ArticleBiomedical engineering online2025
A hybrid method for fusion cardiac biomarkers and echocardiography videos in the experimental classification of Trypanosoma cruzi infection.
Article in Biomedical engineering online, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Cardiac imaging in Chagas cardiomyopathy for phenotypic characterisation and risk stratification.Heart failure reviews · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundChagas 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.
methodsAn 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.
resultsFrom 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.
conclusionsWe 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.