Evaluation of an Automated Smartphone-based Digital Auscultation Application for Detecting Abnormal Heart Sounds Using Deep Learning Techniques (AVDA)

Evaluation of an Automated Smartphone-based Digital Auscultation Application for Detecting Abnormal Heart Sounds Using Deep Learning Techniques - the Automated Valvular Heart Disease Assessment (AVDA) Pilot Study

This pilot study is to investigate the feasibility of obtaining medical grade audio phonocardiogram (PCG) recordings using a smartphone-based auscultation device in the first step. The ability to determine Valvular Heart Disease (VHD) (i.e., presence or absence of cardiac murmurs) using novel handheld CAA-devices shall be analyzed and first data on a smartphone-based auscultation in a hospital setting shall be collected. In further studies, the data provided from this study can be used to investigate the potential diagnostic use of such devices in the ambulatory and stationary care scenarios.

Study Overview

• Status: Completed
• Conditions: Valvular Heart Disease

Detailed Description

Cardiac auscultation is considered to be highly subjective with substantial varying sensitivities and specificities in regard of the practitioners' expertise. Computer-assisted auscultation (CAA) aims to provide increased objectivity. CAA makes auscultation procedure less operator-dependent, approximate inter-examiner differences and may reduce uncertainties in the course of the examination. With the introduction of modern Machine Learning software libraries and ever-growing computational resources CAA has advanced significantly and is now able to classify heart sounds and murmurs into normal and abnormal, using complex spectro-temporal signal processing techniques and neural network pathways. CAA has simultaneously made the shift from the deployment on computers to consumer smartphones. A benefit of CAA can be expected from the smartphone alone in terms of cost, application range, the clinical validity of such algorithms should now be measured in this pilot study. This pilot study is to investigate the feasibility of obtaining medical grade audio phonocardiogram (PCG) recordings using a smartphone-based auscultation device in the first step. In further studies, the data provided from this study can be used to investigate the potential diagnostic use of such devices in the ambulatory and stationary care scenarios.

• Study Type: Observational
• Enrollment (Actual): 102

Contacts and Locations

Study Locations

• Switzerland
  • Basel, Switzerland, 4031
    • University Hospital Basel, Division of Internal Medicine

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

Patients will be recruited at the Department of Cardiology, University Hospital Basel. The scheduled appointment lists of patients for an echocardiography will be checked and eligible patients will be contacted by the study physician before presenting for their planned echocardiography appointment.

Description

Inclusion Criteria:

• Older or equal than 18 years of age
• Referred for an echocardiogram
• Able to provide informed consent

Exclusion Criteria:

• Confirmed arrythmia
• Prior valvular intervention
• Evidence of congenital heart disease

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Intervention group; 30 second audio phonocardiogram (PCG) recordings at each of the five standard cardiac auscultatory positions using an Apple iPhone®.; the assessment of VHD by echocardiogram, as reported by the echocardiography laboratory.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Determination of Valvular Heart Disease (VHD) (i.e., presence or absence of cardiac murmurs)	Ability to determine VHD (i.e., presence or absence of cardiac murmurs) using novel handheld CAA-devices is investigated by collection of data on a smartphone-based auscultation in a hospital setting.	one time assessment at baseline (approx. 5 minutes)

Collaborators and Investigators

• Sponsor: University Hospital, Basel, Switzerland
• Investigators: Principal Investigator: Jens Eckstein, Prof. Dr. med., University Hospital Basel, Division of Internal Medicine

Study record dates

Study Major Dates

• Study Start (Actual): August 30, 2022
• Primary Completion (Actual): July 31, 2023
• Study Completion (Actual): July 31, 2023

Study Registration Dates

• First Submitted: April 5, 2022
• First Submitted That Met QC Criteria: April 5, 2022
• First Posted (Actual): April 13, 2022

Study Record Updates

• Last Update Posted (Actual): September 14, 2023
• Last Update Submitted That Met QC Criteria: September 13, 2023
• Last Verified: September 1, 2023

More Information

Terms related to this study

• Keywords: automated digital auscultation; deep learning technique; smartphone-based digital auscultation; Computer-assisted auscultation (CAA); artificial neural networks (ANN); heart sound (HS) identification; medical grade audio phonocardiogram (PCG)
• Additional Relevant MeSH Terms: Cardiovascular Diseases; Heart Diseases; Heart Valve Diseases
• Other Study ID Numbers: 2021-02515; am21Eckstein

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No