Video-based detection of the parasite responsible for causing Chagas disease

This study introduces a technique for identifying the presence of the parasite Trypanosoma cruzi in video recordings, the primary cause of Chagas disease. Early diagnosis—especially during the acute phase—is vital to prevent serious complications. The primary contribution of this work is a computational algorithm capable of detecting the presence of Chagas parasites in capillary tubes containing blood samples. While prior algorithms relied on stained blood samples, this study employs unstained blood samples, utilizing the motility of living parasites for detection. The proposed approach combines optical flow estimation using Farnebäck’s algorithm with a classification stage where several machine learning models were evaluated. Among these, the best performance in terms of accuracy was achieved by a convolutional neural network based on the ResNet-18 architecture. The dataset consists of 24 videos totaling 32 minutes of recording. The results demonstrate optimal performance, achieving a F1-score of 0.9383. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1139Dimensions.Open Alex.