Innovations in cervical cancer diagnosis for low resource settings using advanced optical imaging and machine learning diagnostic algorithms.

该项目的主要目标是采用便携式，低成本，易于使用的袖珍大小的镜(开发 在其他资金下)，用于社区设置，并开发结合 新生血管、糖原耗竭和乙酰美白，为专家提供可比的诊断。这部作品 将由Third Stone Design，Inc.、杜克大学和肯尼亚医学研究公司合作完成 研究所。这项建议的具体目标是： 目标1(第一阶段)：通过设计连续放大机制和改进袖珍镜 改进设备工作流程集成，通过使用 一次性光学透明无菌套筒。供应商对我们之前开发的袖珍镜的反馈 一致建议增加一个滑块机构来控制粗略变焦和一个袖子消耗品 袖珍镜设计。 目标2(第一阶段)：用于宫颈癌前病变检测的自动算法和软件我们将改进 威盛使用一种带有嵌入式机器学习诊断算法的新型软件应用程序的特殊性 自动宫颈癌筛查。我们将应用和验证VIA和GIVI(绿色)的单独算法 照明血管成像)对使用Pocket进行的200名患者的临床研究获得的现有图像 镜检查。然后，我们将比较算法的性能与专家医生对相同算法的解释 图像，以病理学为金标准。 目标3(第二阶段)：记录肯尼亚使用袖珍镜的用户体验。我们将开发一种 直接在社区卫生保健环境中提供与文化相关的培训包。我们将收集量化和 定性数据，包括调查、深度访谈和来自幼稚提供者和患者的临床观察 并利用这些发现来改进袖珍镜在肯尼亚和 同时，告知AIM 4的临床研究。 目标4(第二阶段)：比较袖珍镜和目视检查的性能 用于对肯尼亚HPV阳性妇女进行分类的醋酸。我们将在400名HPV+患者中进行整群随机试验 在肯尼亚基苏木，妇女将标准分诊与使用袖珍镜的分诊进行比较。所有HPV+女性 将接受活检以确定不同的敏感性、特异性以及阳性和阴性预测值 分类策略。数据将被用来模拟算法的性能与镜专家的性能。 目标5(第二阶段)：评估以下项目的费用、增量成本效益和人口健康影响 基于人乳头瘤病毒的宫颈癌筛查计划和建议的分流策略。我们将确定 通过测量四种分诊策略的增量成本-效果比以及绝对和相对成本 成本和模拟人群健康结果(癌症病例、死亡和伤残调整寿命年)。

The broad goal of this project is to adapt a portable, low-cost, easy-to-use Pocket-sized Colposcope (developed under other funding) for use in a community setting, and develop automated algorithms that combine neovascularization, glycogen depletion and acetowhitening to provide comparable diagnosis to an expert. This work will be done in a collaboration between 3rd Stone Design, Inc, Duke University and Kenya Medical Research Institute. The specific aims of this proposal are: Aim 1 (Phase I): Improve Pocket colposcope by designing continuous magnification mechanism and improving device workflow integration to eliminate between-use disinfection through the use of a disposable optically clear sterile sleeve. Provider feedback on our previously developed Pocket colposcope has unanimously suggested the addition of a slider mechanism to control coarse zoom and a sleeve consumable to the Pocket colposcope design. Aim 2 (Phase I): Automated algorithms and software for cervical pre-cancer detection We will improve the specificity of VIA using a novel software application with embedded machine learning diagnostic algorithms for automated cervical cancer screening. We will apply and validate the individual algorithms for VIA and GIVI (green illumination vascular imaging) to existing images obtained from a 200-patient clinical study with the Pocket colposcope. We will then compare the performance of the algorithms to expert physician interpretation of the same images, with pathology serving as the gold standard. Aim 3 (Phase II): Document user experience with Pocket colposcope in Kenya. We will develop a culturally relevant training package directly in the community healthcare setting. We will collect quantitative and qualitative data including surveys, in-depth interviews, and clinic observations from both naive providers and patients and use these findings to and use these findings to improve the introduction of the Pocket colposcope in Kenya and simultaneously, inform the clinical investigations in Aim 4. Aim 4 (Phase II): Compare the performance of the Pocket colposcope to Visual Inspection with Acetic Acid for triage of HPV+ women in Kenya. We will carry out a cluster-randomized trial among 400 HPV+ women to compare the standard triage with that using the Pocket colposcope in Kisumu, Kenya. All HPV+ women will undergo biopsy to determine sensitivity, specificity and positive and negative predictive values of the different triage strategies. Data will be used to model the performance of the algorithm against that of expert colposcopists. Aim 5 (Phase II): Assess the costs, incremental cost-effectiveness and population health impact of HPV-based cervical cancer screening programs with proposed triage strategies. We will determine the incremental cost-effectiveness ratio and the absolute and relative costs for four triage strategies by measuring the costs and model population health outcomes (cancer cases, deaths and disability adjusted life years).