Poli Usha Rani, Gudlavalleti Anirudh G, Bharadwaj Y Jaya, Pant Hira B, Agiwal Varun, Murthy G V S
Public Health Foundation of India, Hyderabad, India.
Joly AI, Hyderabad, India.
JCO Glob Oncol. 2024 Dec;10:e2400146. doi: 10.1200/GO.24.00146. Epub 2024 Dec 12.
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.
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.
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%.
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.
印度宫颈癌负担巨大,2020年报告的死亡人数超过6万。世卫组织全球消除宫颈癌战略的关键干预措施是,目标是为90%被诊断为宫颈病变的女性提供治疗和护理。目前的筛查与治疗方法是资源有限的医疗保健系统的一种选择,即通过用醋酸涂抹进行视觉检查(VIA)筛查宫颈,检测结果呈阳性时由护士立即进行消融治疗。由于该检测的主观性,这种方法常常导致过度治疗。使用新兴的计算机辅助视觉评估技术,即利用人工智能(AI)工具对VIA检测呈阳性的女性进行分流,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的VIA-AI工具,以识别和分类VIA筛查呈阳性的区域,确定其是否适合进行消融治疗;(2)了解VIA-AI工具在指导护士决定筛查与治疗宫颈癌筛查项目中的治疗适用性方面的效果。
这是一项探索性干预研究。VIA-AI工具是利用我们之前开展的筛查项目收集的图像库中的深度学习人工智能开发的。然后,该VIA-AI工具在一个正在进行的由护士主导的VIA筛查项目中进行了试点测试。
使用VIA-AI工具对所有女性的宫颈特征进行的比较评估显示,临床准确率为76%。假阳性的感知挑战率为20%。
这种基于宫颈图像的新型VIA-AI算法在实际应用中显示出了有前景的结果,并且可以帮助在资源有限的情况下,在单次就诊的VIA筛查与治疗项目中尽量减少过度治疗。