背景:疟疾每年影响近2.5亿人。具体来说,乌干达的负担是最高的,1300万例,近2万人死亡。控制疟疾的传播依赖于媒介监测,收集的蚊子在农村地区的媒介物种密度进行分析,以制定相应的干预措施。然而,这依赖于训练有素的昆虫学家,称为媒介控制官员(VCO),他们通过显微镜识别物种。昆虫学家的全球短缺以及这种耗时的过程导致了严重的报告延迟。VectorCam是一种低成本的基于人工智能的工具,可以识别蚊子的物种,性别,和腹部状态,并将这些结果从监测点以电子方式发送给决策者,从而对乡村卫生队(VHTs)的流程进行解链。
目的:本研究通过评估VectorCam系统在VHT中的效率来评估其可用性,有效性,和满意度。
方法:VectorCam系统具有成像硬件和旨在识别蚊子种类的手机应用程序。需要两个用户:(1)使用应用程序捕获蚊子图像的成像器,以及(2)从硬件加载和卸载蚊子的加载器。确定了两个角色的关键成功任务,哪些VCO用来训练和认证VHT。在第一阶段(第一阶段),VCO和VHT配对以承担成像仪或加载器的角色。之后,他们交换了。在第二阶段,两个VHT配对,模仿真正的用途。拍摄每只蚊子的时间,严重错误,记录每个参与者的系统可用性量表(SUS)评分。
结果:总体而言,招募了14名20至70岁的男性和6名女性VHT成员,其中12名(60%)参与者有智能手机使用经验。成像仪第1阶段和第2阶段的平均吞吐量值分别为每个蚊子70(SD30.3)秒和56.1(SD22.9)秒,分别,表明对蚊子托盘成像的时间长度减少。装载机第1阶段和第2阶段的平均吞吐量值分别为每只蚊子50.0秒和55.7秒,分别,表明时间略有增加。在有效性方面,在第1阶段,成像仪有8%(6/80)的关键误差,加载器有13%(10/80)的关键误差.在阶段2中,成像器(对于VHT对)具有14%(11/80)的关键误差,并且加载器(对于VHT对)具有12%(19/160)的关键误差。系统的平均SUS评分为70.25,表明正的可用性。Kruskal-Wallis分析表明,性别或具有和不具有智能手机使用经验的用户之间的SUS(H值)得分没有显着差异。
结论:VectorCam是一种可用的系统,用于在乌干达农村地区对蚊子标本进行现场鉴定。即将进行的设计更新将解决用户和观察者的担忧。
BACKGROUND: Malaria impacts nearly 250 million individuals annually. Specifically, Uganda has one of the highest burdens, with 13 million cases and nearly 20,000 deaths. Controlling the spread of malaria relies on vector surveillance, a system where collected mosquitos are analyzed for vector species\' density in rural areas to plan interventions accordingly. However, this relies on trained entomologists known as vector control officers (VCOs) who identify species via microscopy. The global shortage of entomologists and this time-intensive process cause significant reporting delays. VectorCam is a low-cost artificial intelligence-based tool that identifies a mosquito\'s species, sex, and abdomen status with a picture and sends these results electronically from surveillance sites to decision makers, thereby deskilling the process to village health teams (VHTs).
OBJECTIVE: This
study evaluates the usability of the VectorCam system among VHTs by assessing its efficiency, effectiveness, and satisfaction.
METHODS: The VectorCam system has imaging hardware and a phone app designed to identify mosquito species. Two users are needed: (1) an imager to capture images of mosquitos using the app and (2) a loader to load and unload mosquitos from the hardware. Critical success tasks for both roles were identified, which VCOs used to train and certify VHTs. In the first testing phase (phase 1), a VCO and a VHT were paired to assume the role of an imager or a loader. Afterward, they swapped. In phase 2, two VHTs were paired, mimicking real use. The time taken to image each mosquito, critical errors, and System Usability Scale (SUS) scores were recorded for each participant.
RESULTS: Overall, 14 male and 6 female VHT members aged 20 to 70 years were recruited, of which 12 (60%) participants had smartphone use experience. The average throughput values for phases 1 and 2 for the imager were 70 (SD 30.3) seconds and 56.1 (SD 22.9) seconds per mosquito, respectively, indicating a decrease in the length of time for imaging a tray of mosquitos. The loader\'s average throughput values for phases 1 and 2 were 50.0 and 55.7 seconds per mosquito, respectively, indicating a slight increase in time. In terms of effectiveness, the imager had 8% (6/80) critical errors and the loader had 13% (10/80) critical errors in phase 1. In phase 2, the imager (for VHT pairs) had 14% (11/80) critical errors and the loader (for VHT pairs) had 12% (19/160) critical errors. The average SUS score of the system was 70.25, indicating positive usability. A Kruskal-Wallis analysis demonstrated no significant difference in SUS (H value) scores between genders or users with and without smartphone use experience.
CONCLUSIONS: VectorCam is a usable system for deskilling the in-field identification of mosquito specimens in rural Uganda. Upcoming design updates will address the concerns of users and observers.