Bai Shaowen, Shi Liang, Yang Kun
Key Laboratory of National Health and Family Planning Commission on Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, China.
School of Public Health, Nanjing Medical University, Nanjing, China.
Pest Manag Sci. 2025 Feb;81(2):527-539. doi: 10.1002/ps.8473. Epub 2024 Oct 18.
Vector-borne diseases (VBDs) represent a critical global public health concern, with approximately 80% of the world's population at risk of one or more VBD. Manual disease vector identification is time-consuming and expert-dependent, hindering disease control efforts. Deep learning (DL), widely used in image, text, and audio tasks, offers automation potential for disease vector identification. This paper explores the substantial potential of combining DL with disease vector identification. Our aim is to comprehensively summarize the current status of DL in disease vector identification, covering data collection, data preprocessing, model construction, evaluation methods, and applications in identification spanning from species classification to object detection and breeding site identification. We also discuss the challenges and possible prospects for DL in disease vector identification for further research. © 2024 Society of Chemical Industry.
媒介传播疾病（VBDs）是全球公共卫生的重大关切问题，全球约80%的人口面临感染一种或多种媒介传播疾病的风险。人工识别疾病媒介既耗时又依赖专家，阻碍了疾病控制工作。深度学习（DL）广泛应用于图像、文本和音频任务，为疾病媒介识别提供了自动化潜力。本文探讨了将深度学习与疾病媒介识别相结合的巨大潜力。我们的目的是全面总结深度学习在疾病媒介识别中的现状，涵盖数据收集、数据预处理、模型构建、评估方法，以及在从物种分类到目标检测和繁殖地识别等识别领域的应用。我们还讨论了深度学习在疾病媒介识别中面临的挑战和可能的前景，以供进一步研究。© 2024化学工业协会。
