INTRODUCTION: The COVID-19 infection, along with various drugs administered for its treatment may prolong ventricular repolarization and QT interval, increasing the risk of potentially fatal arrhythmias. Electrocardiogram (ECG) tracing with conventional devices increases health worker exposure to COVID-19. METHODS: In sequential tests, corrected QT interval (QTc) of electrocardiographic tracing obtained with AliveCor® single-lead (DI) Kardia Mobile (KM) was compared to QTc obtained with a 12-lead ECG. Authors evaluated numeric precision (proportion of measurements with a difference <10 msec), and consistency between the two devices in determining QTc prolongation (QTc ≥470 ms in male, or ≥480 ms in female), with kappa statistics. RESULTS: Records of 128 hospitalized patients with a suspected or confirmed COVID-19 diagnosis in the Hospital Universitario San Ignacio, Bogotá D.C. (Colombia) were included. The QTc interval measured with KM was similar to the interval measured with conventional ECG (442.5 ± 40.5 vs. 442.4 ± 40.2 ms, p: 0.986). Numeric precision was 93%. Concordance between the two devices for determining QTc prolongation was excellent, both in females (kappa: 0.901) and males (kappa: 0.896). CONCLUSION: Single-lead electrocardiographic tracing obtained with the AliveCor® Kardia Mobile allows accurate QTc interval assessment. Since KM use is fast and practical, it is ideal for reducing the exposure time of healthcare workers in the COVID-19 pandemic. The KM is capable of detecting prolonged QTc during treatment in COVID-19 patients. KEY WORDS: Kardia Mobile; AliveCor; corrected QT; QT interval; smart phone, ventricular arrhythmias, COVID-19.

Objectives: In this study, we aim to describe the diagnostic accuracy of two applications neural networks-based system and a visual algorithm performed by different evaluators to identify the manufacturer of electronic implantable cardiac devices by chest x-rays. Background: cardiac rhythm devices frequently require interrogation, and they have different software depending on the manufacturer. Currently, there are a visual algorithm and two applications based on artificial intelligence for the identification of the manufacturer from chest radiographs. Methods: Retrospective trial between January 2010 and December 2021 at a single institution. Chest radiographs were obtained from patients with cardiac devices; they were cropped and resized to 224 by 224 pixels. Then, they were analyzed using the applications Pacemaker ID ® with a cell phone, Pacemaker ID ® web and PPMnn ® web, and the visual algorithm CaRDIA-X ® performed by evaluators at different levels of training. Results: 400 radiographic images with cardiac devices were collected comprising 4 manufacturers and 40 different models. The agreement for Pacemaker ID ® with a cell phone was 90.6% ( p <0.001), for Pacemaker ID ® web was 81.2% ( p < 0.001); and for PPMnn ® web was 82% ( p < 0.001). The agreement from the CaRDIA-X ® algorithm performed by 4 evaluators ranged from 73.8% to 97.7% ( p < 0.001). Conclusions: The use of applications based on neural networks offers a good agreement in the identification of the manufacturer and is a tool for clinical use. In our paper, the visual algorithm has a better agreement in identifying the manufacturer and it doesn’t require much training.