Gastro hep advances · 2026
Artificial Intelligence for Anal Cancer Screening: Multicenter Multicontinental Validation of a Trinary Model in High-Resolution Anoscopy.
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Martins Miguel, Mascarenhas Miguel, Frias Joana, Araújo Catarina, Barroso Luís, Manzione Thiago, Donoso Pedro Diaz, Alam Amine, Javed Ashamed, Afonso João, Cardoso Pedro, Almeida Maria João, Mota Joana, Ribeiro Tiago, Mendes Francisco, Santos André, Costa António, Ferreira de Carvalho Matheus, Fathallal Nadia, Nadal Sidney, La Rosa Luciana, Mendes Bruno, Ferreira João, Macedo Guilherme, de Parades Vincent
Аннотация
BACKGROUND AND AIMS: Anal cancer incidence is rising, primarily due to increasing rates of human papillomavirus infection. High-resolution anoscopy (HRA) is the gold standard diagnostic modality for evaluating human papillomavirus-related anal lesions; however, its broader adoption is limited by subjectivity and interobserver variability. This study aims to develop and validate a deep learning-based trinary classification model that detects and classifies high-grade squamous intraepithelial lesion (HSIL), low-grade squamous intraepithelial lesion (LSIL), and non-dysplastic lesions (eg, inflammation) on HRA frames.
METHODS: A multicentric study was conducted to develop a convolutional neural network for automatic detection and classification of HSIL, LSIL, and non-dysplastic lesions. A You Only Look Once (YOLO)-v11 model was trained and validated on 191,951 frames (from 107 HRA procedures) containing histologically confirmed lesions, collected from 5 different imaging devices across 5 independent centers. Performance metrics (recall, precision, accuracy, and F1-score) were calculated at the object classification level as weighted averages across 5 confidence thresholds (0.45, 0.47, 0.50, 0.52, and 0.55).
RESULTS: For HSIL and LSIL, recall was 97.9% and 98.9%, and precision was 93.9% and 98.5%, respectively. For non-dysplastic lesions, recall was 98.7%, and precision was 96.0%. The overall classification accuracy on the test set was 88.1% (95% CI: 78.2-98.1).
CONCLUSION: This is the first interoperable artificial intelligence model capable of simultaneous detection and trinary classification of anal canal lesions (HSIL, LSIL, and non-dysplastic mimickers). By addressing key limitations of HRA, this model supports broader clinical applicability and represents a significant step toward real-world deployment and personalized artificial intelligence-assisted care.
METHODS: A multicentric study was conducted to develop a convolutional neural network for automatic detection and classification of HSIL, LSIL, and non-dysplastic lesions. A You Only Look Once (YOLO)-v11 model was trained and validated on 191,951 frames (from 107 HRA procedures) containing histologically confirmed lesions, collected from 5 different imaging devices across 5 independent centers. Performance metrics (recall, precision, accuracy, and F1-score) were calculated at the object classification level as weighted averages across 5 confidence thresholds (0.45, 0.47, 0.50, 0.52, and 0.55).
RESULTS: For HSIL and LSIL, recall was 97.9% and 98.9%, and precision was 93.9% and 98.5%, respectively. For non-dysplastic lesions, recall was 98.7%, and precision was 96.0%. The overall classification accuracy on the test set was 88.1% (95% CI: 78.2-98.1).
CONCLUSION: This is the first interoperable artificial intelligence model capable of simultaneous detection and trinary classification of anal canal lesions (HSIL, LSIL, and non-dysplastic mimickers). By addressing key limitations of HRA, this model supports broader clinical applicability and represents a significant step toward real-world deployment and personalized artificial intelligence-assisted care.