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基于 Android 设备的资源匮乏地区宫颈癌筛查。

Andriod Device-Based Cervical Cancer Screening for Resource-Poor Settings.

机构信息

School of Information Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

NMAMIT, Nitte, 574110, India.

出版信息

J Digit Imaging. 2018 Oct;31(5):646-654. doi: 10.1007/s10278-018-0083-x.

Abstract

Visual inspection with acetic acid (VIA) is an effective, affordable and simple test for cervical cancer screening in resource-poor settings. But considerable expertise is needed to differentiate cancerous lesions from normal lesions, which is lacking in developing countries. Many studies have attempted automation of cervical cancer detection from cervix images acquired during the VIA process. These studies used images acquired through colposcopy or cervicography. However, colposcopy is expensive and hence is not feasible as a screening tool in resource-poor settings. Cervicography uses a digital camera to acquire cervix images which are subsequently sent to experts for evaluation. Hence, cervicography does not provide a real-time decision of whether the cervix is normal or not, during the VIA examination. In case the cervix is found to be abnormal, the patient may be referred to a hospital for further evaluation using Pap smear and/or biopsy. An android device with an inbuilt app to acquire images and provide instant results would be an obvious choice in resource-poor settings. In this paper, we propose an algorithm for analysis of cervix images acquired using an android device, which can be used for the development of decision support system to provide instant decision during cervical cancer screening. This algorithm offers an accuracy of 97.94%, a sensitivity of 99.05% and specificity of 97.16%.

摘要

醋酸肉眼观察(VIA)是一种在资源匮乏环境中用于宫颈癌筛查的有效、经济且简便的方法。但是,要从 VIA 过程中获得的宫颈图像中区分癌性病变与正常病变,需要相当的专业知识,而发展中国家往往缺乏这种知识。许多研究尝试从 VIA 过程中采集的宫颈图像自动检测宫颈癌。这些研究使用阴道镜或宫颈摄影术采集的图像。然而,阴道镜检查费用昂贵,因此作为一种筛查工具在资源匮乏的环境中并不可行。宫颈摄影术使用数码相机采集宫颈图像,然后将其发送给专家进行评估。因此,在 VIA 检查期间,宫颈摄影术无法实时判断宫颈是否正常。如果发现宫颈异常,患者可能会被转介到医院进行进一步的巴氏涂片和/或活组织检查。在资源匮乏的环境中,具有内置应用程序以采集图像并提供即时结果的安卓设备显然是一个更好的选择。在本文中,我们提出了一种用于分析安卓设备采集的宫颈图像的算法,该算法可用于开发决策支持系统,以便在宫颈癌筛查过程中提供即时决策。该算法的准确率为 97.94%,灵敏度为 99.05%,特异性为 97.16%。

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