@article{103130,
  keywords = {smartphone-based diagnostics, Chagas, Chagas disease, Artificial Intelligence, Validation studies},
  author = {GEBRAN CH KJ and LEYVA JL and DEMOLDER A and KRESNAKOVA V and Omaña-Ávila ÓD and MILANO-MARTÍNEZ JM and GÓMEZ-FERNÁNDEZ JM and RAFAJDUS A and Forero-Peña DA and HERMAN R and Mendoza I},
  title = {Smartphone-based AI-enhanced ECG Detection of Left Ventricular Systolic Dysfunction in Chagas Disease: External Validation in a Resource-limited Setting},
  abstract = {<p><strong>Highlights</strong></p>

<ul>
	<li>The artificial intelligence-enhanced electrocardiographic (AI-enhanced ECG) model achieved high sensitivity and negative predictive values for detecting left ventricular systolic dysfunction in Chagas disease patients using photographed paper-based ECGs.</li>
	<li>Model performance remained robust across ECGs of varying quality and in populations outside the training dataset.</li>
	<li>The smartphone-compatible AI-enhanced ECG offers a scalable, low-cost solution for the early detection of cardiac dysfunction in low-resource settings.</li>
</ul>
},
  year = {2025},
  journal = {Journal of Cardiac Failure - Intersections},
  volume = {1},
  pages = {176-180},
  month = {11/2025},
  publisher = {Elsevier BV},
  issn = {3050-6611},
  url = {https://pdf.sciencedirectassets.com/789132/1-s2.0-S3050661125X00034/1-s2.0-S3050661125000383/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEJr%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCIAeEp7y8xiBTdiqhzl140WcMoOUXYnfdfeCgOFMR1QP8AiAnP88gucqj%2BC},
  doi = {10.1016/j.yjcafi.2025.08.010},
  language = {ENG},
}