Johns Hopkins University Center for Bioengineering Innovation and Design, Baltimore, MD, United States.
JMIR Hum Factors. 2024 Aug 16;11:e56605. doi: 10.2196/56605.
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).
This study evaluates the usability of the VectorCam system among VHTs by assessing its efficiency, effectiveness, and satisfaction.
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.
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.
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.
疟疾每年影响近 2.5 亿人。具体来说，乌干达的负担最重，有 1300 万例病例和近 2 万人死亡。控制疟疾的传播依赖于媒介监测，这是一个在农村地区收集蚊子并分析媒介物种密度的系统，以便相应地规划干预措施。然而，这依赖于受过训练的昆虫学家，称为病媒控制官员（VCO），他们通过显微镜识别物种。全球昆虫学家短缺和这个费时的过程导致了严重的报告延迟。VectorCam 是一种基于人工智能的低成本工具，可以通过拍照识别蚊子的种类、性别和腹部状况，并将这些结果通过电子方式从监测点发送给决策者，从而将该过程简化为乡村卫生队（VHT）。
本研究通过评估 VectorCam 系统的效率、效果和满意度，评估 VHT 对该系统的可用性。
VectorCam 系统具有成像硬件和一个手机应用程序，旨在识别蚊子的种类。需要两个用户：（1）一个图像采集器，使用应用程序拍摄蚊子的图像；（2）一个加载器，用于从硬件上加载和卸载蚊子。为了培训和认证 VHT，VCO 确定了这两个角色的关键成功任务。在第一测试阶段（阶段 1），一名 VCO 和一名 VHT 配对，分别担任图像采集器或加载器的角色。之后，他们进行了角色交换。在第二阶段，两名 VHT 配对，模拟真实使用情况。记录了每位参与者拍摄每只蚊子的时间、关键错误和系统可用性量表（SUS）得分。
总共招募了 14 名男性和 6 名女性 VHT 成员，年龄在 20 至 70 岁之间，其中 12 名（60%）参与者有智能手机使用经验。阶段 1 和 2 中图像采集器的平均吞吐量值分别为 70（SD 30.3）秒和 56.1（SD 22.9）秒/只蚊子，表明拍摄一托盘蚊子的时间缩短了。阶段 1 和 2 中加载器的平均吞吐量值分别为 50.0 和 55.7 秒/只蚊子，表明时间略有增加。在效果方面，图像采集器在阶段 1 中有 8%（6/80）的关键错误，而加载器有 13%（10/80）的关键错误。在第二阶段，图像采集器（VHT 对）有 14%（11/80）的关键错误，而加载器（VHT 对）有 12%（19/160）的关键错误。系统的平均 SUS 得分为 70.25，表明可用性良好。克鲁斯卡尔-沃利斯分析表明，性别或是否有智能手机使用经验的用户在 SUS（H 值）得分方面没有显著差异。
VectorCam 是一种在乌干达农村地区简化蚊子标本现场鉴定的可用系统。即将进行的设计更新将解决用户和观察员的关切。
