Mosquito-borne diseases represent an escalating global health threat, driven by climate change, urbanization, and the spread of invasive vectors into new territories. Effective surveillance is constrained by a critical taxonomic impediment: the rate of specimen collection far outpaces the capacity of expert entomologists to process and identify trap catches. To address this bottleneck we developed MosAICo, an integrated AI-powered ecosystem for automated mosquito species identification designed for real-world, national-scale entomological surveillance. The system combines a standardized benchtop imaging device with MosAICo-Net, a deep learning pipeline enabling efficient and principled open-set recognition and uncertainty quantification. Trained and evaluated on 12, 499 specimens spanning 15 species collected across Italy, the model identifies seven priority vector species while explicitly rejecting out-of-distribution specimens. On a geographically stratified held-out test set, MosAICo-Net achieved over 90% accuracy on target species, and an AUROC of 0.96 for out-of-distribution detection. Field validation across 20 Italian surveillance sites confirmed these results: 94% micro accuracy on 1, 470 field-collected target specimens and strong agreement with expert manual counts ([Formula] = 0.66). To assess cross-geographic generalizability, the system was further evaluated on 118 Aedes albopictus specimens collected at the fringe of the species invasion front in Ghana: a 97.4% accuracy with only a single specimen escalated to expert review, suggests that MosAICo is well-suited for deployment in distant and epidemiologically critical regions. The system processes up to 82 specimens per image, matching expert throughput at constant speed regardless of taxonomic complexity. By embedding uncertainty-aware AI within a standardized hardware-software pipeline, MosAICo acts as a scalable force multiplier for public health entomology, freeing expert attention for rare, invasive, or ambiguous specimens that require human validation.
1. 蚊媒疾病正成为日益严峻的全球健康威胁，其驱动因素包括气候变化、城市化进程以及入侵性病媒向新领地的扩散。有效的监测工作受到一个关键分类学瓶颈的制约：标本采集速度远超昆虫学专家处理和鉴定诱捕样本的能力。为突破这一瓶颈，我们开发了MosAICo——一个集成人工智能的自动化蚊种识别生态系统，专为现实世界国家级昆虫学监测设计。该系统将标准化台式成像设备与MosAICo-Net深度学习流程相结合，实现了高效且原理清晰的开放集识别与不确定性量化。模型基于意大利境内采集的12,499份标本（涵盖15个物种）进行训练与评估，能够识别七种重点病媒蚊种，同时明确拒绝分布外样本。在地理分层的独立测试集上，MosAICo-Net对目标物种的识别准确率超过90%，分布外检测的AUROC值达0.96。在意大利20个监测点的实地验证中进一步证实了这些结果：对1,470份野外采集的目标标本实现94%的微观准确率，且与专家人工计数高度吻合（[公式] = 0.66）。为评估跨地域泛化能力，该系统还对加纳物种入侵前沿边缘采集的118份白纹伊蚊标本进行了测试：97.4%的准确率且仅单份标本需提交专家复核，表明MosAICo非常适合部署在偏远且流行病学关键区域。该系统单张图像可处理多达82份标本，处理速度恒定且不受分类复杂度影响，与专家吞吐量相当。通过将不确定性感知人工智能嵌入标准化软硬件流程，MosAICo成为公共卫生昆虫学领域的可扩展力量倍增器，使专家能专注于需要人工验证的稀有、入侵性或模糊标本。
Sarleti, N. et al. · CC-BY 4.0