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Huecker M, Schutzman C, French J, El-Kersh K, Ghafghazi S, Desai R, Frick D, Thomas JJ. Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study. JMIR Cardio 2024; 8:e57111. [PMID: 38924781 DOI: 10.2196/57111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/19/2024] [Accepted: 04/10/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal reimbursement dollars. No current modality or technology allows for accurate measurement of relevant HF parameters in ambulatory, rural, or underserved settings. This limits the use of telehealth to diagnose or monitor HF in ambulatory patients. OBJECTIVE This study describes a novel HF diagnostic technology using audio recordings from a standard mobile phone. METHODS This prospective study of acoustic microphone recordings enrolled convenience samples of patients from 2 different clinical sites in 2 separate areas of the United States. Recordings were obtained at the aortic (second intercostal) site with the patient sitting upright. The team used recordings to create predictive algorithms using physics-based (not neural networks) models. The analysis matched mobile phone acoustic data to ejection fraction (EF) and stroke volume (SV) as evaluated by echocardiograms. Using the physics-based approach to determine features eliminates the need for neural networks and overfitting strategies entirely, potentially offering advantages in data efficiency, model stability, regulatory visibility, and physical insightfulness. RESULTS Recordings were obtained from 113 participants. No recordings were excluded due to background noise or for any other reason. Participants had diverse racial backgrounds and body surface areas. Reliable echocardiogram data were available for EF from 113 patients and for SV from 65 patients. The mean age of the EF cohort was 66.3 (SD 13.3) years, with female patients comprising 38.3% (43/113) of the group. Using an EF cutoff of ≤40% versus >40%, the model (using 4 features) had an area under the receiver operating curve (AUROC) of 0.955, sensitivity of 0.952, specificity of 0.958, and accuracy of 0.956. The mean age of the SV cohort was 65.5 (SD 12.7) years, with female patients comprising 34% (38/65) of the group. Using a clinically relevant SV cutoff of <50 mL versus >50 mL, the model (using 3 features) had an AUROC of 0.922, sensitivity of 1.000, specificity of 0.844, and accuracy of 0.923. Acoustics frequencies associated with SV were observed to be higher than those associated with EF and, therefore, were less likely to pass through the tissue without distortion. CONCLUSIONS This work describes the use of mobile phone auscultation recordings obtained with unaltered cellular microphones. The analysis reproduced the estimates of EF and SV with impressive accuracy. This technology will be further developed into a mobile app that could bring screening and monitoring of HF to several clinical settings, such as home or telehealth, rural, remote, and underserved areas across the globe. This would bring high-quality diagnostic methods to patients with HF using equipment they already own and in situations where no other diagnostic and monitoring options exist.
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Affiliation(s)
- Martin Huecker
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Craig Schutzman
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Joshua French
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Karim El-Kersh
- Department of Pulmonary and Critical Care Medicine, The University of Arizona, Phoenix, AZ, United States
| | - Shahab Ghafghazi
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Ravi Desai
- Lehigh Valley Health Network Cardiology and Critical Care, Allentown, PA, United States
| | - Daniel Frick
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Jarred Jeremy Thomas
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
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Wienhold J, Kemper I, Czaplik M, Follmann A, Rossaint R, Derwall M. [Teleconsultation for preoperative evaluation and informed consent-Are we ready for a paradigm shift?]. DIE ANAESTHESIOLOGIE 2023; 72:697-702. [PMID: 37563314 DOI: 10.1007/s00101-023-01319-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/27/2023] [Indexed: 08/12/2023]
Abstract
In Germany, approximately 17 million anaesthesiological procedures and, consequently, roughly the same number of preoperative consultations are conducted each year. So far, these have predominantly taken place in person. However, recent developments in technology, medical-legal aspects, and politics, combined with the catalyzing effect of the pandemic situation, have led to a significant boost in telemedicine. In the field of anaesthesia, there are new approaches to implementing telemedicine in the pre- and postoperative setting. This article focuses on the preoperative setting and presents general requirements for a teleconsultation as preoperative evaluation, the current state of technology, and medical-legal aspects.
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Affiliation(s)
- Jan Wienhold
- Klinik für Anästhesiologie, Medizinische Fakultät der RWTH Aachen, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Deutschland.
| | - Ilka Kemper
- Geschäftsbereich Recht, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Deutschland
| | - Michael Czaplik
- Klinik für Anästhesiologie, Medizinische Fakultät der RWTH Aachen, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Deutschland
| | - Andreas Follmann
- Klinik für Anästhesiologie, Medizinische Fakultät der RWTH Aachen, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Deutschland
| | - Rolf Rossaint
- Klinik für Anästhesiologie, Medizinische Fakultät der RWTH Aachen, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Deutschland
| | - Matthias Derwall
- St. Johannes Hospital Dortmund, Johannesstraße 9-17, 44137, Dortmund, Deutschland
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Azmeen A, Vakilzadian H, Haider H, Mathers DH, Zimmerman R, Bedi S, O'Leary EL. Heart sounds: Past, present, and future from a technological and clinical perspective - a systematic review. Proc Inst Mech Eng H 2023:9544119231172858. [PMID: 37139865 DOI: 10.1177/09544119231172858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The high prevalence of cardiac diseases around the world has created a need for quick, easy and cost effective approaches to diagnose heart disease. The auscultation and interpretation of heart sounds using the stethoscope is relatively inexpensive, requires minimal to advanced training, and is widely available and easily carried by healthcare providers working in urban environments or medically underserved rural areas. Since René-Théophile-Hyacinthe Laennec's simple, monoaural design, the capabilities of modern-day, commercially available stethoscopes and stethoscope systems have radically advanced with the integration of electronic hardware and software tools, however these systems are largely confined to the metropolitan medical centers. The purpose of this paper is to review the history of stethoscopes, compare commercially available stethoscope products and analytical software, and discuss future directions. Our review includes a description of heart sounds and how modern software enables the measurement and analysis of time intervals, teaching auscultation, remote cardiac examination (telemedicine) and, more recently, spectrographic evaluation and electronic storage. The basic methodologies behind modern software algorithms and techniques for heart sound preprocessing, segmentation and classification are described to provide awareness.
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Affiliation(s)
- Ayesha Azmeen
- University of Nebraska Medical Center, Omaha, NE, USA
| | | | - Hani Haider
- University of Nebraska Medical Center, Omaha, NE, USA
| | | | | | - Shine Bedi
- Univeristy of Nebraska-Lincoln, Lincoln, NE, USA
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Khalilian MR, Safari M, Hajipour M, Rahmani K, Safari M, Ahmadpour MH, Tahouri T. Evaluation of the heart sounds in children using a Doppler Phonolyser. Biomed Eng Online 2023; 22:24. [PMID: 36899353 PMCID: PMC9999563 DOI: 10.1186/s12938-023-01084-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 02/21/2023] [Indexed: 03/12/2023] Open
Abstract
BACKGROUND Heart auscultation is an easy and inexpensive tool for early diagnosis of congenital heart defects. In this regard, a simple device which can be used easily by physicians for heart murmur detection will be very useful. The current study was conducted to evaluate the validity of a Doppler-based device named "Doppler Phonolyser" for the diagnosis of structural heart diseases in pediatric patients. In this cross-sectional study, 1272 patients under 16 years who were referred between April 2021 and February 2022, to a pediatric cardiology clinic in Mofid Children Hospital, Tehran, Iran, were enrolled. All the patients were examined by a single experienced pediatric cardiologist using a conventional stethoscope at the first step and a Doppler Phonolyser device at the second step. Afterward, the patient underwent trans-thoracic echocardiography, and the echocardiogram results were compared with the conventional stethoscope as well as the Doppler Phonolyser findings. RESULTS Sensitivity of the Doppler Phonolyser for detecting congenital heart defects was 90.5%. The specificity of the Doppler Phonolyser in detecting heart disease was 68.9% in compared with the specificity of the conventional stethoscope, which was 94.8%. Among the most common congenital heart defects in our study population, the sensitivity of the Doppler Phonolyser was 100% for detection of tetralogy of Fallot (TOF); In contrast, sensitivity of both the conventional stethoscope and the Doppler Phonolyser was relatively low for detecting atrial septal defect. CONCLUSIONS Doppler Phonolyser could be useful as a diagnostic tool for the detection of congenital heart defects. The main advantages of the Doppler Phonolyser over the conventional stethoscope are no need for operator experience, the ability to distinguish innocent murmurs from the pathologic ones and no effect of environmental sounds on the performance of the device.
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Affiliation(s)
- Mohammad Reza Khalilian
- Department of Pediatrics, Shahid Beheshti University of Medical Sciences, Shahid Modarres Educational Hospital, Intersection of Saadat Abad and Yadegar Imam Highway, Tehran, Iran
| | - Mahsa Safari
- Department of Pediatrics, Mofid Children's Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahmoud Hajipour
- Pediatric Gastroenterology, Hepatology and Nutrition Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Khosro Rahmani
- Head of Rheumatology Department Mofid Children's Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahmoud Safari
- Department of Anatomy, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Hassan Ahmadpour
- Department of Nursing, Faculty of Nursing and Midwifery, Branch of Varamin and Pishva, Islamic Azad University, Tehran, Iran
| | - Tahmineh Tahouri
- Pediatric Cardiology, Shahid Modarres Educational Hospital, Shahid Beheshti University of Medical Science, School of Medicine, Tehran, Iran.
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Park JS, Kim K, Kim JH, Choi YJ, Kim K, Suh DI. A machine learning approach to the development and prospective evaluation of a pediatric lung sound classification model. Sci Rep 2023; 13:1289. [PMID: 36690658 PMCID: PMC9871007 DOI: 10.1038/s41598-023-27399-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 01/02/2023] [Indexed: 01/25/2023] Open
Abstract
Auscultation, a cost-effective and non-invasive part of physical examination, is essential to diagnose pediatric respiratory disorders. Electronic stethoscopes allow transmission, storage, and analysis of lung sounds. We aimed to develop a machine learning model to classify pediatric respiratory sounds. Lung sounds were digitally recorded during routine physical examinations at a pediatric pulmonology outpatient clinic from July to November 2019 and labeled as normal, crackles, or wheezing. Ensemble support vector machine models were trained and evaluated for four classification tasks (normal vs. abnormal, crackles vs. wheezing, normal vs. crackles, and normal vs. wheezing) using K-fold cross-validation (K = 10). Model performance on a prospective validation set (June to July 2021) was compared with those of pediatricians and non-pediatricians. Total 680 clips were used for training and internal validation. The model accuracies during internal validation for normal vs. abnormal, crackles vs. wheezing, normal vs. crackles, and normal vs. wheezing were 83.68%, 83.67%, 80.94%, and 90.42%, respectively. The prospective validation (n = 90) accuracies were 82.22%, 67.74%, 67.80%, and 81.36%, respectively, which were comparable to pediatrician and non-pediatrician performance. An automated classification model of pediatric lung sounds is feasible and maybe utilized as a screening tool for respiratory disorders in this pandemic era.
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Affiliation(s)
- Ji Soo Park
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, South Korea
| | - Kyungdo Kim
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Ji Hye Kim
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, South Korea
| | - Yun Jung Choi
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, South Korea
| | - Kwangsoo Kim
- Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, South Korea.
| | - Dong In Suh
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, South Korea.
- Department of Pediatrics, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, South Korea.
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Luo H, Lamata P, Bazin S, Bautista T, Barclay N, Shahmohammadi M, Lubrecht JM, Delhaas T, Prinzen FW. Smartphone as an electronic stethoscope: factors influencing heart sound quality . EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:473-480. [PMID: 36712168 PMCID: PMC9708017 DOI: 10.1093/ehjdh/ztac044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 08/11/2022] [Indexed: 02/01/2023]
Abstract
Aims Smartphones are equipped with a high-quality microphone which may be used as an electronic stethoscope. We aim to investigate the factors influencing quality of heart sound recorded using a smartphone by non-medical users. Methods and results An app named Echoes was developed for recording heart sounds using iPhone. Information on phone version and users' characteristics including sex, age, and body mass index (BMI) was collected. Heart sound quality was visually assessed and its relation to phone version and users' characteristics was analysed. A total of 1148 users contributed to 7597 heart sound recordings. Over 80% of users were able to make at least one good-quality recording. Good-, unsure- and bad-quality recordings amounted to 5647 (74.6%), 466 (6.2%) and 1457 (19.2%), respectively. Most good recordings were collected in the first three attempts of the users. Phone version did not significantly change the users' success rate of making a good recording, neither was sex in the first attempt (P = 0.41) or the first three attempts (P = 0.21). Success rate tended to decrease with age in the first attempt (P = 0.06) but not the first three attempts (P = 0.70). BMI did not significantly affect the heart sound quality in a single attempt (P = 0.73) or in three attempts (P = 0.14). Conclusion Smartphone can be used by non-medical users to record heart sounds in good quality. Age may affect heart sound recording, but hardware, sex, and BMI do not alter the recording.
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Affiliation(s)
- Hongxing Luo
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Pablo Lamata
- School of Biomedical Engineering and Imaging Sciences, King's College London, 5th Floor Becket House, Lambeth Palace Road, London SE1 7EU, UK
| | - Salomé Bazin
- Cellule Studio, Leyton Studios, 15 Argall Avenue, London E107QE, UK
| | - Thea Bautista
- School of Biomedical Engineering and Imaging Sciences, King's College London, 5th Floor Becket House, Lambeth Palace Road, London SE1 7EU, UK
| | - Natsuki Barclay
- School of Biomedical Engineering and Imaging Sciences, King's College London, 5th Floor Becket House, Lambeth Palace Road, London SE1 7EU, UK
| | - Mehrdad Shahmohammadi
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Jolijn M Lubrecht
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Frits W Prinzen
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
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An audio processing pipeline for acquiring diagnostic quality heart sounds via mobile phone. Comput Biol Med 2022; 145:105415. [PMID: 35366471 DOI: 10.1016/j.compbiomed.2022.105415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/22/2022] [Accepted: 03/14/2022] [Indexed: 11/27/2022]
Abstract
Recently, heart sound signals captured using mobile phones have been employed to develop data-driven heart disease detection systems. Such signals are generally captured in person by trained clinicians who can determine if the recorded heart sounds are of diagnosable quality. However, mobile phones have the potential to support heart health diagnostics, even where access to trained medical professionals is limited. To adopt mobile phones as self-diagnostic tools for the masses, we would need to have a mechanism to automatically establish that heart sounds recorded by non-expert users in uncontrolled conditions have the required quality for diagnostic purposes. This paper proposes a quality assessment and enhancement pipeline for heart sounds captured using mobile phones. The pipeline analyzes a heart sound and determines if it has the required quality for diagnostic tasks. Also, in cases where the quality of the captured signal is below the required threshold, the pipeline can improve the quality by applying quality enhancement algorithms. Using this pipeline, we can also provide feedback to users regarding the cause of low-quality signal capture and guide them towards a successful one. We conducted a survey of a group of thirteen clinicians with auscultation skills and experience. The results of this survey were used to inform and validate the proposed quality assessment and enhancement pipeline. We observed a high level of agreement between the survey results and fundamental design decisions within the proposed pipeline. Also, the results indicate that the proposed pipeline can reduce our dependency on trained clinicians for capture of diagnosable heart sounds.
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Jiao Y, Yuan J, Sodimu OM, Qiang Y, Ding Y. Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury. Front Cardiovasc Med 2022; 8:724183. [PMID: 35083295 PMCID: PMC8784602 DOI: 10.3389/fcvm.2021.724183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 12/17/2021] [Indexed: 11/13/2022] Open
Abstract
Deep neural networks have become the mainstream approach for analyzing and interpreting histology images. In this study, we established and validated an interpretable DNN model to assess endomyocardial biopsy (EMB) data of patients with myocardial injury. Deep learning models were used to extract features and classify EMB histopathological images of heart failure cases diagnosed with either ischemic cardiomyopathy or idiopathic dilated cardiomyopathy and non-failing cases (organ donors without a history of heart failure). We utilized the gradient-weighted class activation mapping (Grad-CAM) technique to emphasize injured regions, providing an entry point to assess the dominant morphology in the process of a comprehensive evaluation. To visualize clustered regions of interest (ROI), we utilized uniform manifold approximation and projection (UMAP) embedding for dimension reduction. We further implemented a multi-model ensemble mechanism to improve the quantitative metric (area under the receiver operating characteristic curve, AUC) to 0.985 and 0.992 on ROI-level and case-level, respectively, outperforming the achievement of 0.971 ± 0.017 and 0.981 ± 0.020 based on the sub-models. Collectively, this new methodology provides a robust and interpretive framework to explore local histopathological patterns, facilitating the automatic and high-throughput quantification of cardiac EMB analysis.
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Mazzu-Nascimento T, Evangelista DN, Abubakar O, Roscani MG, Aguilar RS, Chachá SGF, Rosa PRD, Silva DF. Smartphone-Based Screening for Cardiovascular Diseases: A Trend? INTERNATIONAL JOURNAL OF CARDIOVASCULAR SCIENCES 2021. [DOI: 10.36660/ijcs.20210096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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Jani V, Danford DA, Thompson WR, Schuster A, Manlhiot C, Kutty S. The discerning ear: cardiac auscultation in the era of artificial intelligence and telemedicine. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:456-466. [PMID: 36713594 PMCID: PMC9707892 DOI: 10.1093/ehjdh/ztab059] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 06/19/2021] [Indexed: 02/01/2023]
Abstract
Heart murmur, a thoracic auscultatory finding of cardiovascular origin, is extremely common in childhood and can appear at any age from premature newborn to late adolescence. The objective of this review is to provide a modern examination and update of cardiac murmur auscultation in this new era of artificial intelligence (AI) and telemedicine. First, we provide a comprehensive review of the causes and differential diagnosis, clinical features, evaluation, and long-term management of paediatric heart murmurs. Next, we provide a brief history of computer-assisted auscultation and murmur analysis, along with insight into the engineering design of the digital stethoscope. We conclude with a discussion of the paradigm shifting impact of deep learning on murmur analysis, AI-assisted auscultation, and the implications of these technologies on telemedicine in paediatric cardiology. It is our hope that this article provides an updated perspective on the impact of AI on cardiac auscultation for the modern paediatric cardiologist.
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Affiliation(s)
- Vivek Jani
- Department of Pediatrics, Blalock Taussig Thomas Heart Center, The Johns Hopkins Hospital and School of Medicine, M2315, 1800 Orleans St, Baltimore, MD 21287, USA
| | - David A Danford
- Department of Pediatrics, Blalock Taussig Thomas Heart Center, The Johns Hopkins Hospital and School of Medicine, M2315, 1800 Orleans St, Baltimore, MD 21287, USA
| | - W Reid Thompson
- Department of Pediatrics, Blalock Taussig Thomas Heart Center, The Johns Hopkins Hospital and School of Medicine, M2315, 1800 Orleans St, Baltimore, MD 21287, USA
| | - Andreas Schuster
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Göttingen, Germany,German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, 37077 Göttingen, Germany
| | - Cedric Manlhiot
- Department of Pediatrics, Blalock Taussig Thomas Heart Center, The Johns Hopkins Hospital and School of Medicine, M2315, 1800 Orleans St, Baltimore, MD 21287, USA
| | - Shelby Kutty
- Department of Pediatrics, Blalock Taussig Thomas Heart Center, The Johns Hopkins Hospital and School of Medicine, M2315, 1800 Orleans St, Baltimore, MD 21287, USA,Corresponding author. Tel: +1 410 502 3350, Fax: +1 410 955 9897,
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Yang C, Zhang W, Pang Z, Zhang J, Zou D, Zhang X, Guo S, Wan J, Wang K, Pang W. A Low-Cost, Ear-Contactless Electronic Stethoscope Powered by Raspberry Pi for Auscultation of Patients With COVID-19: Prototype Development and Feasibility Study. JMIR Med Inform 2021; 9:e22753. [PMID: 33436354 PMCID: PMC7817256 DOI: 10.2196/22753] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 12/28/2020] [Accepted: 01/12/2021] [Indexed: 12/18/2022] Open
Abstract
Background Chest examination by auscultation is essential in patients with COVID-19, especially those with poor respiratory conditions, such as severe pneumonia and respiratory dysfunction, and intensive cases who are intubated and whose breathing is assisted with a ventilator. However, proper auscultation of these patients is difficult when medical workers wear personal protective equipment and when it is necessary to minimize contact with patients. Objective The objective of our study was to design and develop a low-cost electronic stethoscope enabling ear-contactless auscultation and digital storage of data for further analysis. The clinical feasibility of our device was assessed in comparison to a standard electronic stethoscope. Methods We developed a prototype of the ear-contactless electronic stethoscope, called Auscul Pi, powered by Raspberry Pi and Python. Our device enables real-time capture of auscultation sounds with a microspeaker instead of an earpiece, and it can store data files for later analysis. We assessed the feasibility of using this stethoscope by detecting abnormal heart and respiratory sounds from 8 patients with heart failure or structural heart diseases and from 2 healthy volunteers and by comparing the results with those from a 3M Littmann electronic stethoscope. Results We were able to conveniently operate Auscul Pi and precisely record the patients’ auscultation sounds. Auscul Pi showed similar real-time recording and playback performance to the Littmann stethoscope. The phonocardiograms of data obtained with the two stethoscopes were consistent and could be aligned with the cardiac cycles of the corresponding electrocardiograms. Pearson correlation analysis of amplitude data from the two types of phonocardiograms showed that Auscul Pi was correlated with the Littmann stethoscope with coefficients of 0.3245-0.5570 for healthy participants (P<.001) and of 0.3449-0.5138 among 4 patients (P<.001). Conclusions Auscul Pi can be used for auscultation in clinical practice by applying real-time ear-contactless playback followed by quantitative analysis. Auscul Pi may allow accurate auscultation when medical workers are wearing protective suits and have difficulties in examining patients with COVID-19. Trial Registration ChiCTR.org.cn ChiCTR2000033830; http://www.chictr.org.cn/showproj.aspx?proj=54971.
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Affiliation(s)
- Chuan Yang
- Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Wei Zhang
- Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Zhixuan Pang
- Sewickley Academy Senior High School, Pittsburgh, PA, United States
| | - Jing Zhang
- School of Population Health, University of New South Wales, Sydney, Australia
| | - Deling Zou
- Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xinzhong Zhang
- Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Sicong Guo
- Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jiye Wan
- Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ke Wang
- Department of Cardiac Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Wenyue Pang
- Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China
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Jones B, Reed B, Hayanga JA. Autonomously Driven: Artificial Intelligence in Cardiothoracic Surgery. Ann Thorac Surg 2020; 110:373. [PMID: 32277880 DOI: 10.1016/j.athoracsur.2020.02.074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 02/29/2020] [Indexed: 10/24/2022]
Affiliation(s)
- Brendan Jones
- Department of Cardiovascular and Thoracic Surgery, Heart and Vascular Institute, West Virginia University, 1 Medical Center Dr, Morgantown, WV 26506
| | - Benjamin Reed
- Department of Cardiovascular and Thoracic Surgery, Heart and Vascular Institute, West Virginia University, 1 Medical Center Dr, Morgantown, WV 26506
| | - Jw Awori Hayanga
- Department of Cardiovascular and Thoracic Surgery, Heart and Vascular Institute, West Virginia University, 1 Medical Center Dr, Morgantown, WV 26506.
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Abstract
This article reviews the use of the smartphone in exotic pet medicine. The mobile app is the most instinctive use of the smartphone; however, there are very limited software dedicated to the exotic pet specifically. With an adapter, the smartphone can be attached to a regular endoscope and acts as a small endoscopic unit. Additional devices, such as infrared thermography or ultrasound, can be connected to the smartphone through the micro-USB port. The medical use of the smartphone is still in its infancy in veterinary medicine but can bring several solutions to the exotic pet practitioner and improve point-of-care evaluation.
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Tobore I, Li J, Yuhang L, Al-Handarish Y, Kandwal A, Nie Z, Wang L. Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations. JMIR Mhealth Uhealth 2019; 7:e11966. [PMID: 31376272 PMCID: PMC6696854 DOI: 10.2196/11966] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 04/14/2019] [Accepted: 06/12/2019] [Indexed: 01/10/2023] Open
Abstract
The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.
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Affiliation(s)
- Igbe Tobore
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China.,Graduate University, Chinese Academy of Sciences, Beijing, China
| | - Jingzhen Li
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Liu Yuhang
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yousef Al-Handarish
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Abhishek Kandwal
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zedong Nie
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Lei Wang
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
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Giebel GD, Gissel C. Accuracy of mHealth Devices for Atrial Fibrillation Screening: Systematic Review. JMIR Mhealth Uhealth 2019; 7:e13641. [PMID: 31199337 PMCID: PMC6598422 DOI: 10.2196/13641] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 05/14/2019] [Accepted: 05/14/2019] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Mobile health (mHealth) devices can be used for the diagnosis of atrial fibrillation. Early diagnosis allows better treatment and prevention of secondary diseases like stroke. Although there are many different mHealth devices to screen for atrial fibrillation, their accuracy varies due to different technological approaches. OBJECTIVE We aimed to systematically review available studies that assessed the accuracy of mHealth devices in screening for atrial fibrillation. The goal of this review was to provide a comprehensive overview of available technologies, specific characteristics, and accuracy of all relevant studies. METHODS PubMed and Web of Science databases were searched from January 2014 until January 2019. Our systematic review was performed according to the Preferred Reporting Items for Systematic Review and Meta-Analyses. We restricted the search by year of publication, language, noninvasive methods, and focus on diagnosis of atrial fibrillation. Articles not including information about the accuracy of devices were excluded. RESULTS We found 467 relevant studies. After removing duplicates and excluding ineligible records, 22 studies were included. The accuracy of mHealth devices varied among different technologies, their application settings, and study populations. We described and summarized the eligible studies. CONCLUSIONS Our systematic review identifies different technologies for screening for atrial fibrillation with mHealth devices. A specific technology's suitability depends on the underlying form of atrial fibrillation to be diagnosed. With the suitable use of mHealth, early diagnosis and treatment of atrial fibrillation are possible. Successful application of mHealth technologies could contribute to significantly reducing the cost of illness of atrial fibrillation.
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Affiliation(s)
- Godwin Denk Giebel
- Health Economics, Department of Economics and Business, Justus Liebig University, Giessen, Germany
| | - Christian Gissel
- Health Economics, Department of Economics and Business, Justus Liebig University, Giessen, Germany
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Montinari MR, Minelli S. The first 200 years of cardiac auscultation and future perspectives. J Multidiscip Healthc 2019; 12:183-189. [PMID: 30881010 PMCID: PMC6408918 DOI: 10.2147/jmdh.s193904] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Cardiac auscultation - even with its limitations - is still a valid and economical technique for the diagnosis of cardiovascular diseases, and despite the growing demand for sophisticated imaging techniques, clinical use of the stethoscope in medical practice has not yet been abandoned. In 1816, René-Théophile-Hyacinthe Laënnec invented the stethoscope, while examining a young woman with suspected heart disease, giving rise to mediated auscultation. He described in detail several heart and lung sounds, correlating them with postmortem pathology. Even today, a correct interpretation of heart sounds, integrated with the clinical history and physical examination, allows to detect properly most of the structural heart abnormalities or to evaluate them in a differential diagnosis. However, the lack of organic teaching of auscultation and its inadequate practice have a negative impact on the clinical competence of physicians in training, also reflecting a diminished academic interest in physical semiotic. Medical simulation could be an effective instructional tool in teaching and deepening auscultation. Handheld ultrasound devices could be used for screening or for integrating and improving auscultatory abilities of physicians; the electronic stethoscope, with its new digital capabilities, will help to achieve a correct diagnosis. The availability of innovative representations of the sounds with phono- and spectrograms provides an important aid in diagnosis, in teaching practice and pedagogy. Technological innovations, despite their undoubted value, must complement and not supplant a complete physical examination; clinical auscultation remains an important and cost-effective screening method for the physicians in cardiorespiratory diagnosis. Cardiac auscultation has a future, and the stethoscope has not yet become a medical heirloom.
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Affiliation(s)
- Maria Rosa Montinari
- Department of Biological and Environmental Science and Technology, University of Salento, Lecce, Italy,
| | - Sergio Minelli
- Department of Cardiology, Local Health Unit Lecce, Lecce, Italy
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