Cargando…

Cardiac Auscultation Using Smartphones: Pilot Study

BACKGROUND: Cardiac auscultation is a cost-effective, noninvasive screening tool that can provide information about cardiovascular hemodynamics and disease. However, with advances in imaging and laboratory tests, the importance of cardiac auscultation is less appreciated in clinical practice. The wi...

Descripción completa

Detalles Bibliográficos
Autores principales: Kang, Si-Hyuck, Joe, Byunggill, Yoon, Yeonyee, Cho, Goo-Yeong, Shin, Insik, Suh, Jung-Won
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5853766/
https://www.ncbi.nlm.nih.gov/pubmed/29490899
http://dx.doi.org/10.2196/mhealth.8946
_version_ 1783306812937732096
author Kang, Si-Hyuck
Joe, Byunggill
Yoon, Yeonyee
Cho, Goo-Yeong
Shin, Insik
Suh, Jung-Won
author_facet Kang, Si-Hyuck
Joe, Byunggill
Yoon, Yeonyee
Cho, Goo-Yeong
Shin, Insik
Suh, Jung-Won
author_sort Kang, Si-Hyuck
collection PubMed
description BACKGROUND: Cardiac auscultation is a cost-effective, noninvasive screening tool that can provide information about cardiovascular hemodynamics and disease. However, with advances in imaging and laboratory tests, the importance of cardiac auscultation is less appreciated in clinical practice. The widespread use of smartphones provides opportunities for nonmedical expert users to perform self-examination before hospital visits. OBJECTIVE: The objective of our study was to assess the feasibility of cardiac auscultation using smartphones with no add-on devices for use at the prehospital stage. METHODS: We performed a pilot study of patients with normal and pathologic heart sounds. Heart sounds were recorded on the skin of the chest wall using 3 smartphones: the Samsung Galaxy S5 and Galaxy S6, and the LG G3. Recorded heart sounds were processed and classified by a diagnostic algorithm using convolutional neural networks. We assessed diagnostic accuracy, as well as sensitivity, specificity, and predictive values. RESULTS: A total of 46 participants underwent heart sound recording. After audio file processing, 30 of 46 (65%) heart sounds were proven interpretable. Atrial fibrillation and diastolic murmur were significantly associated with failure to acquire interpretable heart sounds. The diagnostic algorithm classified the heart sounds into the correct category with high accuracy: Galaxy S5, 90% (95% CI 73%-98%); Galaxy S6, 87% (95% CI 69%-96%); and LG G3, 90% (95% CI 73%-98%). Sensitivity, specificity, positive predictive value, and negative predictive value were also acceptable for the 3 devices. CONCLUSIONS: Cardiac auscultation using smartphones was feasible. Discrimination using convolutional neural networks yielded high diagnostic accuracy. However, using the built-in microphones alone, the acquisition of reproducible and interpretable heart sounds was still a major challenge. TRIAL REGISTRATION: ClinicalTrials.gov NCT03273803; https://clinicaltrials.gov/ct2/show/NCT03273803 (Archived by WebCite at http://www.webcitation.org/6x6g1fHIu)
format Online
Article
Text
id pubmed-5853766
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-58537662018-03-19 Cardiac Auscultation Using Smartphones: Pilot Study Kang, Si-Hyuck Joe, Byunggill Yoon, Yeonyee Cho, Goo-Yeong Shin, Insik Suh, Jung-Won JMIR Mhealth Uhealth Original Paper BACKGROUND: Cardiac auscultation is a cost-effective, noninvasive screening tool that can provide information about cardiovascular hemodynamics and disease. However, with advances in imaging and laboratory tests, the importance of cardiac auscultation is less appreciated in clinical practice. The widespread use of smartphones provides opportunities for nonmedical expert users to perform self-examination before hospital visits. OBJECTIVE: The objective of our study was to assess the feasibility of cardiac auscultation using smartphones with no add-on devices for use at the prehospital stage. METHODS: We performed a pilot study of patients with normal and pathologic heart sounds. Heart sounds were recorded on the skin of the chest wall using 3 smartphones: the Samsung Galaxy S5 and Galaxy S6, and the LG G3. Recorded heart sounds were processed and classified by a diagnostic algorithm using convolutional neural networks. We assessed diagnostic accuracy, as well as sensitivity, specificity, and predictive values. RESULTS: A total of 46 participants underwent heart sound recording. After audio file processing, 30 of 46 (65%) heart sounds were proven interpretable. Atrial fibrillation and diastolic murmur were significantly associated with failure to acquire interpretable heart sounds. The diagnostic algorithm classified the heart sounds into the correct category with high accuracy: Galaxy S5, 90% (95% CI 73%-98%); Galaxy S6, 87% (95% CI 69%-96%); and LG G3, 90% (95% CI 73%-98%). Sensitivity, specificity, positive predictive value, and negative predictive value were also acceptable for the 3 devices. CONCLUSIONS: Cardiac auscultation using smartphones was feasible. Discrimination using convolutional neural networks yielded high diagnostic accuracy. However, using the built-in microphones alone, the acquisition of reproducible and interpretable heart sounds was still a major challenge. TRIAL REGISTRATION: ClinicalTrials.gov NCT03273803; https://clinicaltrials.gov/ct2/show/NCT03273803 (Archived by WebCite at http://www.webcitation.org/6x6g1fHIu) JMIR Publications 2018-02-28 /pmc/articles/PMC5853766/ /pubmed/29490899 http://dx.doi.org/10.2196/mhealth.8946 Text en ©Si-Hyuck Kang, Byunggill Joe, Yeonyee Yoon, Goo-Yeong Cho, Insik Shin, Jung-Won Suh. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 28.02.2018. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Kang, Si-Hyuck
Joe, Byunggill
Yoon, Yeonyee
Cho, Goo-Yeong
Shin, Insik
Suh, Jung-Won
Cardiac Auscultation Using Smartphones: Pilot Study
title Cardiac Auscultation Using Smartphones: Pilot Study
title_full Cardiac Auscultation Using Smartphones: Pilot Study
title_fullStr Cardiac Auscultation Using Smartphones: Pilot Study
title_full_unstemmed Cardiac Auscultation Using Smartphones: Pilot Study
title_short Cardiac Auscultation Using Smartphones: Pilot Study
title_sort cardiac auscultation using smartphones: pilot study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5853766/
https://www.ncbi.nlm.nih.gov/pubmed/29490899
http://dx.doi.org/10.2196/mhealth.8946
work_keys_str_mv AT kangsihyuck cardiacauscultationusingsmartphonespilotstudy
AT joebyunggill cardiacauscultationusingsmartphonespilotstudy
AT yoonyeonyee cardiacauscultationusingsmartphonespilotstudy
AT chogooyeong cardiacauscultationusingsmartphonespilotstudy
AT shininsik cardiacauscultationusingsmartphonespilotstudy
AT suhjungwon cardiacauscultationusingsmartphonespilotstudy