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What We Do

Artificial Intelligence Powered Identification

Our cutting edge AI models accurately identify 7 key mosquito vectors, including primary malaria vectors, with over 90% accuracy in resource constrained settings. This breakthrough technology empowers effective malaria control and surveillance programs.

Revolutionizing Data Collection

VectorCam's user-friendly app and simple hardware streamline data collection, making surveillance accessible to everyone from medical entomologists to community health workers. Built on deep user insights, our platform ensures accurate and efficient data capture.

Intuitive Data Visualization

Data-driven insights for impact transform raw data into actionable intelligence. VectorCam's interactive dashboards and maps provide clear visualizations, empowering stakeholders to make informed decisions for targeted malaria prevention and control. 

Publications

  1. Dasari, S., Gopinath, B., Gaulke, C. J., Patel, S. M., Merali, K. K., Sunil Kumar, A., & Acharya, S. (2024). VectorCam- a handheld tool for rapid morphological identification of mosquito species for community-based malaria vector surveillance: A summative usability assessment (preprint). JMIR Human Factors. https://doi.org/10.2196/56605 
  2. Li, D., Hegde, S., Kumar, A., Zacharias, A., Mehta, P., Muktineni, V., Srim, S., Patel, S., Suin, M., Chellappa, R., & Acharya, S. (2024). Towards Transforming Malaria Vector Surveillance Using VectorBrain: A Novel Convolutional Neural Network for Mosquito Species, Sex, and Abdomen Status Identifications. https://doi.org/10.21203/rs.3.rs-4462833/v1 



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