AI-Based Identification Method for Cervical Transformation Zone Within Digital Colposcopy: Development and Multicenter Validation Study.

IF 3.3 Q2 ONCOLOGY
JMIR Cancer Pub Date : 2025-03-31 DOI:10.2196/69672
Tong Wu, Yuting Wang, Xiaoli Cui, Peng Xue, Youlin Qiao
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引用次数: 0

Abstract

Background: In low- and middle-income countries, cervical cancer remains a leading cause of death and morbidity for women. Early detection and treatment of precancerous lesions are critical in cervical cancer prevention, and colposcopy is a primary diagnostic tool for identifying cervical lesions and guiding biopsies. The transformation zone (TZ) is where a stratified squamous epithelium develops from the metaplasia of simple columnar epithelium and is the most common site of precancerous lesions. However, inexperienced colposcopists may find it challenging to accurately identify the type and location of the TZ during a colposcopy examination.

Objective: This study aims to present an artificial intelligence (AI) method for identifying the TZ to enhance colposcopy examination and evaluate its potential clinical application.

Methods: The study retrospectively collected data from 3616 women who underwent colposcopy at 6 tertiary hospitals in China between 2019 and 2021. A dataset from 4 hospitals was collected for model conduction. An independent dataset was collected from the other 2 geographic hospitals to validate model performance. There is no overlap between the training and validation datasets. Anonymized digital records, including each colposcopy image, baseline clinical characteristics, colposcopic findings, and pathological outcomes, were collected. The classification model was proposed as a lightweight neural network with multiscale feature enhancement capabilities and designed to classify the 3 types of TZ. The pretrained FastSAM model was first implemented to identify the location of the new squamocolumnar junction for segmenting the TZ. Overall accuracy, average precision, and recall were evaluated for the classification and segmentation models. The classification performance on the external validation was assessed by sensitivity and specificity.

Results: The optimal TZ classification model performed with 83.97% classification accuracy on the test set, which achieved average precision of 91.84%, 89.06%, and 95.62% for types 1, 2, and 3, respectively. The recall and mean average precision of the TZ segmentation model were 0.78 and 0.75, respectively. The proposed model demonstrated outstanding performance in predicting 3 types of the TZ, achieving the sensitivity with 95% CIs for TZ1, TZ2, and TZ3 of 0.78 (0.74-0.81), 0.81 (0.78-0.82), and 0.8 (0.74-0.87), respectively, with specificity with 95% CIs of 0.94 (0.92-0.96), 0.83 (0.81-0.86), and 0.91 (0.89-0.92), based on a comprehensive external dataset of 1335 cases from 2 of the 6 hospitals.

Conclusions: Our proposed AI-based identification system classified the type of cervical TZs and delineated their location on multicenter, colposcopic, high-resolution images. The findings of this study have shown its potential to predict TZ types and specific regions accurately. It was developed as a valuable assistant to encourage precise colposcopic examination in clinical practice.

基于人工智能的数字阴道镜宫颈转化区的识别方法:开发及多中心验证研究。
背景:在低收入和中等收入国家,子宫颈癌仍然是妇女死亡和发病的主要原因。早期发现和治疗癌前病变是预防宫颈癌的关键,阴道镜检查是确定宫颈病变和指导活检的主要诊断工具。转化区(TZ)是单层柱状上皮化生形成的层状鳞状上皮,是癌前病变最常见的部位。然而,经验不足的阴道镜医生可能会发现在阴道镜检查中准确识别TZ的类型和位置是一项挑战。目的:本研究旨在提出一种人工智能(AI)方法来识别TZ,以加强阴道镜检查,并评估其潜在的临床应用价值。方法:回顾性收集2019年至2021年在中国6家三级医院接受阴道镜检查的3616名女性的数据。收集了4家医院的数据集进行模型传导。从另外两家地理医院收集独立数据集来验证模型的性能。训练数据集和验证数据集之间没有重叠。收集匿名数字记录,包括每张阴道镜图像、基线临床特征、阴道镜检查结果和病理结果。该分类模型是一种具有多尺度特征增强能力的轻量级神经网络,用于对三种类型的TZ进行分类。首先使用预训练的FastSAM模型来识别新的鳞状柱状连接的位置,以便对TZ进行分割。对分类和分割模型的总体准确率、平均准确率和召回率进行了评估。通过灵敏度和特异度对外部验证的分类效果进行评价。结果:最优TZ分类模型在测试集上的分类准确率为83.97%,其中类型1、类型2和类型3的平均准确率分别为91.84%、89.06%和95.62%。TZ分割模型的召回率和平均精度分别为0.78和0.75。该模型在预测三种类型TZ方面表现出色,基于6家医院中2家的1335例病例的综合外部数据,TZ1、TZ2和TZ3的95% ci分别为0.78(0.74-0.81)、0.81(0.78-0.82)和0.8(0.74-0.87),灵敏度为95% ci为0.94(0.92-0.96)、0.83(0.81-0.86)和0.91(0.89-0.92)。结论:我们提出的基于人工智能的识别系统对宫颈TZs的类型进行了分类,并在多中心、阴道镜、高分辨率图像上描绘了它们的位置。本研究结果表明,该方法具有准确预测TZ类型和特定区域的潜力。在临床实践中,它作为一种有价值的辅助工具来鼓励精确的阴道镜检查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JMIR Cancer
JMIR Cancer ONCOLOGY-
CiteScore
4.10
自引率
0.00%
发文量
64
审稿时长
12 weeks
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