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 pediat...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/12/5750 |
| _version_ | 1797592664479105024 |
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| author | Youness Arjoune Trong N. Nguyen Tyler Salvador Anha Telluri Jonathan C. Schroeder Robert L. Geggel Joseph W. May Dinesh K. Pillai Stephen J. Teach Shilpa J. Patel Robin W. Doroshow Raj Shekhar |
| author_facet | Youness Arjoune Trong N. Nguyen Tyler Salvador Anha Telluri Jonathan C. Schroeder Robert L. Geggel Joseph W. May Dinesh K. Pillai Stephen J. Teach Shilpa J. Patel Robin W. Doroshow Raj Shekhar |
| author_sort | Youness Arjoune |
| collection | DOAJ |
| description | (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. |
| first_indexed | 2024-03-11T01:56:22Z |
| format | Article |
| id | doaj.art-c1a80b4fe2fe43b8bcd6312325620b51 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:56:22Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c1a80b4fe2fe43b8bcd6312325620b512023-11-18T12:35:34ZengMDPI AGSensors1424-82202023-06-012312575010.3390/s23125750StethAid: A Digital Auscultation Platform for PediatricsYouness Arjoune0Trong N. Nguyen1Tyler Salvador2Anha Telluri3Jonathan C. Schroeder4Robert L. Geggel5Joseph W. May6Dinesh K. Pillai7Stephen J. Teach8Shilpa J. Patel9Robin W. Doroshow10Raj Shekhar11Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USAAusculTech Dx, 2601 University Blvd West #301, Silver Spring, MD 20902, USASheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USASchool of Medicine and Health Sciences, George Washington University, Washington, DC 20052, USADivision of Pulmonary and Sleep Medicine, Children’s National Hospital, Washington, DC 20010, USADepartment of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USADepartment of Pediatrics, Walter Reed National Military Medical Center, Bethesda, MD 20814, USADivision of Pulmonary and Sleep Medicine, Children’s National Hospital, Washington, DC 20010, USADepartment of Pediatrics, Children’s National Hospital, Washington, DC 20010, USADivision of Emergency Medicine, Children’s National Hospital, Washington, DC 20010, USAAusculTech Dx, 2601 University Blvd West #301, Silver Spring, MD 20902, USASheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA(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.https://www.mdpi.com/1424-8220/23/12/5750digital stethoscopepediatric stethoscopeartificial intelligence-assisted auscultationStill’s murmur detectionwheeze detectiondeep learning |
| spellingShingle | Youness Arjoune Trong N. Nguyen Tyler Salvador Anha Telluri Jonathan C. Schroeder Robert L. Geggel Joseph W. May Dinesh K. Pillai Stephen J. Teach Shilpa J. Patel Robin W. Doroshow Raj Shekhar StethAid: A Digital Auscultation Platform for Pediatrics Sensors digital stethoscope pediatric stethoscope artificial intelligence-assisted auscultation Still’s murmur detection wheeze detection deep learning |
| title | StethAid: A Digital Auscultation Platform for Pediatrics |
| title_full | StethAid: A Digital Auscultation Platform for Pediatrics |
| title_fullStr | StethAid: A Digital Auscultation Platform for Pediatrics |
| title_full_unstemmed | StethAid: A Digital Auscultation Platform for Pediatrics |
| title_short | StethAid: A Digital Auscultation Platform for Pediatrics |
| title_sort | stethaid a digital auscultation platform for pediatrics |
| topic | digital stethoscope pediatric stethoscope artificial intelligence-assisted auscultation Still’s murmur detection wheeze detection deep learning |
| url | https://www.mdpi.com/1424-8220/23/12/5750 |
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