Portal de investigação
Artificial Intelligence and Colposcopy: Automatic Identification of Vaginal Squamous Cell Carcinoma Precursors
Data de publicação:
Autores da FMUP
Participantes de fora da FMUP
- Mascarenhas, M
- Alencoao, I
- Carinhas, MJ
- Martins, M
- Ribeiro, T
- Mendes, F
- Cardoso, P
- Almeida, MJ
- Mota, J
- Fernandes, J
- Ferreira, J
- Mascarenhas, T
- Zulmira, R
Unidades de investigação
Abstract
Background/Objectives: While human papillomavirus (HPV) is well known for its role in cervical cancer, it also affects vaginal cancers. Although colposcopy offers a comprehensive examination of the female genital tract, its diagnostic accuracy remains suboptimal. Integrating artificial intelligence (AI) could enhance the cost-effectiveness of colposcopy, but no AI models specifically differentiate low-grade (LSILs) and high-grade (HSILs) squamous intraepithelial lesions in the vagina. This study aims to develop and validate an AI model for the differentiation of HPV-associated dysplastic lesions in this region. Methods: A convolutional neural network (CNN) model was developed to differentiate HSILs from LSILs in vaginoscopy (during colposcopy) still images. The AI model was developed on a dataset of 57,250 frames (90% training/validation [including a 5-fold cross-validation] and 10% testing) obtained from 71 procedures. The model was evaluated based on its sensitivity, specificity, accuracy and area under the receiver operating curve (AUROC). Results: For HSIL/LSIL differentiation in the vagina, during the training/validation phase, the CNN demonstrated a mean sensitivity, specificity and accuracy of 98.7% (IC95% 96.7-100.0%), 99.1% (IC95% 98.1-100.0%), and 98.9% (IC95% 97.9-99.8%), respectively. The mean AUROC was 0.990 +/- 0.004. During testing phase, the sensitivity was 99.6% and 99.7% for both specificity and accuracy. Conclusions: This is the first globally developed AI model capable of HSIL/LSIL differentiation in the vaginal region, demonstrating high and robust performance metrics. Its effective application paves the way for AI-powered colposcopic assessment across the entire female genital tract, offering a significant advancement in women's healthcare worldwide.
Dados da publicação
- ISSN/ISSNe:
- 2072-6694, 2072-6694
- Tipo:
- Article
- Páginas:
- -
- Link para outro recurso:
- www.scopus.com
Cancers Multidisciplinary Digital Publishing Institute (MDPI)
Documentos
- Não há documentos
Filiações
Keywords
- vaginal neoplasms; HSIL; LSIL; colposcopy; artificial intelligence
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Citar a publicação
Mascarenhas M,Alencoao I,Carinhas MJ,Martins M,Ribeiro T,Mendes F,Cardoso P,Almeida MJ,Mota J,Fernandes J,Ferreira J,Macedo G,Mascarenhas T,Zulmira R. Artificial Intelligence and Colposcopy: Automatic Identification of Vaginal Squamous Cell Carcinoma Precursors. Cancers. 2024. 16. (20):3540. IF:5,200. (2).
