AI Sniffs Out Snake Venom in Minutes

A groundbreaking laboratory sensor, enhanced by artificial intelligence, can now distinguish between cobra and viper venom with remarkable speed and accuracy. Developed by researchers, this system analyzes changes in liquid crystals exposed to venom.

The MobileNetV3-Large AI model achieved 95% classification accuracy in a rapid four-minute sensing time. It also demonstrated the ability to detect venom at concentrations as low as 45 micrograms per millilitre.

Snakebites pose a significant public health challenge, particularly in rural areas. In Iran alone, between 4,500 and 7,000 bites occur annually. Accurate venom identification is crucial for guiding the appropriate antivenom selection, minimizing risks associated with less effective polyvalent treatments.

This new sensor operates by observing how venom disrupts the alignment of liquid crystal molecules, creating distinct optical patterns. Deep learning algorithms analyze these patterns, offering a simpler and more cost-effective diagnostic method compared to existing approaches.

While the sensor represents a significant proof of concept, its developers emphasize the need for further validation. Extensive testing under clinically relevant conditions is required to confirm its readiness for routine use in diagnosing snakebite patients.