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. 2023 Jun 20;23(12):5750.
doi: 10.3390/s23125750.

StethAid: A Digital Auscultation Platform for Pediatrics

Youness Arjoune  1 , Trong N Nguyen  2 , Tyler Salvador  1 , Anha Telluri  3 , Jonathan C Schroeder  4 , Robert L Geggel  5 , Joseph W May  6 , Dinesh K Pillai  4 , Stephen J Teach  7 , Shilpa J Patel  8 , Robin W Doroshow  2   9 , Raj Shekhar  1   2
Affiliations

StethAid: A Digital Auscultation Platform for Pediatrics

Youness Arjoune et al. Sensors (Basel). .

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.

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

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Conflict of interest statement

R.W.D. and R.S. are founders of AusculTech Dx.

Figures

Figure 1
Figure 1
StethAid platform.
Figure 2
Figure 2
StethAid stethoscope: (a) assembled unit; (b) mic inputs and microcontroller; and (c) main and charging boards.
Figure 3
Figure 3
Charging of an assembled StethAid device: (a) wireless charging and (b) wired charging.
Figure 4
Figure 4
StethAid device (a) exploded view; (b) StethAid length, height, and width [23].
Figure 5
Figure 5
StethAid app features Auscultation Tools, AI Suite, Telehealth, and Patient Library.
Figure 6
Figure 6
Web portal.
Figure 7
Figure 7
StethAid telehealth patient and physician interfaces.
Figure 8
Figure 8
StethAid frequency response measurement rig: (a) 3D model showing the rig as open, (b) 3D model showing the rig as closed, and (c) the StethAid stethoscope mounted on the 3D-printed rig in contact with the speaker.
Figure 9
Figure 9
Frequency response of StethAid compared with those of Littmann 3200, Thinklabs One, and Eko Core.
See this image and copyright information in PMC

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