This paper introduces the solution of team cardio_Basel25 to the PhysioNet Challenge 2025. Chagas disease, caused by Trypanosoma cruzi, remains underdiagnosed in low-resource settings where serological testing is limited. Given the widespread availability of electrocardiography (ECG) and the conduction abnormalities characteristic of Chagas cardiomyopathy and ECG-based artificial intelligence offers a scalable alternative for early detection. A deep transfer learning model was developed based on a pretrained InceptionTime architecture, and fine-tuned on Brazilian (CODE-15%, Sami-Trop) and European (PTB-XL) ECG datasets. Recordings were preprocessed with filtering, downsampling, normalization, and 5-second segment extraction, with data augmentation applied during training. The training loss consisted of binary cross-entropy with a penalization term to emphasize the challenge metric. Inference combined predictions across multiple ECG segments and models. The approach achieved a cross-validation score of 0.42 and a score of 0.382 on the validation set, ranking 27th on the official leaderboard. These findings demonstrate the feasibility of deep transfer learning for ECG-based Chagas screening and its potential to expand diagnostic access in underserved regions.