Can artificial intelligence-assisted auscultation become the Heimdallr for diagnosing congenital heart disease?

The Evolving Stethoscope: Insights Derived from Studying Phonocardiography in Trainees

Phonocardiography (PCG) is used as an adjunct to teach cardiac auscultation and is now a function of PCG-capable stethoscopes (PCS). To evaluate the efficacy of PCG and PCS, the authors investigated the impact of providing PCG data and PCSs on how frequently murmurs, rubs, and gallops (MRGs) were correctly identified by third-year medical students. Following their internal medicine rotation, third-year medical students from the Georgetown University School of Medicine completed a standardized auscultation assessment. Sound files of 10 different MRGs with a corresponding clinical vignette and physical exam location were provided with and without PCG (with interchangeable question stems) as 10 paired questions (20 total questions). Some (32) students also received a PCS to use during their rotation. Discrimination/difficulty indexes, comparative chi-squared, and McNemar test p-values were calculated. The addition of phonocardiograms to audio data was associated with more frequent identification of mitral stenosis, S4, and cardiac friction rub, but less frequent identification of ventricular septal defect, S3, and tricuspid regurgitation. Students with a PCS had a higher frequency of identifying a cardiac friction rub. PCG may improve the identification of low-frequency, usually diastolic, heart sounds but appears to worsen or have little effect on the identification of higher-frequency, often systolic, heart sounds. As digital and phonocardiography-capable stethoscopes become more prevalent, insights regarding their strengths and weaknesses may be incorporated into medical school curricula, bedside rounds (to enhance teaching and diagnosis), and telemedicine/tele-auscultation efforts.

Role of artificial intelligence in early detection of congenital heart diseases in neonates

In the domain of healthcare, most importantly pediatric healthcare, the role of artificial intelligence (AI) has significantly impacted the medical field. Congenital heart diseases represent a group of heart diseases that are known to be some of the most critical cardiac conditions present at birth. These heart diseases need a swift diagnosis as well as an intervention to ensure the wellbeing of newborns. Fortunately, with the help of AI, including the highly advanced algorithms, analytics and imaging involved, it provides us with a promising era for neonatal care. This article reviewed published data in PubMed, Science Direct, UpToDate, and Google Scholar between the years 2015-2023. To conclude The use of artificial intelligence in detecting congenital heart diseases has shown great promise in improving the accuracy and efficiency of diagnosis. Several studies have demonstrated the efficacy of AI-based approaches for diagnosing congenital heart diseases, with results indicating that the systems can achieve high levels of sensitivity and specificity. In addition, AI can help reduce the workload of healthcare professionals allowing them to focus on other critical aspects of patient care. Despite the potential benefits of using AI, in addition to detecting congenital heart disease, there are still some challenges to overcome, such as the need for large amounts of high-quality data and the requirement for careful validation of the algorithms. Nevertheless, with ongoing research and development, AI is likely to become an increasingly valuable tool for improving the diagnosis and treatment of congenital heart diseases.

Audiological Diagnosis of Valvular and Congenital Heart Diseases in the Era of Artificial Intelligence

In recent years, electronic stethoscopes have been combined with artificial intelligence (AI) technology to digitally acquire heart sounds, intelligently identify valvular disease and congenital heart disease, and improve the accuracy of heart disease diagnosis. The research on AI-based intelligent stethoscopy technology mainly focuses on AI algorithms, and the commonly used methods are end-to-end deep learning algorithms and machine learning algorithms based on feature extraction, and the hot spot for future research is to establish a large standardized heart sound database and unify these algorithms for external validation; in addition, different electronic stethoscopes should also be extensively compared so that the algorithms can be compatible with different. In addition, there should be extensive comparison of different electronic stethoscopes so that the algorithms can be compatible with heart sounds collected by different stethoscopes; especially importantly, the deployment of algorithms in the cloud is a major trend in the future development of artificial intelligence. Finally, the research of artificial intelligence based on heart sounds is still in the preliminary stage, although there is great progress in identifying valve disease and congenital heart disease, they are all in the research of algorithm for disease diagnosis, and there is little research on disease severity, remote monitoring, prognosis, etc., which will be a hot spot for future research.

The discerning ear: cardiac auscultation in the era of artificial intelligence and telemedicine

Heart murmur, a thoracic auscultatory finding of cardiovascular origin, is extremely common in childhood and can appear at any age from premature newborn to late adolescence. The objective of this review is to provide a modern examination and update of cardiac murmur auscultation in this new era of artificial intelligence (AI) and telemedicine. First, we provide a comprehensive review of the causes and differential diagnosis, clinical features, evaluation, and long-term management of paediatric heart murmurs. Next, we provide a brief history of computer-assisted auscultation and murmur analysis, along with insight into the engineering design of the digital stethoscope. We conclude with a discussion of the paradigm shifting impact of deep learning on murmur analysis, AI-assisted auscultation, and the implications of these technologies on telemedicine in paediatric cardiology. It is our hope that this article provides an updated perspective on the impact of AI on cardiac auscultation for the modern paediatric cardiologist.