Clinical trial NCT05325723: Evaluation of an Automated Smartphone-based Digital Auscultation Application for Detecting Abnormal Heart Sounds Using Deep Learning Techniques
Official title: 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
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Valvular Heart Disease
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)
Lead sponsor: University Hospital, Basel, Switzerland
Sponsor class: OTHER
First posted: 2022-04-13
Enrollment: 102
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

Brief summary: 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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Determination of Valvular Heart Disease (VHD) (i.e., presence or absence of cardiac murmurs)
timeFrame: one time assessment at baseline (approx. 5 minutes)
Secondary outcomes:
Other outcomes:
Locations:
city: Basel
country: Switzerland
facility: University Hospital Basel, Division of Internal Medicine
Study officials:
affiliation: University Hospital Basel, Division of Internal Medicine
name: Jens Eckstein, Prof. Dr. med.
role: PRINCIPAL_INVESTIGATOR
References: