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Papunen I, Poutanen T, Ylänen K. Major congenital heart defects are rarely diagnosed after newborns' hospital discharge with modern screening. Acta Paediatr 2024; 113:143-149. [PMID: 37522553 DOI: 10.1111/apa.16928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/16/2023] [Accepted: 07/28/2023] [Indexed: 08/01/2023]
Abstract
AIM Our aim was to assess undiagnosed congenital heart defects (CHD) after newborns' hospital discharge in patients with a murmur or CHD suspicion, to find out the signs that predict CHDs and to estimate the costs of the examinations. METHODS We reviewed retrospective medical records of patients (n = 490) referred for the evaluation of CHD suspicion during 2017-2018. RESULTS The median age of the patients was 2.5 (IQR 0.5-7.4) years. Sixty-three (13%) patients had an abnormal echocardiography. Neither ductal-dependent nor cyanotic CHDs were found. Cardiac interventions were performed for 14 out of 63 (22%) patients. Clinical signs indicating CHDs were murmur grade ≥3 (10/11 [91%] vs. 53/479 [11%], p < 0.001) and harsh murmur (15/44 [34%] vs. 48/446 [11%], p < 0.001). Abnormal electrocardiography did not indicate CHD (8/40 [20%] vs. 55/447 [12%], p = 0.165). The total cost of the examinations was 259 700€. The share of the cost of studies assessed as benign was 59%. CONCLUSION Only a few CHDs were found after newborn hospital discharge among patients who received foetal and newborn screening and were examined due to CHD suspicion. The high number of benign murmurs in children leads to many referrals, resulting in unnecessary healthcare costs.
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Affiliation(s)
- I Papunen
- Tampere Center for Child, Faculty of Medicine and Health Technology, Adolescent and Maternal Health Research, Tampere University, Tampere, Finland
| | - T Poutanen
- Tampere Center for Child, Faculty of Medicine and Health Technology, Adolescent and Maternal Health Research, Tampere University, Tampere, Finland
- Department of Paediatrics, Tampere University Hospital, Tampere, Finland
| | - K Ylänen
- Tampere Center for Child, Faculty of Medicine and Health Technology, Adolescent and Maternal Health Research, Tampere University, Tampere, Finland
- Department of Paediatrics, Tampere University Hospital, Tampere, Finland
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2
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Prince J, Maidens J, Kieu S, Currie C, Barbosa D, Hitchcock C, Saltman A, Norozi K, Wiesner P, Slamon N, Del Grippo E, Padmanabhan D, Subramanian A, Manjunath C, Chorba J, Venkatraman S. Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease. J Am Heart Assoc 2023; 12:e030377. [PMID: 37830333 PMCID: PMC10757522 DOI: 10.1161/jaha.123.030377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 09/11/2023] [Indexed: 10/14/2023]
Abstract
Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to clinical practice. We evaluated a novel suite of Food and Drug Administration-cleared algorithms trained via deep learning on >15 000 heart sound recordings. Methods and Results We validated the algorithms on a data set of 2375 recordings from 615 unique subjects. This data set was collected in real clinical environments using commercially available digital stethoscopes, annotated by board-certified cardiologists, and paired with echocardiograms as the gold standard. To model the algorithm in clinical practice, we compared its performance against 10 clinicians on a subset of the validation database. Our algorithm reliably detected structural murmurs with a sensitivity of 85.6% and specificity of 84.4%. When limiting the analysis to clearly audible murmurs in adults, performance improved to a sensitivity of 97.9% and specificity of 90.6%. The algorithm also reported timing within the cardiac cycle, differentiating between systolic and diastolic murmurs. Despite optimizing acoustics for the clinicians, the algorithm substantially outperformed the clinicians (average clinician accuracy, 77.9%; algorithm accuracy, 84.7%.) Conclusions The algorithms accurately identified murmurs associated with structural heart disease. Our results illustrate a marked contrast between the consistency of the algorithm and the substantial interobserver variability of clinicians. Our results suggest that adopting machine learning algorithms into clinical practice could improve the detection of structural heart disease to facilitate patient care.
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Affiliation(s)
| | | | | | | | | | | | | | - Kambiz Norozi
- Department of Pediatrics, Pediatric CardiologyWestern UniversityLondonONCanada
- Department of Pediatric Cardiology and Intensive Care MedicineHannover Medical SchoolHannoverGermany
- Children Health Research InstituteLondonONCanada
| | | | | | | | - Deepak Padmanabhan
- Sri Jayadeva Institute of Cardiovascular Sciences and ResearchBengaluruIndia
| | - Anand Subramanian
- Sri Jayadeva Institute of Cardiovascular Sciences and ResearchBengaluruIndia
| | | | - John Chorba
- Division of Cardiology, Zuckerberg San Francisco General Hospital, Department of MedicineUniversity of California San FranciscoSan FranciscoCAUSA
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Howard-Quijano K, Saraf K, Borgstrom P, Baek C, Wasko M, Zhang X, Zheng Y, Saba S, Mukkamala R, Kaiser W, Mahajan A. Evaluation of Wearable Acoustic Sensors and Machine Learning Algorithms for Automated Measurement of Left Ventricular Ejection Fraction. Am J Cardiol 2023; 200:87-94. [PMID: 37307784 DOI: 10.1016/j.amjcard.2023.04.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/12/2023] [Accepted: 04/28/2023] [Indexed: 06/14/2023]
Abstract
Left ventricular ejection fraction (EF) is a predictor of mortality and guides clinical decisions. Although transthoracic echocardiography (TTE) is commonly used for measuring EF, it has limitations, such as subjectivity and requires expert personnel. Advancements in biosensor technology and artificial intelligence are allowing systems capable of determining left ventricular function and providing automated measurement of EF. In this study, we tested new wearable automated real-time biosensors (Cardiac Performance System [CPS]) that compute EF using waveform machine learning on cardiac acoustic signals. The primary aim was to compare the accuracy of CPS EF with TTE EF. Adult patients presenting to cardiology, presurgical, and diagnostic radiology clinical settings in an academic center were enrolled. TTE examination was performed by a sonographer, followed immediately by a 3-minute recording of acoustic signals from CPS biosensors placed on the chest by nonexpert personnel. TTE EF was calculated offline using the Simpson biplane method. A total of 81 patients (aged 19 to 88 years, 27 women, 20% to 80% EF) were included. Deming regression and Bland-Altman analysis were performed to assess the accuracy of CPS EF against TTE EF. Both Deming regression (slope 0.9981; intercept 0.03415%) and Bland-Altman analysis (bias -0.0247%; limits of agreement [-11.65, 11.60]%) demonstrated equivalency between CPS EF and TTE EF. The receiver operating characteristic for measuring sensitivity and specificity of CPS in identifying subjects with abnormal EF showed an area under the curve value of 0.974 for identifying EF <35% and 0.916 for detecting EF <50% CPS EF intraoperator and interoperator assessments demonstrated low variability. In conclusion, this technology measuring cardiac function from noninvasive biosensors and machine learning on acoustic signals provides an accurate EF measurement that is automated, real-time, and acquired rapidly by personnel with minimal training.
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Affiliation(s)
| | - Kanav Saraf
- Samueli School of Engineering, University of California Los Angeles, Los Angeles, California
| | - Per Borgstrom
- Samueli School of Engineering, University of California Los Angeles, Los Angeles, California
| | - Christopher Baek
- Samueli School of Engineering, University of California Los Angeles, Los Angeles, California
| | - Michael Wasko
- Samueli School of Engineering, University of California Los Angeles, Los Angeles, California
| | - Xu Zhang
- Samueli School of Engineering, University of California Los Angeles, Los Angeles, California
| | - Yi Zheng
- Samueli School of Engineering, University of California Los Angeles, Los Angeles, California
| | - Samir Saba
- University of Pittsburgh Medical Center, Pittsburgh, Pensylvannia
| | - Rama Mukkamala
- University of Pittsburgh Medical Center, Pittsburgh, Pensylvannia
| | - William Kaiser
- Samueli School of Engineering, University of California Los Angeles, Los Angeles, California
| | - Aman Mahajan
- University of Pittsburgh Medical Center, Pittsburgh, Pensylvannia
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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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Bordbar A, Kashaki M, Vafapour M, Sepehri AA. Determining the incidence of heart malformations in neonates: A novel and clinically approved solution. Front Pediatr 2023; 11:1058947. [PMID: 37009269 PMCID: PMC10050760 DOI: 10.3389/fped.2023.1058947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/27/2023] [Indexed: 04/04/2023] Open
Abstract
Background Screening for critical congenital heart defects should be performed as early as possible and is essential for saving the lives of children and reducing the incidence of undetected adult congenital heart diseases. Heart malformations remain unrecognized at birth in more than 50% of neonates at maternity hospitals. Accurate screening for congenital heart malformations is possible using a certified and internationally patented digital intelligent phonocardiography machine. This study aimed to assess the actual incidence of heart defects in neonates. A pre-evaluation of the incidence of unrecognized severe and critical congenital heart defects at birth in our well-baby nursery was also performed. Methods We conducted the Neonates Cardiac Monitoring Research Project (ethics approval number: IR-IUMS-FMD. REC.1398.098) at the Shahid Akbarabadi Maternity Hospital. This study was a retrospective analysis of congenital heart malformations observed after screening 840 neonates. Using a double-blind format, 840 neonates from the well-baby nursery were randomly chosen to undergo routine clinical examinations at birth and digital intelligent phonocardiogram examinations. A pediatric cardiologist performed echocardiography for each neonate classified as having abnormal heart sounds using an intelligent machine or during routine medical examinations. If the pediatric cardiologist requested a follow-up examination, then the neonate was considered to have a congenital heart malformation, and the cumulative incidence was calculated accordingly. Results The incidence of heart malformations in our well-baby nursery was 5%. Furthermore, 45% of heart malformations were unrecognized in neonates at birth, including one critical congenital heart defect. The intelligent machine interpreted innocent murmurs as healthy heart sound. Conclusion We accurately and cost-effectively screened for congenital heart malformations in all neonates in our hospital using a digital intelligent phonocardiogram. Using an intelligent machine, we successfully identified neonates with CCHD and congenital heart defects that could not be detected using standard medical examinations. The Pouya Heart machine can record and analyze sounds with a spectral power level lower than the minimum level of the human hearing threshold. Furthermore, by redesigning the study, the identification of previously unrecognized heart malformations could increase to 58%.
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Affiliation(s)
- Arash Bordbar
- Shahid Akbarabadi Clinical Research & Development Unit (ShACRDU), Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Mandana Kashaki
- Shahid Akbarabadi Clinical Research & Development Unit (ShACRDU), Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Maryam Vafapour
- Department of Pediatrics, Ali-Asghar Children’s Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Amir A. Sepehri
- Biomedical R&D Department, CAPIS Research and Development Co., Mons, Belgium
- Correspondence: Amir A. Sepehri
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Anton O, Dore H, Rendon-Morales E, Aviles-Espinosa R, Seddon P, Wertheim D, Fernandez R, Rabe H. Non-invasive sensor methods used in monitoring newborn babies after birth, a clinical perspective. Matern Health Neonatol Perinatol 2022; 8:9. [DOI: 10.1186/s40748-022-00144-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 10/25/2022] [Indexed: 11/24/2022] Open
Abstract
Abstract
Background
Reducing the global new-born mortality is a paramount challenge for humanity. There are approximately 786,323 live births in the UK each year according to the office for National Statistics; around 10% of these newborn infants require assistance during this transition after birth. Each year around, globally around 2.5 million newborns die within their first month. The main causes are complications due to prematurity and during delivery. To act in a timely manner and prevent further damage, health professionals should rely on accurate monitoring of the main vital signs heart rate and respiratory rate.
Aims
To present a clinical perspective on innovative, non-invasive methods to monitor heart rate and respiratory rate in babies highlighting their advantages and limitations in comparison with well-established methods.
Methods
Using the data collected in our recently published systematic review we highlight the barriers and facilitators for the novel sensor devices in obtaining reliable heart rate measurements. Details about difficulties related to the application of sensors and interfaces, time to display, and user feedback are explored. We also provide a unique overview of using a non-invasive respiratory rate monitoring method by extracting RR from the pulse oximetry trace of newborn babies.
Results
Novel sensors to monitor heart rate offer the advantages of minimally obtrusive technologies but have limitations due to movement artefact, bad sensor coupling, intermittent measurement, and poor-quality recordings compared to gold standard well established methods. Respiratory rate can be derived accurately from pleth recordings in infants.
Conclusion
Some limitations have been identified in current methods to monitor heart rate and respiratory rate in newborn babies. Novel minimally invasive sensors have advantages that may help clinical practice. Further research studies are needed to assess whether they are sufficiently accurate, practical, and reliable to be suitable for clinical use.
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Melvin RL, Broyles MG, Duggan EW, John S, Smith AD, Berkowitz DE. Artificial Intelligence in Perioperative Medicine: A Proposed Common Language With Applications to FDA-Approved Devices. Front Digit Health 2022; 4:872675. [PMID: 35547090 PMCID: PMC9081677 DOI: 10.3389/fdgth.2022.872675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 03/29/2022] [Indexed: 11/28/2022] Open
Abstract
As implementation of artificial intelligence grows more prevalent in perioperative medicine, a clinician's ability to distinguish differentiating aspects of these algorithms is critical. There are currently numerous marketing and technical terms to describe these algorithms with little standardization. Additionally, the need to communicate with algorithm developers is paramount to actualize effective and practical implementation. Of particular interest in these discussions is the extent to which the output or predictions of algorithms and tools are understandable by medical practitioners. This work proposes a simple nomenclature that is intelligible to both clinicians and developers for quickly describing the interpretability of model results. There are three high-level categories: transparent, translucent, and opaque. To demonstrate the applicability and utility of this terminology, these terms were applied to the artificial intelligence and machine-learning-based products that have gained Food and Drug Administration approval. During this review and categorization process, 22 algorithms were found with perioperative utility (in a database of 70 total algorithms), and 12 of these had publicly available citations. The primary aim of this work is to establish a common nomenclature that will expedite and simplify descriptions of algorithm requirements from clinicians to developers and explanations of appropriate model use and limitations from developers to clinicians.
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Affiliation(s)
- Ryan L. Melvin
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
- *Correspondence: Ryan L. Melvin
| | - Matthew G. Broyles
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Elizabeth W. Duggan
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Sonia John
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Andrew D. Smith
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Dan E. Berkowitz
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
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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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A survey of general practitioners' knowledge and clinical practice in relation to valvular heart disease. Ir J Med Sci 2021; 191:777-784. [PMID: 33895965 PMCID: PMC8983626 DOI: 10.1007/s11845-021-02619-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 04/04/2021] [Indexed: 01/10/2023]
Abstract
INTRODUCTION General practice has a key role in diagnosing patients with valvular heart disease (VHD) and referring them to appropriate services. METHODS An anonymous survey was conducted to assess the knowledge and clinical practice behaviour of Irish general practitioners (GPs) in relation to VHD. In addition to demographic data, the survey captured information in the following domains: knowledge of VHD prevalence, knowledge of contemporary VHD treatments, barriers to diagnosis, and referral patterns. To augment responses, a monetary prize (donated to charity) was offered and the survey was also disseminated using social media and by the Irish College of General Practitioners. RESULTS Valid survey responses were received from 197 GPs. The sample was well-balanced by gender, number of years in practice, and practice setting. A small proportion of GPs (16.8%) used a stethoscope to examine for VHD in all patients over 60 years, a figure that rose to 22.3% in patients over 75. Approximately half of participants (48%) felt confident in their ability to detect and diagnose VHD using a stethoscope, and 74% felt lack of access to echocardiography was a major barrier to making a VHD diagnosis. There was a high level of awareness among GPs of minimally invasive nonsurgical interventions now available for VHD treatment. DISCUSSION Irish GPs displayed good understanding of contemporary VHD treatment options but reported low confidence and inconsistent practices in evaluating patients for VHD. Improved access to echocardiography might help address these deficiencies, but reorganisation of services will be required in a resource-limited public health service.
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Soto-Murillo MA, Galván-Tejada JI, Galván-Tejada CE, Celaya-Padilla JM, Luna-García H, Magallanes-Quintanar R, Gutiérrez-García TA, Gamboa-Rosales H. Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods. Healthcare (Basel) 2021; 9:317. [PMID: 33809283 PMCID: PMC7999739 DOI: 10.3390/healthcare9030317] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 02/25/2021] [Accepted: 03/04/2021] [Indexed: 11/16/2022] Open
Abstract
The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems.
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Affiliation(s)
- Manuel A. Soto-Murillo
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (M.A.S.-M.); (C.E.G.-T.); (J.M.C.-P.); (H.L.-G.); (R.M.-Q.); (H.G.-R.)
| | - Jorge I. Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (M.A.S.-M.); (C.E.G.-T.); (J.M.C.-P.); (H.L.-G.); (R.M.-Q.); (H.G.-R.)
| | - Carlos E. Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (M.A.S.-M.); (C.E.G.-T.); (J.M.C.-P.); (H.L.-G.); (R.M.-Q.); (H.G.-R.)
| | - Jose M. Celaya-Padilla
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (M.A.S.-M.); (C.E.G.-T.); (J.M.C.-P.); (H.L.-G.); (R.M.-Q.); (H.G.-R.)
| | - Huizilopoztli Luna-García
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (M.A.S.-M.); (C.E.G.-T.); (J.M.C.-P.); (H.L.-G.); (R.M.-Q.); (H.G.-R.)
| | - Rafael Magallanes-Quintanar
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (M.A.S.-M.); (C.E.G.-T.); (J.M.C.-P.); (H.L.-G.); (R.M.-Q.); (H.G.-R.)
| | - Tania A. Gutiérrez-García
- Departamento de Ciencias Computacionales, Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, Guadalajara, Jalisco 44430, Mexico;
| | - Hamurabi Gamboa-Rosales
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (M.A.S.-M.); (C.E.G.-T.); (J.M.C.-P.); (H.L.-G.); (R.M.-Q.); (H.G.-R.)
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Detection of Pathologic Heart Murmurs Using a Piezoelectric Sensor. SENSORS 2021; 21:s21041376. [PMID: 33669261 PMCID: PMC7920032 DOI: 10.3390/s21041376] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 02/03/2021] [Accepted: 02/12/2021] [Indexed: 11/27/2022]
Abstract
This study aimed to evaluate the capability of a piezoelectric sensor to detect a heart murmur in patients with congenital heart defects. Heart sounds and murmurs were recorded using a piezoelectric sensor and an electronic stethoscope in healthy neonates (n = 9) and in neonates with systolic murmurs caused by congenital heart defects (n = 9) who were born at a hospital. Signal data were digitally filtered by high-pass filtering, and the envelope of the processed signals was calculated. The amplitudes of systolic murmurs were evaluated using the signal-to-noise ratio and compared between healthy neonates and those with congenital heart defects. In addition, the correlation between the amplitudes of systolic murmurs recorded by the piezoelectric sensor and electronic stethoscope was determined. The amplitudes of systolic murmurs detected by the piezoelectric sensor were significantly higher in neonates with congenital heart defects than in healthy neonates (p < 0.01). Systolic murmurs recorded by the piezoelectric sensor had a strong correlation with those recorded by the electronic stethoscope (ρ = 0.899 and p < 0.01, respectively). The piezoelectric sensor can detect heart murmurs objectively. Mechanical improvement and automatic analysis algorithms are expected to improve recording in the future.
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12
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Gómez-Quintana S, Schwarz CE, Shelevytsky I, Shelevytska V, Semenova O, Factor A, Popovici E, Temko A. A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram. Healthcare (Basel) 2021; 9:healthcare9020169. [PMID: 33562544 PMCID: PMC7914824 DOI: 10.3390/healthcare9020169] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 01/24/2021] [Accepted: 02/03/2021] [Indexed: 11/16/2022] Open
Abstract
The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.
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Affiliation(s)
- Sergi Gómez-Quintana
- Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland; (O.S.); (E.P.); (A.T.)
- Correspondence:
| | - Christoph E. Schwarz
- Irish Centre for Maternal and Child Health Research, University College Cork, T12 K8AF Cork, Ireland;
| | - Ihor Shelevytsky
- Faculty of Information Technologies, Kryvyi Rih Institute of Economics, 50479 Kryvyi Rih, Ukraine;
| | - Victoriya Shelevytska
- Faculty of Postgraduate Education, Dnipropetrovsk Medical Academy of Health, 49098 Dnipro, Ukraine;
| | - Oksana Semenova
- Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland; (O.S.); (E.P.); (A.T.)
| | - Andreea Factor
- Department of Anatomy and Neuroscience, University College Cork, T12 K8AF Cork, Ireland;
| | - Emanuel Popovici
- Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland; (O.S.); (E.P.); (A.T.)
| | - Andriy Temko
- Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland; (O.S.); (E.P.); (A.T.)
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13
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Thoenes M, Agarwal A, Grundmann D, Ferrero C, McDonald A, Bramlage P, Steeds RP. Narrative review of the role of artificial intelligence to improve aortic valve disease management. J Thorac Dis 2021; 13:396-404. [PMID: 33569220 PMCID: PMC7867819 DOI: 10.21037/jtd-20-1837] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Valvular heart disease (VHD) is a chronic progressive condition with an increasing prevalence in the Western world due to aging populations. VHD is often diagnosed at a late stage when patients are symptomatic and the outcomes of therapy, including valve replacement, may be sub-optimal due the development of secondary complications, including left ventricular (LV) dysfunction. The clinical application of artificial intelligence (AI), including machine learning (ML), has promise in supporting not only early and more timely diagnosis, but also hastening patient referral and ensuring optimal treatment of VHD. As physician auscultation lacks accuracy in diagnosis of significant VHD, computer-aided auscultation (CAA) with the help of a commercially available digital stethoscopes improves the detection and classification of heart murmurs. Although used little in current clinical practice, CAA can screen large populations at low cost with high accuracy for VHD and faciliate appropriate patient referral. Echocardiography remains the next step in assessment and planning management and AI is delivering major changes in speeding training, improving image quality by pattern recognition and image sorting, as well as automated measurement of multiple variables, thereby improving accuracy. Furthermore, AI then has the potential to hasten patient disposal, by automated alerts for red-flag findings, as well as decision support in dealing with results. In management, there is great potential in ML-enabled tools to support comprehensive disease monitoring and individualized treatment decisions. Using data from multiple sources, including demographic and clinical risk data to image variables and electronic reports from electronic medical records, specific patient phenotypes may be identified that are associated with greater risk or modeled to the estimate trajectory of VHD progression. Finally, AI algorithms are of proven value in planning intervention, facilitating transcatheter valve replacement by automated measurements of anatomical dimensions derived from imaging data to improve valve selection, valve size and method of delivery.
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Affiliation(s)
- Martin Thoenes
- Léman Research Institute, Schaffhausen am Rheinfall, Switerzland
| | | | | | - Carmen Ferrero
- Departamento de Farmacia y Tecnología Farmacéutica, Facultad de Farmacia, Universidad de Sevilla, Spain
| | | | - Peter Bramlage
- Institute for Pharmacology and Preventive Medicine, Cloppenburg, Germany
| | - Richard P Steeds
- Queen Elizabeth Hospital & Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
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14
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Sztechman D, Żera T, Czarzasta K, Wojciechowska M, Szczepańska-Sadowska E, Cudnoch-Jędrzejewska A. Transthoracic echocardiography: from guidelines for humans to cardiac ultrasound of the heart in rats. Physiol Meas 2020; 41:10TR02. [PMID: 33164918 DOI: 10.1088/1361-6579/abb3a3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Ultrasound examination of the heart is a cornerstone of clinical evaluation of patients with established or suspected cardiovascular conditions. Advancements in ultrasound imaging technology have brought transthoracic echocardiography to preclinical murine models of cardiovascular diseases. The translational potential of cardiac ultrasound is critically important in rat models of myocardial infarction and ischemia-reperfusion injury, congestive heart failure, arterial hypertension, cardiac hypertrophy, pulmonary hypertension, right heart failure, Takotsubo cardiomyopathy, hypertrophic and dilated cardiomyopathies, developmental disorders, and metabolic syndrome. Modern echocardiographic machines capable of high-frame-rate image acquisition and fitted with high-frequency transducers allow for cardiac ultrasound in rats that yields most of the echocardiographic measurements and indices recommended by international guidelines for cardiac ultrasound in human patients. Among them are dimensions of cardiac chambers and walls, indices of systolic and diastolic cardiac function, and valvular function. In addition, measurements of cardiac dimensions and ejection fraction can be significantly improved by intravenous administration of ultrasound enhancing agents (UEAs). In this article we discuss echocardiography in rats, describe a technique for minimally invasive intravenous administration of UEAs via the saphenous vein and present a step-by-step approach to cardiac ultrasound in rats.
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Affiliation(s)
- Dorota Sztechman
- Department of Experimental and Clinical Physiology, Laboratory of Centre for Preclinical Research, Medical University of Warsaw, Banacha 1B, 02-097 Warsaw, Poland
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15
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Feltman DM, Moore GP, Beck AF, Siffermann E, Bellieni C, Lantos J. Seeking Normalcy as the Curve Flattens: Ethical Considerations for Pediatricians Managing Collateral Damage of Coronavirus Disease-2019. J Pediatr 2020; 225:233-238. [PMID: 32599031 PMCID: PMC7319624 DOI: 10.1016/j.jpeds.2020.06.067] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 06/03/2020] [Accepted: 06/22/2020] [Indexed: 12/17/2022]
Affiliation(s)
- Dalia M Feltman
- NorthShore University HealthSystem Evanston Hospital, Evanston, IL; University of Chicago Pritzker School of Medicine, Chicago, IL.
| | - Gregory P Moore
- Division of Neonatology, Children's Hospital of Eastern Ontario and The Ottawa Hospital, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Andrew F Beck
- Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, OH
| | | | - Carlo Bellieni
- Pediatric Intensive Care Unit, Le Scotte University Hospital, Siena, Italy
| | - John Lantos
- Department of Pediatrics and Children's Mercy Bioethics Center, Children's Mercy Hospital, Kansas City, MO
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16
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Li Y, Zhang T, Yang Y, Gao Y. Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects. J Int Med Res 2020; 48:300060520945141. [PMID: 32924683 PMCID: PMC7493240 DOI: 10.1177/0300060520945141] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Artificial intelligence (AI)-aided decision support has developed rapidly to meet the needs for effective analysis of substantial data sets from electronic medical records and medical images generated daily, and computer-assisted intelligent drug design. In clinical practice, paediatricians make medical decisions after obtaining a large amount of information about symptoms, physical examinations, laboratory test indicators, special examinations and treatments. This information is used in combination with paediatricians' knowledge and experience to form the basis of clinical decisions. This diagnosis and therapeutic strategy development based on large amounts of information storage can be applied to both large clinical databases and data for individual patients. To date, AI applications have been of great value in intelligent diagnosis and treatment, intelligent image recognition, research and development of intelligent drugs and intelligent health management. This review aims to summarize recent advances in the research and clinical use of AI in paediatrics.
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Affiliation(s)
- Yawen Li
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
| | - Tiannan Zhang
- Department of Pediatrics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yushan Yang
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yuchen Gao
- School of Economics and Management, Tsinghua University, Beijing, China
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17
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Penslar J, Webster RJ, Jetty R. Nonauscultatory clinical criteria are sensitive for cardiac pathology in low-risk paediatric heart murmurs. Paediatr Child Health 2020; 26:294-298. [PMID: 34336057 DOI: 10.1093/pch/pxaa067] [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: 09/12/2019] [Accepted: 04/28/2020] [Indexed: 11/13/2022] Open
Abstract
Background Healthy children with likely innocent heart murmurs are frequently referred to cardiologists for reassurance. Existing guidelines that advise against these referrals are not consistently followed partly because they involve subjective auscultatory judgements with which many care providers are uncomfortable. Here, we investigate whether clinical criteria with no subjective auscultatory component are sensitive for cardiac pathology. Methods A retrospective chart review was performed of all new patients seen in our paediatric cardiology clinic for assessment of a murmur from January 1, 2016 through June 30, 2018. Patients were characterized as "low-risk" if they met all of the following criteria: asymptomatic; normal physical examination other than the murmur; no risk factors for congenital heart disease; and age over 12 months. The primary outcomes were the sensitivity for ruling out pathology and the negative predictive value of the proposed criteria. Results Of 915 total patients, 214 met the low-risk criteria. The sensitivity of our criteria for ruling out pathology was 97.2% (95% confidence interval 94.1% to 99.0%) and the negative predictive value was also 97.2% (95% confidence interval 94.0% to 98.7%). Six of the 214 low-risk patients had pathology (2.8%; 95% confidence interval 1.3% to 6.0%), none of which has required intervention since diagnosis. Each of these six children had a murmur that sounded pathological to the auscultating cardiologist. Conclusions Basic clinical criteria that do not require auscultation are highly sensitive for ruling out significant cardiac pathology in children over 12 months of age.
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Affiliation(s)
- Joshua Penslar
- Faculty of Medicine, University of Ottawa, Ottawa, Canada.,Department of Pediatrics, Children's Hospital of Eastern Ontario, Ottawa, Canada
| | | | - Radha Jetty
- Faculty of Medicine, University of Ottawa, Ottawa, Canada.,Department of Pediatrics, Children's Hospital of Eastern Ontario, Ottawa, Canada
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18
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Vallejo Valdezate LÁ, Santamaria-Vazquez E, Hornero R, Gil-Carcedo E, Herrero-Calvo D. Desarrollo de una aplicación para configurar el teléfono inteligente como fonendoscopio para profesionales sanitarios con deficiencias auditivas. REVISTA ORL 2020. [DOI: 10.14201/orl.22751] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Introducción y objetivos. La hipoacusia supone un severo hándicap para cualquier profesional cuya actividad se base en el reconocimiento de sonidos. En el caso de profesionales sanitarios, la auscultación constituye una actividad rutinaria y el padecimiento de hipoacusia la limita en grado variable en función de la severidad de la misma. Aquellos profesionales sanitarios que por la severidad de su hipoacusia necesitan del uso de audífonos ven dificultadas las rutinas basadas en el uso del fonendoscopio. El objetivo del presente trabajo es describir el proceso llevado a cabo para desarrollar una aplicación para smartphones, que permita la reproducción en tiempo real, el registro y el análisis de sonidos para facilitar la labor de profesionales sanitarios con hipoacusia. Métodos. Hemos recogido somatosonidos cardiacos, pulmonares y abdominales de sujetos sanos y patológicos a fin de caracterizarles frecuencialmente. Posteriormente, la aplicación ha sido diseñada con el objetivo de facilitar la labor diagnóstica del profesional sanitario hipoacúsico, teniendo en cuenta la caracterización anterior para optimizar la escucha y el análisis de sonidos cardiacos, pulmonares y abdominales. Además, con el objetivo de maximizar el número de dispositivos compatibles, ha sido desarrollada para el sistema operativo Android, el más extendido del mercado. Resultados. Hemos desarrollado una App. para smartphones (a la que hemos llamado STETHOSCOPE) basados en Android que configura el teléfono como un fonendoscopio recogiendo el somatosonido a través de su micrófono (siendo posible utilizar exclusivamente el micrófono interno del smartphone o bien micrófonos externos de alta calidad a través de su conector JACK), procesando la señal hasta enviarla finalmente por Bluetooth a los audífonos del profesional hipoacúsico. Esta aplicación permite grabar y representar gráficamente sonidos cardiacos, pulmonares y abdominales en dispositivos Android y almacenarlos en formato WAV, según las recomendaciones del Instituto de Ingeniería Eléctrica y Electrónica (Institute of Electrical and Electronics Engineers, IEEE), utilizando una codificación FLOAT de 32 bits sin compresión posibilitando su archivo, comparación o compartición con otros profesionales. Conclusiones. En este estudio presentamos una aplicación destinada a utilizar el smartphone como fonendoscopio, haciendo llegar el sonido captado a la ayuda auditiva (por vía inalámbrica) del profesional sanitario hipoacúsico que lo precise.
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19
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Ramanathan A, Zhou L, Marzbanrad F, Roseby R, Tan K, Kevat A, Malhotra A. Digital stethoscopes in paediatric medicine. Acta Paediatr 2019; 108:814-822. [PMID: 30536440 DOI: 10.1111/apa.14686] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 11/29/2018] [Accepted: 12/04/2018] [Indexed: 12/30/2022]
Abstract
AIM To explore, synthesise and discuss currently available digital stethoscopes (DS) and the evidence for their use in paediatric medicine. METHODS Systematic review and narrative synthesis of digital stethoscope use in paediatrics following searches of OVID Medline, Embase, Scopus, PubMed and Google Scholar databases. RESULTS Six digital stethoscope makes were identified to have been used in paediatric focused studies so far. A total of 25 studies of DS use in paediatrics were included. We discuss the use of digital stethoscope technology in current paediatric medicine, comment on the technical properties of the available devices, the effectiveness and limitations of this technology, and potential uses in the fields of paediatrics and neonatology, from telemedicine to computer-aided diagnostics. CONCLUSION Further validation and testing of available DS devices is required. Comparison studies between different types of DS would be useful in identifying strengths and flaws of each DS as well as identifying clinical situations for which each may be most appropriately suited.
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Affiliation(s)
| | - Lindsay Zhou
- Monash Newborn Monash Children's Hospital Melbourne VIC Australia
| | - Faezeh Marzbanrad
- Department of Electrical and Computer Systems Engineering Monash University Melbourne VIC Australia
| | - Robert Roseby
- Department of Paediatrics Monash University Melbourne VIC Australia
- Department of Paediatric Respiratory Medicine Monash Children's Hospital Melbourne VIC Australia
| | - Kenneth Tan
- Department of Paediatrics Monash University Melbourne VIC Australia
- Monash Newborn Monash Children's Hospital Melbourne VIC Australia
- The Ritchie Centre Hudson Institute of Medical Research Melbourne VIC Australia
| | - Ajay Kevat
- Department of Paediatrics Monash University Melbourne VIC Australia
| | - Atul Malhotra
- Department of Paediatrics Monash University Melbourne VIC Australia
- Monash Newborn Monash Children's Hospital Melbourne VIC Australia
- The Ritchie Centre Hudson Institute of Medical Research Melbourne VIC Australia
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20
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Thompson WR, Reinisch AJ, Unterberger MJ, Schriefl AJ. Artificial Intelligence-Assisted Auscultation of Heart Murmurs: Validation by Virtual Clinical Trial. Pediatr Cardiol 2019; 40:623-629. [PMID: 30542919 DOI: 10.1007/s00246-018-2036-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2018] [Accepted: 12/05/2018] [Indexed: 11/25/2022]
Abstract
Artificial intelligence (AI) has potential to improve the accuracy of screening for valvular and congenital heart disease by auscultation. However, despite recent advances in signal processing and classification algorithms focused on heart sounds, clinical acceptance of this technology has been limited, in part due to lack of objective performance data. We hypothesized that a heart murmur detection algorithm could be quantitatively and objectively evaluated by virtual clinical trial. All cases from the Johns Hopkins Cardiac Auscultatory Recording Database (CARD) with either a pathologic murmur, an innocent murmur or no murmur were selected. The test algorithm, developed independently of CARD, analyzed each recording using an automated batch processing protocol. 3180 heart sound recordings from 603 outpatient visits were selected from CARD. Algorithm estimation of heart rate was similar to gold standard. Sensitivity and specificity for detection of pathologic cases were 93% (CI 90-95%) and 81% (CI 75-85%), respectively, with accuracy 88% (CI 85-91%). Performance varied according to algorithm certainty measure, age of patient, heart rate, murmur intensity, location of recording on the chest and pathologic diagnosis. This is the first reported comprehensive and objective evaluation of an AI-based murmur detection algorithm to our knowledge. The test algorithm performed well in this virtual clinical trial. This strategy can be used to efficiently compare performance of other algorithms against the same dataset and improve understanding of the potential clinical usefulness of AI-assisted auscultation.
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Affiliation(s)
- W Reid Thompson
- Division of Pediatric Cardiology, Johns Hopkins Children's Center, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, MD, 21287, USA.
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21
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Grgic-Mustafic R, Baik-Schneditz N, Schwaberger B, Mileder L, Binder-Heschl C, Pansy J, Koestenberger M, Urlesberger B, Avian A, Pichler G. Novel algorithm to screen for heart murmurs using computer-aided auscultation in neonates: a prospective single center pilot observational study. Minerva Pediatr 2018; 71:221-228. [PMID: 29968444 DOI: 10.23736/s0026-4946.18.04974-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND Automated detection of heart murmurs with computer-aided auscultation is not yet in clinical routine use. Aim of this study was to test sensitivity and specificity of a novel prototype algorithm in automated detection of heart murmurs from digitally recorded phonocardiograms in neonates admitted at the Neonatal Intensive Care Unit. METHODS In a prospective pilot observational study from November 2012 to December 2013 auscultations by pediatricians and computer aided auscultation were performed within 12 hours of neonatal echocardiography. Echocardiography was defined as pathological when resulting in any clinical consequences or causing murmur. Phonocardiograms and auscultation were defined as pathological if a murmur was detected. Phonocardiograms were analyzed offline with a novel algorithm prototype (CSD Labs, Graz, Austria) for detection of murmurs in neonates in a first run and with an optimized algorithm in a second run and were compared with echocardiography. Sensitivity and specificity of auscultation by pediatrician and computer aided auscultation were analyzed. RESULTS Thirty-six neonates (gestational age: 36±3 weeks) were included. Twenty-three (64%) neonates had pathological or murmur causing findings in echocardiography (positive echocardiography). Sensitivity and specificity of auscultation by pediatrician were 17% and 100%, respectively. In comparison to auscultation by pediatrician sensitivity of first run and second run were significantly higher with 70% and 83%, respectively. Specificity of first run and second run were 77% and 85%, respectively. CONCLUSIONS Phonocardiogram analysis using the novel algorithm prototype had a higher sensitivity than auscultation by pediatrician in detecting positive echocardiography findings in neonates.
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Affiliation(s)
- Renata Grgic-Mustafic
- Research Unit for Neonatal Micro- and Macrocirculation, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria.,Division of Neonatology, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria
| | - Nariae Baik-Schneditz
- Research Unit for Neonatal Micro- and Macrocirculation, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria.,Division of Neonatology, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria
| | - Bernhard Schwaberger
- Research Unit for Neonatal Micro- and Macrocirculation, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria.,Division of Neonatology, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria
| | - Lukas Mileder
- Research Unit for Neonatal Micro- and Macrocirculation, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria.,Division of Neonatology, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria.,Division of Pediatric Cardiology, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria
| | - Corinna Binder-Heschl
- Research Unit for Neonatal Micro- and Macrocirculation, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria.,Division of Neonatology, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria
| | - Jasmin Pansy
- Research Unit for Neonatal Micro- and Macrocirculation, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria.,Division of Neonatology, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria
| | - Martin Koestenberger
- Division of Pediatric Cardiology, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria
| | - Berndt Urlesberger
- Research Unit for Neonatal Micro- and Macrocirculation, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria.,Division of Neonatology, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria
| | - Alexander Avian
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Gerhard Pichler
- Research Unit for Neonatal Micro- and Macrocirculation, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria -
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22
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Lefort B, Cheyssac E, Soulé N, Poinsot J, Vaillant MC, Nassimi A, Chantepie A. Auscultation While Standing: A Basic and Reliable Method to Rule Out a Pathologic Heart Murmur in Children. Ann Fam Med 2017; 15:523-528. [PMID: 29133490 PMCID: PMC5683863 DOI: 10.1370/afm.2105] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Revised: 03/23/2017] [Accepted: 04/11/2017] [Indexed: 11/09/2022] Open
Abstract
PURPOSE The distinction between physiologic (innocent) and pathologic (organic) heart murmurs is not always easy in routine practice, leading too often to unnecessary cardiology referrals and expensive investigations. We aimed to test the hypothesis that the complete disappearance of murmur on standing can exclude cardiac disease in children. METHODS From January 2014 to January 2015, we prospectively included 194 consecutive children aged 2 to 18 years who were referred for heart murmur evaluation to pediatric cardiologists at 2 French medical centers. Heart murmur characteristics while supine and then while standing were recorded, and an echo-cardiogram was performed. RESULTS Overall, 30 (15%) of the 194 children had a pathologic heart murmur as determined by an abnormal echocardiogram. Among the 100 children (51%) who had a murmur that was present while they were supine but completely disappeared when they stood up, only 2 had a pathologic murmur, and just 1 of them needed further evaluation. Complete disappearance of the heart murmur on standing therefore excluded a pathologic murmur with a high positive predictive value of 98% and specificity of 93%, albeit with a lower sensitivity of 60%. CONCLUSIONS Disappearance of a heart murmur on standing is a reliable clinical tool for ruling out pathologic heart murmurs in children aged 2 years and older. This basic clinical assessment would avoid many unnecessary referrals to cardiologists.
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Affiliation(s)
- Bruno Lefort
- Children Hospital Gatien de Clocheville, University Hospital Centre of Tours, Tours, France .,University François Rabelais, Tours, France.,INSERM UMR 1069 - Nutrition, Croissance et Cancer, Tours, France
| | - Elodie Cheyssac
- Children Hospital Gatien de Clocheville, University Hospital Centre of Tours, Tours, France
| | - Nathalie Soulé
- Children Hospital Gatien de Clocheville, University Hospital Centre of Tours, Tours, France
| | - Jacques Poinsot
- Children Hospital Gatien de Clocheville, University Hospital Centre of Tours, Tours, France
| | | | | | - Alain Chantepie
- Children Hospital Gatien de Clocheville, University Hospital Centre of Tours, Tours, France.,University François Rabelais, Tours, France
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23
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Hoeting NM, McCracken CE, McConnell M, Sallee D, Iannucci GJ, Oster ME. Systolic ejection click versus split first heart sound: Are our ears deceiving us? CONGENIT HEART DIS 2017; 12:417-420. [DOI: 10.1111/chd.12460] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 02/10/2017] [Accepted: 02/21/2017] [Indexed: 12/01/2022]
Affiliation(s)
| | | | - Michael McConnell
- Emory University School of Medicine; Atlanta Georgia USA
- Children's Healthcare of Atlanta; Atlanta Georgia USA
| | - Denver Sallee
- Emory University School of Medicine; Atlanta Georgia USA
- Children's Healthcare of Atlanta; Atlanta Georgia USA
| | - Glen J. Iannucci
- Emory University School of Medicine; Atlanta Georgia USA
- Children's Healthcare of Atlanta; Atlanta Georgia USA
| | - Matthew E. Oster
- Emory University School of Medicine; Atlanta Georgia USA
- Children's Healthcare of Atlanta; Atlanta Georgia USA
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24
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Thompson WR. In defence of auscultation: a glorious future? HEART ASIA 2017; 9:44-47. [PMID: 28243316 DOI: 10.1136/heartasia-2016-010796] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 01/09/2017] [Accepted: 01/10/2017] [Indexed: 11/03/2022]
Abstract
Auscultation of the heart using a simple stethoscope continues to be a central aspect of the cardiovascular examination despite declining proficiency and availability of competing technologies such as hand-held ultrasound. In the ears and mind of a trained cardiologist, heart sounds can provide important information to help screen for certain diseases such as valvar lesions and many congenital defects. Using emerging technology, auscultation is poised to undergo a transformation that will simultaneously improve the teaching and evaluation of this important clinical skill and create a new generation of smart stethoscopes, capable of assisting the clinician in quickly and confidently screening for heart disease. These developments have important implications for global health, screening of athletes and recognition of congenital heart disease.
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Harbison J. 'The old guessing tube': 200 years of the stethoscope. QJM 2017; 110:9-10. [PMID: 27486261 DOI: 10.1093/qjmed/hcw108] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 07/19/2016] [Indexed: 11/14/2022] Open
Affiliation(s)
- J Harbison
- From the Department of Medical Gerontology, University of Dublin, Trinity College, Mercer's Institute for Successful Ageing, St James's Hospital, Dublin D08 RT2X, Ireland
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