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ByteSight Technologies

Team Members:
  • Ebenezer Armah
  • Zack Buono
  • Sophia Diaz
  • Karina Frank
  • Kiley Gersch
  • Monet Slinowsk
  • Jennifer Stevenson, PhD
  • Yvonne Linton, PhD
  • Mohamed Bayoh, PhD


ByteSight Technologies aims to decrease the global malaria burden via an innovative computer vision tool that rapidly identifies the dangerous mosquitoes that carry and transmit malaria. Malaria is one of the most devastating vector-borne diseases, resulting in approximately 228 million cases and 405,000 deaths per year. The most effective way to reduce malaria cases is to prevent them before they occur. Vector control programs can accomplish this task by identifying and eliminating the malaria-transmitting mosquitoes before they can spread disease. Effective vector control operation is dependent on accurate and timely vector surveillance, or the monitoring of the density and distribution of these dangerous mosquitoes. Although the thousands of mosquito species in existence may look the same to the naked eye, only 40 of these are capable of actually transmitting malaria. All vector control implementation decisions depend on knowing the type, location, density, and behaviors of these vectors. Therefore, in order to effectively deploy the correct interventions directed at mosquito populations in specific locations, these programs must have accurate, species-specific mosquito demographic data. Current vector surveillance practices are time- and labor-intensive, requiring highly trained entomologists to visually identify individual specimens manually. Entomologists’ average identification rate is approximately one mosquito per 20 minutes, with an average identification accuracy of ~66%. Our computer vision-based technology addresses the current gap in surveillance by improving identification accuracy to well over 90%, and by identifying individual specimens on the order of seconds, thereby increasing the availability of key vector surveillance data needed for effective vector control.

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