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<p>Human papillomavirus (HPV) infection presents neoplastic risks in both cervix and anus. High-resolution colposcopy/anoscopy is crucial for assessing these regions but has suboptimal accuracy. This study aims to develop a Convolutional Neural Network (CNN) to identify and differentiate low-grade (LSIL) and high grade (HSIL) squamous intraepithelial lesions, in the cervix and anus. A retrospective multicenter study was conducted to develop a CNN using 320 colposcopy and anoscopy examinations, from 3 device types. Dataset included 88,073 frames, categorized as LSIL or HSIL based on pathological analysis. The data was split into training/validation (90%, n = 79,265, including a threefold cross-validation) and test sets (10%, n = 8808). Diagnostic metrics including sensitivity, specificity, accuracy, positive and negative predictive values (PPV and NPV, respectively) and an area under the receiving operating and the precision-recall curves (AUC-ROC and AUC-PR) were calculated. During training/validation phase, the model achieved an average sensitivity for HSIL of 98.1% (IC95% 97.6–98.5%), specificity of 97.4% (IC95% 96.0–98.8%), PPV of 97.2% (IC95% 95.8–98.7%), NPV of 98.2% (IC95% 97.7–98.6%), and accuracy of 97.7% (IC95% 97.2–98.6%). The mean AUC-ROC and AUC-PR were both 0.98 ± 0.01. In the testing phase, performance metrics for HSIL were: sensitivity 99.0%, specificity 97.8%, PPV 97.6%, NPV 99.0%, and accuracy 98.3%. HPV infection impacts both cervical and anal region. This study developed a pioneering CNN to differentiate HSIL and LSIL in HPV-related dysplastic lesions, during cervical and anal examinations. This model achieved promising results, suggesting its potential to improve detection accuracy and cost-effectiveness in clinical practice.</p>

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Automated detection and classification of cervical and anal squamous cancer precursors using deep learning and multidevice colposcopy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Miguel Mascarenhas,
  • Miguel Martins,
  • Luís Barroso,
  • Lucas Spindler,
  • Nadia Fathallah,
  • Thiago Manzione,
  • Inês Alencoão,
  • Maria João Carinhas,
  • Tiago Ribeiro,
  • Francisco Mendes,
  • Pedro Cardoso,
  • Maria João Almeida,
  • Joana Mota,
  • Joana Fernandes,
  • João Ferreira,
  • Teresa Mascarenhas,
  • Sidney Nadal,
  • Rosa Zulmira,
  • Guilherme Macedo,
  • Vincent de Parades

摘要

Human papillomavirus (HPV) infection presents neoplastic risks in both cervix and anus. High-resolution colposcopy/anoscopy is crucial for assessing these regions but has suboptimal accuracy. This study aims to develop a Convolutional Neural Network (CNN) to identify and differentiate low-grade (LSIL) and high grade (HSIL) squamous intraepithelial lesions, in the cervix and anus. A retrospective multicenter study was conducted to develop a CNN using 320 colposcopy and anoscopy examinations, from 3 device types. Dataset included 88,073 frames, categorized as LSIL or HSIL based on pathological analysis. The data was split into training/validation (90%, n = 79,265, including a threefold cross-validation) and test sets (10%, n = 8808). Diagnostic metrics including sensitivity, specificity, accuracy, positive and negative predictive values (PPV and NPV, respectively) and an area under the receiving operating and the precision-recall curves (AUC-ROC and AUC-PR) were calculated. During training/validation phase, the model achieved an average sensitivity for HSIL of 98.1% (IC95% 97.6–98.5%), specificity of 97.4% (IC95% 96.0–98.8%), PPV of 97.2% (IC95% 95.8–98.7%), NPV of 98.2% (IC95% 97.7–98.6%), and accuracy of 97.7% (IC95% 97.2–98.6%). The mean AUC-ROC and AUC-PR were both 0.98 ± 0.01. In the testing phase, performance metrics for HSIL were: sensitivity 99.0%, specificity 97.8%, PPV 97.6%, NPV 99.0%, and accuracy 98.3%. HPV infection impacts both cervical and anal region. This study developed a pioneering CNN to differentiate HSIL and LSIL in HPV-related dysplastic lesions, during cervical and anal examinations. This model achieved promising results, suggesting its potential to improve detection accuracy and cost-effectiveness in clinical practice.