Chagas disease (CD) is a tropical parasitic disease that often remains asymptomatic but can lead to serious long-term cardiac complications. This study describes our contribution to the George B. Moody PhysioNet Challenge 2025, which focused on developing deep learning algorithms to detect CD using 12-lead electrocardiogram (ECG) data. We trained a convolutional neural network initialized with weights from ECGFounder, a foundation model pretrained on over 10 million ECGs. Model development used multinational datasets and addressed label noise by applying soft labels to ECGs with features suggestive of asymptomatic CD, such as right bundle branch block and atrial fibrillation. The model was evaluated using the true positive rate among the top 5% of predictions (TPR@5%), reflecting a scenario of resource-constrained deployment. Our team, UMC Utrecht, achieved a TPR@5% challenge score of 0.280, resulting in a 130th overall place. These findings underscore the potential of ECG-based tools for screening CD in settings with limited access to serological testing.