醋酸视觉检查的开发和临床验证-使用宫颈图像的人工智能工具在南印度宫颈癌的筛查和治疗视觉筛查:一项试点研究。

IF 3.2 Q2 ONCOLOGY
JCO Global Oncology Pub Date : 2024-12-01 Epub Date: 2024-12-12 DOI:10.1200/GO.24.00146
Usha Rani Poli, Anirudh G Gudlavalleti, Jaya Bharadwaj Y, Hira B Pant, Varun Agiwal, G V S Murthy
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引用次数: 0

摘要

目的:印度的子宫颈癌负担巨大,据报告2020年有6万多人死亡。世卫组织消除宫颈癌全球战略的关键干预措施是,为90%被诊断患有宫颈病变的妇女提供治疗和护理。目前的筛查和治疗方法是资源有限的卫生保健系统的一种选择,其中通过乙酸应用目视检查(VIA)筛查子宫颈,然后在检测阳性的情况下由护士立即进行消融治疗。由于测试的主观性,这种方法常常导致过度治疗。通过使用新兴的计算机辅助视觉评估技术,使用人工智能(AI)工具对经膜阳性妇女进行分类,可以减少不必要的治疗。本研究的目的是(1)开发一种利用宫颈图像识别和分类宫颈造影阳性区域以确定是否适合消融治疗的VIA-AI工具,以及(2)了解该工具在指导护士在筛查和治疗宫颈筛查项目中决定治疗资格方面的效果。方法:这是一项探索性、介入性研究。VIA-AI工具是利用我们之前进行的筛选项目中收集的图像库中的深度学习AI开发的。然后,在一项正在进行的护士主导的VIA筛查项目中,对这种VIA- ai工具进行了试点测试。结果:使用VIA-AI工具对所有女性进行的宫颈特征比较评估显示临床准确率为76%。假阳性的感知挑战率为20%。结论:这种基于宫颈图像的新型VIA- ai算法在现实生活中显示出良好的效果,可以帮助减少资源受限情况下单次就诊VIA筛查和治疗方案的过度治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and Clinical Validation of Visual Inspection With Acetic Acid Application-Artificial Intelligence Tool Using Cervical Images in Screen-and-Treat Visual Screening for Cervical Cancer in South India: A Pilot Study.

Purpose: The burden of cervical cancer in India is enormous, with more than 60,000 deaths being reported in 2020. The key intervention in the WHO's global strategy for the elimination of cervical cancer is to aim for the treatment and care of 90% of women diagnosed with cervical lesions. The current screen-and-treat approach as an option for resource-limited health care systems where screening of the cervix with visual inspection with acetic acid application (VIA) is followed by immediate ablative treatment by nurses in the case of a positive test. This approach often results in overtreatment, owing to the subjective nature of the test. Unnecessary treatments can be diminished with the use of emerging computer-assisted visual evaluation technology, using artificial intelligence (AI) tool to triage VIA-positive women. The aim of this study was (1) to develop a VIA-AI tool using cervical images to identify and categorize the VIA-screen-positive areas for eligibility and suitability for ablative treatment, and (2) to understand the efficacy of the VIA-AI tool in guiding the nurses to decide on treatment eligibility in the screen-and-treat cervical screening program.

Methods: This was an exploratory, interventional study. The VIA-AI tool was developed using deep-learning AI from the image bank collected in our previously conducted screening programs. This VIA-AI tool was then pilot-tested in an ongoing nurse-led VIA screening program.

Results: A comparative assessment of the cervical features performed in all women using the VIA-AI tool showed clinical accuracy of 76%. The perceived challenge rate for false positives was 20%.

Conclusion: This novel cervical image-based VIA-AI algorithm showed promising results in real-life settings, and could help minimize overtreatment in single-visit VIA screening and treatment programs in resource-constrained situations.

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来源期刊
JCO Global Oncology
JCO Global Oncology Medicine-Oncology
CiteScore
6.70
自引率
6.70%
发文量
310
审稿时长
7 weeks
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