Testing the Performance of Smartphones and Their Accessories in Detecting Irregularly Irregular Heart Rhythm

NCT07154303 | Enrolling By Invitation

• 2 other identifiers: AUSC/ECG/rPPG-AFUW 24-763
• Study Type: observational
• Target: 209
• Locations: 1 country
• Sites: 1

Brief Summary

The purpose of this 4-in-1 observational study is to test the performance of artificial intelligences (AIs) in distinguishing irregularly irregular heart rhythm called atrial fibrillation (AF) from normal heart rhythm using physiological signals collected by smartphones' built-in hardware and/or external accessories. Participants will:

• Have their weight, height, resting heart rate and blood pressures measured
• Have 12-lead electrocardiogram (ECG) of their heart electrical activities recorded
• Have their heart sounds and 1-lead ECG recorded from their chest, and optical-based blood flow data (photoplethysmography or PPG) and 1-lead ECG recorded from their fingers using smartphones' built-in microphone, camera, and/or external accessories
• Optionally have their optical-based blood flow data recorded from their face using smartphones' built-in camera (remote PPG or rPPG). The researchers will also create a database containing the physiological signals collected in this study along with the participants' medically relevant information to help train and test future AIs for medical applications.

Conditions

Atrial Fibrillation (AF)

Keywords

Smartphone; Persistent Atrial Fibrillation; Mobile Health; mHealth; Telemedicine; Artificial Intelligence; Heart Sounds; Phonocardiography; Photoplethysmography; Electrocardiography; 12-Lead ECG; 12-Lead EKG; 12-Lead Electrocardiography; ECG; EKG; Electrocardiogram; Electrocardiograph

Outcome Measures

Primary Outcomes (1)

• Differentiation of Atrial Fibrillation from Sinus Rhythm in Heart Sound Recordings

  AUSC-AF: Identification of atrial fibrillation (AF) from sinus rhythm in recorded heart sounds (phonocardiogram \[PCG\]) as verified by the gold standard 12-lead electrocardiography (ECG) interpretation, measured in the form of sensitivity and specificity.

  Day 0

Secondary Outcomes (4)

• Differentiation of Atrial Fibrillation from Sinus Rhythm in Heart Sound Recordings

  Day 0

• Differentiation of Atrial Fibrillation from Sinus Rhythm in 1-Lead ECG Signals

  Day 0

• Differentiation of Atrial Fibrillation from Sinus Rhythm in PCG and 1-Lead ECG Signals

  Day 0

• Differentiation of Atrial Fibrillation from Sinus Rhythm in Facial Photoplethysmography Signals

  Day 0

Study Arms (2)

Atrial Fibrillation

Diagnostic Test: Computer algorithms

Sinus Rhythm

Diagnostic Test: Computer algorithms

Interventions

Computer algorithms DIAGNOSTIC_TEST

Computer algorithms that are designed to perform heart sound, electrocardiography (ECG), and/or facial photoplethysmography (rPPG) analysis on data collected from smartphone's internal hardware and/or external accessories.

Also known as: ausculto®, Vitogram®, FacialAI

Atrial Fibrillation; Sinus Rhythm

Eligibility Criteria

• Age: 22 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Hospital cardiology patients

You may qualify if:

• Age: ≥22 years (adult)
• Patients who have one of the following:
• Permanent atrial fibrillation, or
• Long-standing persistent atrial fibrillation (12 months or longer), or
• Confirmed 12-lead ECG diagnosis for persistent atrial fibrillation (\> 7 days) or sinus rhythm within 12 months at the time of their normal attendance at the hospital

You may not qualify if:

• Any of the following:
• Implanted active medical devices in the torso, such as pacemakers and defibrillators
• Patients without atrial fibrillation who have another arrhythmia
• Completely missing one or more limbs, or missing any hand
• Disability in using their hands or arms
• Lack of both index fingers, or all fingers in any hand
• Both index fingers with any of the following characteristics:
• Tattooed/inked
• Reduced blood flow in the fingertip (e.g. perniosis or callus formation)

Sponsors & Collaborators

• The University of Hong Kong: lead
• Laboratory of Data Discovery for Health: collaborator

Study Sites (1)

Queen Mary Hospital

Hong Kong, China

Location

MeSH Terms

Conditions

Atrial Fibrillation

Condition Hierarchy (Ancestors)

Arrhythmias, Cardiac; Heart Diseases; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Assistant Professor

Study Record Dates

• First Submitted: August 26, 2025
• First Posted: September 4, 2025
• Study Start: August 27, 2025
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): October 1, 2026
• Last Updated: December 31, 2025

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)