Data Construction Project for Artificial Intelligence Learning: Chest Auscultation Sound Data (AI-sound)

The purpose is to establish chest auscultation data and related clinical data for diagnosing heart and lung diseases.

Study Overview

• Status: Completed
• Conditions: Auscultation for Clinical Evaluation
• Intervention / Treatment: Diagnostic test: Chest auscultation

Detailed Description

The incidence of cardiovascular diseases worldwide is steadily increasing. According to the report of the American Heart Association, there were 271 million cardiovascular diseases in 1990, and 523 million cases in 2019, about doubling in 30 years. The number of deaths due to cardiovascular disease is also steadily increasing from 12.1 million in 1990 to 18.6 million in 2019.

Physical examination, which is the most basic skill in patient care, consists of inspection, auscultation, percussion, and palpation. Among them, auscultation is the most widely used test in all areas where a stethoscope is used, and it is a basic examination that is essential from primary medical institutions to tertiary medical institutions for non-invasive initial diagnosis in patients complaining of chest symptoms.

However, if a specialist in the field with a lot of experience does not interpret it carefully, it is difficult to make a decision, and the deviation of the test results is large, so a significant number of patients depend on expensive follow-up tests (ultrasound, CT, MRI, etc.) This leads to a vicious cycle of incurring costs and unnecessary treatment.

Recently, with the development of machine learning techniques, computing technologies, and artificial intelligence (AI) based on a lot of data, various learning technologies are applied as tools for disease diagnosis and prognosis prediction in medicine.

Through machine learning-based chest auscultation sound analysis, there is an expectation that disease diagnosis and prognosis prediction will be able to overcome differences and interpretations by examiners. It can be very helpful in preventing overuse of tests and reducing medical costs.

• Study Type: Observational
• Enrollment (Actual): 6000

Contacts and Locations

Study Locations

• Korea, Republic of
  • Bucheon, Korea, Republic of
    • Soonchunhyang University Bucheon Hospital
  • Seoul, Korea, Republic of
    • Severance Hospital
  • Giheung-gu
    • Yongin, Giheung-gu, Korea, Republic of, 16995
      • Yongin Severance Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 20 years to 90 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Patients who obtained chest auscultation will be enrolled.

Description

Inclusion Criteria:

• Adults who are 20 years and older

Exclusion Criteria:

• Patient refusal
• Uncertain radiographs
• Uncertain tests results

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Severance hospital; Cardiovascular disease patients	Diagnostic test: Chest auscultation; Chest auscultation data
Yongin Severance hospital; Cardiovascular disease patients	Diagnostic test: Chest auscultation; Chest auscultation data
Soon Chun Hyang University Hospital Bucheon; Cardiovascular disease patients	Diagnostic test: Chest auscultation; Chest auscultation data

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Incidence of valvular heart disease	Echocardiography, coronary CTA, coronary angiography and other examinations find direct evidence of coronary artery stenosis, which can confirm the diagnosis	Within one week of echocardiography

Collaborators and Investigators

• Sponsor: Yonsei University
• Investigators: Principal Investigator: Hyuk-Jae Chang, MD, PhD, Severance hospital, Yonsei university college of medicine

Study record dates

Study Major Dates

• Study Start (Actual): May 1, 2022
• Primary Completion (Actual): November 30, 2022
• Study Completion (Actual): December 31, 2022

Study Registration Dates

• First Submitted: March 25, 2022
• First Submitted That Met QC Criteria: April 7, 2022
• First Posted (Actual): April 11, 2022

Study Record Updates

• Last Update Posted (Estimate): January 26, 2023
• Last Update Submitted That Met QC Criteria: January 24, 2023
• Last Verified: January 1, 2023

More Information

Terms related to this study

• Other Study ID Numbers: AI-sound

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No

Drug and device information, study documents

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