StethAid: A Digital Auscultation Platform for Pediatrics

Authors

Youness Arjoune, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC 20010, USA.
Trong N. Nguyen, AusculTech Dx, 2601 University Blvd West #301, Silver Spring, MD 20902, USA.
Tyler Salvador, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC 20010, USA.
Anha Telluri, School of Medicine and Health Sciences, George Washington University, Washington, DC 20052, USA.
Jonathan C. Schroeder, Division of Pulmonary and Sleep Medicine, Children's National Hospital, Washington, DC 20010, USA.
Robert L. Geggel, Department of Cardiology, Boston Children's Hospital, Boston, MA 02115, USA.
Joseph W. May, Department of Pediatrics, Walter Reed National Military Medical Center, Bethesda, MD 20814, USA.
Dinesh K. Pillai, Division of Pulmonary and Sleep Medicine, Children's National Hospital, Washington, DC 20010, USA.
Stephen J. Teach, Department of Pediatrics, Children's National Hospital, Washington, DC 20010, USA.
Shilpa J. Patel, Division of Emergency Medicine, Children's National Hospital, Washington, DC 20010, USA.
Robin W. Doroshow, AusculTech Dx, 2601 University Blvd West #301, Silver Spring, MD 20902, USA.
Raj Shekhar, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC 20010, USA.

Document Type

Journal Article

Publication Date

6-20-2023

Journal

Sensors (Basel, Switzerland)

Volume

23

Issue

12

DOI

10.3390/s23125750

Keywords

Still’s murmur detection; artificial intelligence-assisted auscultation; deep learning; digital stethoscope; pediatric stethoscope; wheeze detection

Abstract

(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.

APA Citation

Arjoune, Youness; Nguyen, Trong N.; Salvador, Tyler; Telluri, Anha; Schroeder, Jonathan C.; Geggel, Robert L.; May, Joseph W.; Pillai, Dinesh K.; Teach, Stephen J.; Patel, Shilpa J.; Doroshow, Robin W.; and Shekhar, Raj, "StethAid: A Digital Auscultation Platform for Pediatrics" (2023). GW Authored Works. Paper 2700.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/2700

Department

Pediatrics