PDF(Pubmed)
Abstract：
OBJECTIVE: Visual inspection with acetic acid (VIA) is a low-cost approach for cervical cancer screening used in most low- and middle-income countries (LMICs) but, similar to other visual tests, is subjective and requires sustained training and quality assurance. We developed, trained, and validated an artificial-intelligence-based \"Automated Visual Evaluation\" (AVE) tool that can be adapted to run on smartphones to assess smartphone-captured images of the cervix and identify precancerous lesions, helping augment VIA performance.
METHODS: Prospective study.
METHODS: Eight public health facilities in Zambia.
METHODS: A total of 8204 women aged 25-55.
METHODS: Cervical images captured on commonly used low-cost smartphone models were matched with key clinical information including human immunodeficiency virus (HIV) and human papillomavirus (HPV) status, plus histopathology analysis (where applicable), to develop and train an AVE algorithm and evaluate its performance for use as a primary screen and triage test for women who are HPV positive.
METHODS: Area under the receiver operating curve (AUC); sensitivity; specificity.
RESULTS: As a general population screening tool for cervical precancerous lesions, AVE identified cases of cervical precancerous and cancerous (CIN2+) lesions with high performance (AUC = 0.91, 95% confidence interval [CI] = 0.89-0.93), which translates to a sensitivity of 85% (95% CI = 81%-90%) and specificity of 86% (95% CI = 84%-88%) based on maximizing the Youden\'s index. This represents a considerable improvement over naked eye VIA, which as per a meta-analysis by the World Health Organization (WHO) has a sensitivity of 66% and specificity of 87%. For women living with HIV, the AUC of AVE was 0.91 (95% CI = 0.88-0.93), and among those testing positive for high-risk HPV types, the AUC was 0.87 (95% CI = 0.83-0.91).
CONCLUSIONS: These results demonstrate the feasibility of utilizing AVE on images captured using a commonly available smartphone by nurses in a screening program, and support our ongoing efforts for moving to more broadly evaluate AVE for its clinical sensitivity, specificity, feasibility, and acceptability across a wider range of settings. Limitations of this study include potential inflation of performance estimates due to verification bias (as biopsies were only obtained from participants with visible aceto-white cervical lesions) and due to this being an internal validation (the test data, while independent from that used to develop the algorithm was drawn from the same study).
METHODS: Prospective study.
METHODS: Eight public health facilities in Zambia.
METHODS: A total of 8204 women aged 25-55.
METHODS: Cervical images captured on commonly used low-cost smartphone models were matched with key clinical information including human immunodeficiency virus (HIV) and human papillomavirus (HPV) status, plus histopathology analysis (where applicable), to develop and train an AVE algorithm and evaluate its performance for use as a primary screen and triage test for women who are HPV positive.
METHODS: Area under the receiver operating curve (AUC); sensitivity; specificity.
RESULTS: As a general population screening tool for cervical precancerous lesions, AVE identified cases of cervical precancerous and cancerous (CIN2+) lesions with high performance (AUC = 0.91, 95% confidence interval [CI] = 0.89-0.93), which translates to a sensitivity of 85% (95% CI = 81%-90%) and specificity of 86% (95% CI = 84%-88%) based on maximizing the Youden\'s index. This represents a considerable improvement over naked eye VIA, which as per a meta-analysis by the World Health Organization (WHO) has a sensitivity of 66% and specificity of 87%. For women living with HIV, the AUC of AVE was 0.91 (95% CI = 0.88-0.93), and among those testing positive for high-risk HPV types, the AUC was 0.87 (95% CI = 0.83-0.91).
CONCLUSIONS: These results demonstrate the feasibility of utilizing AVE on images captured using a commonly available smartphone by nurses in a screening program, and support our ongoing efforts for moving to more broadly evaluate AVE for its clinical sensitivity, specificity, feasibility, and acceptability across a wider range of settings. Limitations of this study include potential inflation of performance estimates due to verification bias (as biopsies were only obtained from participants with visible aceto-white cervical lesions) and due to this being an internal validation (the test data, while independent from that used to develop the algorithm was drawn from the same study).
摘要：
目的：在大多数低收入和中等收入国家（LMICs），乙酸目视检查（VIA）是一种低成本的宫颈癌筛查方法，但是，类似于其他视觉测试，是主观的，需要持续的培训和质量保证。我们开发了,受过训练，并验证了基于人工智能的“自动视觉评估”（AVE）工具，该工具可适用于在智能手机上运行，以评估智能手机捕获的子宫颈图像并识别癌前病变，帮助提高VIA性能。
方法：前瞻性研究。
方法：赞比亚的8个公共卫生设施。
方法：共8204名25-55岁女性。
方法：在常用的低成本智能手机模型上捕获的宫颈图像与关键临床信息相匹配，包括人类免疫缺陷病毒（HIV）和人乳头瘤病毒（HPV）状态，加上组织病理学分析（如适用），开发和训练AVE算法，并评估其性能，以用作HPV阳性女性的主要筛查和分诊测试。
方法：受试者工作曲线下面积（AUC）；灵敏度；特异性。
结果：作为宫颈癌前病变的一般人群筛查工具，AVE识别的宫颈癌前病变和癌（CIN2+）病变具有高性能（AUC=0.91，95％置信区间[CI]=0.89-0.93），基于最大化Youden指数，其灵敏度为85%(95%CI=81%-90%)，特异性为86%(95%CI=84%-88%)。这代表了一个相当大的改进，比肉眼直视，根据世界卫生组织（WHO）的荟萃分析，其敏感性为66％，特异性为87％。对于感染艾滋病毒的妇女来说，AVE的AUC为0.91(95%CI=0.88-0.93)，在高危HPV检测呈阳性的人群中，AUC为0.87(95%CI=0.83-0.91)。
结论：这些结果证明了在筛查计划中使用护士使用常用智能手机捕获的图像上使用AVE的可行性，并支持我们正在进行的努力，以更广泛地评估AVE的临床敏感性，特异性，可行性,以及在更广泛的环境中的可接受性。这项研究的局限性包括由于验证偏差而导致的性能估计的潜在膨胀（因为活检仅来自可见的aceto-white宫颈病变的参与者），并且由于这是内部验证（测试数据，虽然独立于用于开发算法的算法来自同一研究)。
方法：前瞻性研究。
方法：赞比亚的8个公共卫生设施。
方法：共8204名25-55岁女性。
方法：在常用的低成本智能手机模型上捕获的宫颈图像与关键临床信息相匹配，包括人类免疫缺陷病毒（HIV）和人乳头瘤病毒（HPV）状态，加上组织病理学分析（如适用），开发和训练AVE算法，并评估其性能，以用作HPV阳性女性的主要筛查和分诊测试。
方法：受试者工作曲线下面积（AUC）；灵敏度；特异性。
结果：作为宫颈癌前病变的一般人群筛查工具，AVE识别的宫颈癌前病变和癌（CIN2+）病变具有高性能（AUC=0.91，95％置信区间[CI]=0.89-0.93），基于最大化Youden指数，其灵敏度为85%(95%CI=81%-90%)，特异性为86%(95%CI=84%-88%)。这代表了一个相当大的改进，比肉眼直视，根据世界卫生组织（WHO）的荟萃分析，其敏感性为66％，特异性为87％。对于感染艾滋病毒的妇女来说，AVE的AUC为0.91(95%CI=0.88-0.93)，在高危HPV检测呈阳性的人群中，AUC为0.87(95%CI=0.83-0.91)。
结论：这些结果证明了在筛查计划中使用护士使用常用智能手机捕获的图像上使用AVE的可行性，并支持我们正在进行的努力，以更广泛地评估AVE的临床敏感性，特异性，可行性,以及在更广泛的环境中的可接受性。这项研究的局限性包括由于验证偏差而导致的性能估计的潜在膨胀（因为活检仅来自可见的aceto-white宫颈病变的参与者），并且由于这是内部验证（测试数据，虽然独立于用于开发算法的算法来自同一研究)。
