserver variability, particularly at the CIN2+ treatment threshold. We evaluated a deep learning system (EagleEye) for detecting CIN2+ (CIN2, CIN3, ACIS, invasive carcinoma) on hematoxylin-eosin (H&E) whole-slide images (WSIs) and compared its performance with independent pathologists, including an AI-assisted workflow. Spatial correspondence with p16 staining was preliminarily assessed. <b>Methods</b>: Ninety-nine archived cervical punch biopsies from a single university hospital, originally diagnosed as Normal (n = 19), CIN1 (n = 20), CIN2 (n = 20), CIN3 (n = 20), or adenocarcinoma in situ (ACIS; n = 20), were digitized in a deliberately spectrum-balanced design. The original sign-out (P1), a sCancers3808https://www.ebi.ac.uk/biostudies/studies/S-EPMC12691344biostudies-literatureDigital Pathology with AI for Cervical Biopsies: Diagnostic Accuracy at the CIN2+ Threshold.PMC12691344Mortensen ESorbye SWStenbro RAndreassen AKSorensen OfalseDigital Pathology with AI for Cervical Biopsies: Diagnostic Accuracy at the CIN2+ Threshold.<b>Background/Objectives</b>: Histopathologic grading of cervical biopsies is subject to interobserver variability, particularly at the CIN2+ treatment threshold. We evaluated a deep learning system (EagleEye) for detecting CIN2+ (CIN2, CIN3, ACIS, invasive carcinoma) on hematoxylin-eosin (H&E) whole-slide images (WSIs) and compared its performance with independent pathologists, including an AI-assisted workflow. Spatial correspondence with p16 staining was preliminarily assessed. <b>Methods</b>: Ninety-nine archived cervical punch biopsies from a single university hospital, originally diagnosed as Normal (n = 19), CIN1 (n = 20), CIN2 (n = 20), CIN3 (n = 20), or adenocarcinoma in situ (ACIS; n = 20), were digitized in a deliberately spectrum-balanced design. The original sign-out (P1), a s2025-01-01T00:00:00Z2025 Nov2026-05-26T14:03:03.974Z2026-05-24T03:11:52.559ZS-EPMC126913444137500910.3390/cancers17233808