Development of deep-learning models for a hybrid simulation of auscultation training on standard patients using an ECG-based virtual pathology stethoscope
Abstract
Cardiac auscultation (CA), the act of listening to the heart’s sound, is a critical skill that provides valuable information for identifying serious heart diseases. Proficiency in cardiac auscultation requires repeated stethoscope practice and experience in identifying abnormal or irregular cardiac rhythms. However, nowadays, most hospital admissions are short and intensely focused, with fewer opportunities for medical trainees to learn and practice bedside examination skills. It is common practice in many institutions to incorporate standardized patients (SPs) into CA training because these actors are able to represent the patient and convey the symptoms. However, SPs are typically healthy individuals, limiting the kinds of abnormalities that students can hear. In this work, we develop a novel real-time simulation-based method for virtual pathology stethoscope (VPS) detection. The VPS system uses augmented reality (AR) to teach medical students how to perform cardiac examinations by listening to abnormal heart sounds in SPs who are otherwise healthy. A digital stethoscope with two electrodes on the chest piece collects electrocardiogram (ECG) signal data sets from SPs at the four primary auscultation sites. Next, different deep-learning methods are evaluated for classifying the location of the stethoscope by taking advantage of subtle differences in the ECG signals. This study would significantly extend the simulation capabilities of SPs by allowing medical students and trainees to perform realistic CA and hear CA in a clinical environment.
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Fourteen studies (5%) examined the use of AI in simulations.
“Fourteen studies (5%) examined the use of AI in simulations [90, 120–132].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [127]
AR helped teach medical students to identify abnormal heart sounds in standardized patients with normal heart sounds by using a digital stethoscope, utilizing AI deep learning to determine the placement of the stethoscope.
“AR helped teach medical students to identify abnormal heart sounds in standardized patients with normal heart sounds by using a digital stethoscope, utilizing AI deep learning to determine the placement of the stethoscope [127].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [127]
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- Authors
- Yhdego, H., Kidane, N., Mckenzie, F., & Audette, M.
- Affiliations
- Old Dominion University (US)
- Corresponding author
- Yhdego, Haben
- Venue
- SIMULATION
- Publisher
- SAGE Publications
- Published
- 2023-03-29
- Volume
- 99
- Issue
- 9
- Pages
- 903-915
- ISSN
- 0037-5497 (ISSN-L)
- 1741-3133
- OpenAlex
- W4361277815
- Cited by
- 6
- References
- 20
- Citations by year
- 2025: 2 · 2024: 4
- FWCI
- 1.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- SIMULATION (publishedVersion)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Health Sciences › Medicine › Physiology › Simulation-Based Education in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- cardiac auscultation, standardized patients, augmented reality, deep learning, medical training, abnormal heart sounds, hybrid simulation, digital stethoscope
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