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10-Minute Presentation

Medical, Urban, and Veterinary Entomology

10-min: MUVE, Veterinary Entomology and Related Disease Vector Research

Artificial intelligence applied to vector identification and monitoring

Wednesday, November 13, 2024
10:06 AM – 10:18 AM MST
Location: Phoenix Convention Center, 221 C, PCC
Oral Presentation
Recent advances in artificial intelligence and machine learning, such as convolutional neural networks (CNNs) for image recognition, offer promising methods to differentiate mosquito species. This study presents the performance metrics of IDX, Vectech’s AI system for mosquito identification, within Maryland’s mosquito control program. Over 14 weeks in 2023, specimens were collected from 12 CDC gravid trap sites in Anne Arundel County, identified morphologically by an entomologist, and imaged using the IDX system. The system’s accuracy was assessed by comparing its outputs to those of the entomologist. A total of 2,591 mosquitoes, representing 14 species (10 included in the algorithm), were collected and imaged. The micro average accuracy of IDX was 94.9%. For species with fewer than 30 specimens, the macro average accuracy was 79%, while excluding these, it was 93%. These performance analyses are crucial for improving species-specific accuracy. Vectech aims to enhance the IDX system for remote sensing and rapid identification, boosting the surveillance capacity of vector control organizations. These advancements highlight the utility of AI in modern entomology and its potential to support global vector surveillance and control programs, thereby reducing the time between vector emergence and implementation of control measures.