RESUMEN
Introduction: The Apple Watch valuably records event-based electrocardiograms (iECG) in children, as shown in recent studies by Paech et al. In contrast to adults, though, the automatic heart rhythm classification of the Apple Watch did not provide satisfactory results in children. Therefore, ECG analysis is limited to interpretation by a pediatric cardiologist. To surmount this difficulty, an artificial intelligence (AI) based algorithm for the automatic interpretation of pediatric Apple Watch iECGs was developed in this study. Methods: A first AI-based algorithm was designed and trained based on prerecorded and manually classified i.e., labeled iECGs. Afterward the algorithm was evaluated in a prospectively recruited cohort of children at the Leipzig Heart Center. iECG evaluation by the algorithm was compared to the 12-lead-ECG evaluation by a pediatric cardiologist (gold standard). The outcomes were then used to calculate the sensitivity and specificity of the Apple Software and the self-developed AI. Results: The main features of the newly developed AI algorithm and the rapid development cycle are presented. Forty-eight pediatric patients were enrolled in this study. The AI reached a specificity of 96.7% and a sensitivity of 66.7% for classifying a normal sinus rhythm. Conclusion: The current study presents a first AI-based algorithm for the automatic heart rhythm classification of pediatric iECGs, and therefore provides the basis for further development of the AI-based iECG analysis in children as soon as more training data are available. More training in the AI algorithm is inevitable to enable the AI-based iECG analysis to work as a medical tool in complex patients.
RESUMEN
Four years ago, we introduced the use of Castelli membrane tubes into our practice. Although initial reports in the literature and restrictions on indications from the manufacturer on use were extremely cautionary, we were able, by careful intraoperative technique and the use of postoperative antibiotics, to expand the use of the membrane tube and offer it to our entire patient population. To document this, we used a prospective study of 124 consecutive ears receiving a membrane tube. Follow-up study included clinical, audiometric, and tympanometric evaluation. We found the membrane tube to function extremely well in an extremely high percentage of ears, regardless of whether the ear contains serous or mucoid fluid. The advantages of membrane tubes include the lack of special care required, resultant high patient and parental acceptance, and the low incidence of draining ears with tubes in place.
