LocoTrackAI: advanced convolutional neural network-based tool for monitoring locomotor activity in dengue-infected Aedes aegypti mosquitoes

Monitoring the locomotor activity of mosquitoes is vital for understanding their behavioral patterns and role in disease transmission. Studies investigating the locomotor activity of dengue-infected mosquitoes have faced several limitations, including confining mosquitoes within small tubes that restrict natural movement, discontinuous recordings that fail to provide detailed activity patterns, and the lack of open-source tools to effectively monitor mosquito locomotor activity. Here, we present LocoTrackAI, a robust artificial intelligence-based tool that leverages a convolutional neural network (CNN) and a multi-object tracking algorithm to comprehensively analyze the locomotor activity of Aedes aegypti mosquitoes from videos recorded using standard laboratory cages. LocoTrackAI automatically processes video datasets, tracks individual mosquito identities, and provides detailed locomotion results for both individual mosquitoes and group activity, including spatial distributions, movement patterns, heat maps, and activity ratios. The tool features a Skip Frame function to improve computational efficiency, adjustable movement thresholds for customized sensitivity, and a user-friendly interface that supports unsupervised batch processing, ensuring accuracy and flexibility for diverse research applications. The LocoTrackAI achieved 99.91% accuracy with a low centroid detection error of 0.22 pixels across 36,020 frames and demonstrated a 93.23% success rate in reassigning identities during 207 post-occlusion instances. Using LocoTrackAI, we analyzed the locomotor activity of dengue-infected and noninfected mosquitoes across 3.24 million recorded mosquito positions. Results revealed that infected mosquitoes exhibited significantly higher locomotor activity (p = 0.0009), with 95,726 movements (0.30 mean locomotor activity) compared to 42,173 movements (0.13 mean locomotor activity) in noninfected mosquitoes, representing more than 200% of the activity of noninfected mosquitoes. Additionally, spatial analysis indicated a more extensive and uniform distribution for infected mosquitoes, with entropy values of 3.38 for infected and 3.13 for noninfected mosquitoes. These findings suggest that dengue infection increases locomotor activity and spatial exploration, potentially enhancing the mosquitoes' capacity to locate hosts and spread the virus. Future studies could expand on this work by investigating the locomotor effects of other arboviruses and further developing tools to automate the analysis of feeding and other critical behaviors.