A hybrid method for fusion cardiac biomarkers and echocardiography videos in the experimental classification of Trypanosoma cruzi infection.

Background: Chagas disease, caused by Trypanosoma cruzi (T. cruzi), affects millions, mainly in Latin America, but is spreading globally due to migration and climate change. Early identification of infection is vital for preventing chronic complications, and analyzing multimodal cardiac function data may help detect T. cruzi infection early. This study presents a hybrid method based on late multimodal fusion for integrating machine learning (ML) and deep learning (DL) algorithms using cardiac biomarkers and echocardiography (ECHO) video to classify individuals with T. cruzi infection.

Methods: An experimental cohort of 96 ICR mice was utilized to study cardiac functionality in infected individuals. Ensemble feature selection (EFS) and weighted multiple kernel learning (MKL) methods were proposed to classify unimodal and multimodal cardiac biomarkers using an ML approach. In addition, two DL-based architectures were implemented for ECHO video classification. Finally, we integrated the ML and DL algorithms in a hybrid method based on late multimodal fusion.

Results: From 64 biomarkers, we identified 17 biomarkers as the most relevant using EFS. For ML, we trained algorithms with these selected biomarkers and obtained 73% accuracy (ACC), 84% area under the ROC curve (AUC), and an F1 score (F1) of 69% using unweighted MKL, and we noted that these results improved with weighted MKL, achieving ACC, AUC, and F1 of 80% on the test set. For the DL approach, we used ECHO for video classification, obtaining 65% ACC, 60% AUC, and F1 of 58%. Then, we integrated the ML and DL algorithms using the proposed hybrid method, which achieved 84% AUC, and 80% in ACC and F1.

Conclusions: We presented a hybrid method for fusion cardiac biomarkers and ECHO video using late multimodal fusion (ML + DL). This work has the potential to assist in the diagnosis and monitoring of T. cruzi infection by providing an automated tool capable of accurately identifying patients with CD.