Real-Time Remote-Mentored Echocardiography in Management of Newborns with Critical Congenital Heart Defects

Abstract

Background: The management of suspected critical congenital heart defects (CCHD) relies on timely echocardiographic diagnosis. The availability of experienced echocardiographers is limited or even non-existent in many hospitals with obstetric units. This study evaluates remote-mentored echocardiography performed by physicians without experience in imaging of congenital heart defects (CHD). Methods: The setup included a pediatric cardiologist in a separate room, guiding a physician without experience in echocardiographic imaging of CHD in the examination of a symptomatic newborn. This remote-mentoring pair was blinded to the diagnosis of the newborn and presented with a simplified patient history. The echocardiographic images were streamed to the laptop of the mentor, along with a webcam feed showing the probe position. The task was to identify CCHD in need of immediate transfer to a pediatric cardiac surgical center. The result was compared to the previously completed echocardiographic report and the clinical decision of the patient-responsible pediatric cardiologist. Results: During 17 months, 15 newborns were recruited. All six newborns with CCHD were correctly labeled by the remotementoring pair. One newborn with Tetralogy of Fallot was erroneously labeled as needing immediate transfer. Eight newborns without CCHD were correctly labeled. Conclusions: Remote-mentored echocardiography performed by examiners without experience in imaging CHD identified all newborns with CCHD in need of immediate transfer for specialist care. The setup shows promising results for improving the management of CCHD in hospitals without continuous pediatric cardiology service. KEYWORDSRemote-mentoring; echocardiography; telemedicine; congenital heart defect Abbreviations CHD Congenital heart defect CCHD Critical congenital heart defect CoA Coarctation of the aorta This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing

“…These findings underscore the potential of machine learning-based analyses in supporting medical professionals in diagnosing specific types of CHD, showcasing the promising intersection of technology and healthcare. Ge et al proposed an innovative method for identifying Pulmonary Hypertension (PH) associated with Congenital Heart Disease (CHD) by incorporating time-frequency domain analysis and machine learning (ML) characteristics [17]. The researchers integrated time-frequency analysis techniques into an ML framework to extract relevant features from echocardiographic data.…”

“…Further research and innovation are needed to refine and optimize these methodologies for improved accuracy and reliability. [16] Computer-aided analysis of heart sounds in pediatric patients with left-to-right shunt CHD using CNN Limited to specific type of CHD; Dependency on quality of input heart sound data Ge et al [17] Identification of Pulmonary Hypertension associated with CHD using time-frequency domain analysis and ML Limited to Pulmonary Hypertension; Generalization to other types of CHD Steeden et al [18] Exploration of AI-based methodologies for CHD assessment Limited discussion on specific limitations; Generalizability to different AI methodologies Alici-Karaca et al [19] CNN for classifying radiation-induced liver disease…”