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Enrolling by Invitation

NCT Number: NCT07154303

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

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.

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Key information

Conditions

Atrial Fibrillation (AF) Arrhythmias, Cardiac Atrial Fibrillation Cardiovascular Diseases Heart Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms

Age range

22 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Queen Mary Hospital

Hong Kong, China

About this study

4 observational studies have been combined into 1 observational study to share the same pool of participants. These 4 studies are designated as AUSC-AF, ECG-AF, AUSC+ECG-AF, and rPPG-AF corresponding to the signal modality/modalities used for AF detection (see outcome measures) and designated as AUSC/ECG/rPPG-AF when combined as 1 study.

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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

Exclusion criteria

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)

Treatment and study plan

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.

Other names: ausculto®, Vitogram®, FacialAI

Primary outcomes

  1. Differentiation of Atrial Fibrillation from Sinus Rhythm in Heart Sound Recordings

    Time frame: Day 0

    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.

Secondary outcomes

  1. Differentiation of Atrial Fibrillation from Sinus Rhythm in Heart Sound Recordings

    Time frame: Day 0

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

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

    Time frame: Day 0

    ECG-AF: Identification of atrial fibrillation from sinus rhythm in recorded 1-lead ECG signals as verified by the gold standard 12-lead ECG interpretation, measured in the form of sensitivity, specificity, positive and negative predictive values, and accuracy.

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

    Time frame: Day 0

    AUSC+ECG-AF: Identification of atrial fibrillation from sinus rhythm in PCG and 1-lead ECG signals as verified by the gold standard 12-lead ECG interpretation, measured in the form of sensitivity, specificity, positive and negative predictive values, and accuracy.

  4. Differentiation of Atrial Fibrillation from Sinus Rhythm in Facial Photoplethysmography Signals

    Time frame: Day 0

    rPPG-AF: Identification of atrial fibrillation from sinus rhythm in facial photoplethysmography signals (also known as remote photoplethysmography [rPPG]) as verified by the gold standard 12-lead ECG interpretation, measured in the form of sensitivity, specificity, positive and negative predictive values, and accuracy.

Sponsors and collaborators

Lead sponsor

The University of Hong Kong

Other

Collaborators

  • Laboratory of Data Discovery for Health

Registry information

Official study title

Smartphone-Based Use of Phonocardiography, Electrocardiography Accessory, and/or Facial Photoplethysmography to Detect Atrial Fibrillation: Diagnostic Performance Study

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Sep 4, 2025
Registry last updated
Dec 31, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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