StethAid: A Digital Auscultation Platform for Pediatrics.

(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the interpretation of auscultated sounds. A few AI-augmented digital stethoscopes exist but none are dedicated to pediatrics. Our goal was to develop a digital auscultation platform for pediatric medicine. (2) Methods: We developed StethAid-a digital platform for artificial intelligence-assisted auscultation and telehealth in pediatrics-that consists of a wireless digital stethoscope, mobile applications, customized patient-provider portals, and deep learning algorithms. To validate the StethAid platform, we characterized our stethoscope and used the platform in two clinical applications: (1) Still's murmur identification and (2) wheeze detection. The platform has been deployed in four children's medical centers to build the first and largest pediatric cardiopulmonary datasets, to our knowledge. We have trained and tested deep-learning models using these datasets. (3) Results: The frequency response of the StethAid stethoscope was comparable to those of the commercially available Eko Core, Thinklabs One, and Littman 3200 stethoscopes. The labels provided by our expert physician offline were in concordance with the labels of providers at the bedside using their acoustic stethoscopes for 79.3% of lungs cases and 98.3% of heart cases. Our deep learning algorithms achieved high sensitivity and specificity for both Still's murmur identification (sensitivity of 91.9% and specificity of 92.6%) and wheeze detection (sensitivity of 83.7% and specificity of 84.4%). (4) Conclusions: Our team has created a technically and clinically validated pediatric digital AI-enabled auscultation platform. Use of our platform could improve efficacy and efficiency of clinical care for pediatric patients, reduce parental anxiety, and result in cost savings.

Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation.

Digital stethoscopes can enable the development of integrated artificial intelligence (AI) systems that can remove the subjectivity of manual auscultation, improve diagnostic accuracy, and compensate for diminishing auscultatory skills. Developing scalable AI systems can be challenging, especially when acquisition devices differ and thus introduce sensor bias. To address this issue, a precise knowledge of these differences, i.e., frequency responses of these devices, is needed, but the manufacturers often do not provide complete device specifications. In this study, we reported an effective methodology for determining the frequency response of a digital stethoscope and used it to characterise three common digital stethoscopes: Littmann 3200, Eko Core, and Thinklabs One. Our results show significant inter-device variability in that the frequency responses of the three studied stethoscopes were distinctly different. A moderate intra-device variability was seen when comparing two separate units of Littmann 3200. The study highlights the need for normalisation across devices for developing successful AI-assisted auscultation and provides a technical characterisation approach as a first step to accomplish it.

A randomized, controlled trial of an innovative, multimedia instructional program for acquiring auditory skill in identifying pediatric heart murmurs.

Objective: To create a brief, acceptable, innovative method for self-paced learning to enhance recognition of pediatric heart murmurs by medical students, and to demonstrate this method's effectiveness in a randomized, controlled trial. Materials and methods: A curriculum of six 10-min online learning modules was designed to enable deliberate practice of pediatric cardiac auscultation, using recordings of patients' heart murmurs. Principles of andragogy and multimedia learning were applied to optimize acquisition of this skill. A pretest and posttest, given 4 weeks apart, were created using additional recordings and administered to 87 3rd-year medical students during their pediatric clerkship. They were randomized to have access to the modules after the pretest or after the posttest, and asked to use at least the first 2 of the modules. Results: 47 subjects comprised the Intervention group, and 40 subjects the Control group. On our primary outcome, distinguishing innocent from pathological with at least moderate confidence, the posttest scores were significantly higher for the Intervention group (60.5%) than for the Control group (20.0%). For our secondary outcomes, the 2 groups also differed significantly in the ability to distinguish innocent from pathological murmurs, and in identifying the actual diagnosis. On all 3 outcomes, those Intervention group subjects who accessed 4-6 modules scored higher than those who accessed 0-3 modules, who in turn scored higher than the Control group. Summary: Applying current principles of adult learning, we have created a teaching program for medical students to learn to recognize common pediatric murmurs. Its effectiveness was demonstrated in a randomized, controlled trial. The program results in a meaningful gain in this skill from 1 h of self-paced training with high acceptance to learners.

Automated identification of innocent Still's murmur using a convolutional neural network.

Background: Still's murmur is the most prevalent innocent heart murmur of childhood. Auscultation is the primary clinical tool to identify this murmur as innocent. Whereas pediatric cardiologists routinely perform this task, primary care providers are less successful in distinguishing Still's murmur from the murmurs of true heart disease. This results in a large number of children with a Still's murmur being referred to pediatric cardiologists. Objectives: To develop a computer algorithm that can aid primary care providers to identify the innocent Still's murmur at the point of care, to substantially decrease over-referral. Methods: The study included Still's murmurs, pathological murmurs, other innocent murmurs, and normal (i.e., non-murmur) heart sounds of 1,473 pediatric patients recorded using a commercial electronic stethoscope. The recordings with accompanying clinical diagnoses provided by a pediatric cardiologist were used to train and test the convolutional neural network-based algorithm. Results: A comparative analysis showed that the algorithm using only the murmur sounds recorded at the lower left sternal border achieved the highest accuracy. The developed algorithm identified Still's murmur with 90.0% sensitivity and 98.3% specificity for the default decision threshold. The area under the receiver operating characteristic curve was 0.943. Conclusions: Still's murmur can be identified with high accuracy with the algorithm we developed. Using this approach, the algorithm could help to reduce the rate of unnecessary pediatric cardiologist referrals and use of echocardiography for a common benign finding.

PAGOD syndrome: eighth case and comparison to animal models of congenital vitamin A deficiency.

We observed a 46, XY infant with atrophy of the optic nerve, complex congenital heart disease including a double outlet right ventricle, hypoplasia of the right pulmonary artery and lung, eventration of the diaphragm, and ambiguous genitalia. The baby died of cardiac arrhythmias at 204 days. The pattern of malformations was compatible with pulmonary tract and pulmonary artery, agonadism, omphalocele, diaphragmatic defect, and dextrocardia (PAGOD) syndrome. The condition may resemble the malformation complex associated with developmental deficiency of vitamin A or retinoic acid, as described in animal models.

Subarachnoid hemorrhage and isolated atresia of the aortic arch.

The case of a 14 year old boy with subarachnoid hemorrhage and atresia of the aorta without patent ductus arteriosus or intracardiac shunt is described. This case calls attention to the possibility of aortic obstruction in adolescents or young adults with hypertensive stroke. The clinical symptoms, radiographic findings, and surgical repair of isolated aortic interruption, including atresia, are discussed.