Performance and Accuracy of an AI Enhanced Smart Watch Single Lead ECG

Healthy Clinical Trial

— PROCESS  

Official title:

PeRfOrmance and ACcuracy of an artifiCial Intelligence Enhanced Smart Watch Single Lead ECG (PROCESS)

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05450809
• Other study ID #: 21-006770
• Status: Completed
• Start date: November 5, 2021
• Est. completion date: November 2, 2022

Study information

• Verified date: December 2022
• Source: Mayo Clinic
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

The purpose of this study is to show the artificial intelligence enhanced single-lead ECG Apple Watch has similar, robust performance comparable to an AI enhanced 12 lead ECG and AI enhanced single lead (LI) of a 12 lead ECG.

Description:

1. Ambulatory patients undergoing ECG recording in the Mayo Clinic outpatient ECG lab will be asked to consent for this study.

2. Those who consent for the study will be asked to record a ECG using a single-lead watch-based (Apple Watch series 5) recording at a visit for a clinically scheduled 12 lead ECG recording.

3. This watch-based ECG data will be recorded and analyzed in comparison to the near-simultaneously recorded outpatient 12 Lead ECG

Recruitment information / eligibility

• Status: Completed
• Enrollment: 1000
• Est. completion date: November 2, 2022
• Est. primary completion date: November 2, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 89 Years

Eligibility

Inclusion Criteria:

• Age = 18 years and = 89.

• Able to give verbal consent.

• Able to complete routine clinical 12 lead ECG tracing and single lead Apple Watch ECG tracing.

Exclusion Criteria:

• Individuals < 18 and > 89 years of age.

• Unable to given verbal consent.

Related Conditions & MeSH terms

• Healthy

Locations

Country	Name	City	State
United States	Mayo Clinic	Rochester	Minnesota

Sponsors (1)

Lead Sponsor	Collaborator
Mayo Clinic

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Artificial intelligence detection of heart failure by single-lead watch-based ECG	A previously developed AI algorithm to predict potential underlying cardiac pathology assess from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo single-lead watch-based ECG recording. This neural network uses PQRST complexes to yield a probability of heart failure which may not be readily apparent via manual review. Each recorded single-lead watch-based ECG will undergo evaluation by this neural network and will produce a probability of heart failure (0-100%) for each individual patient. This probability will be compared to the AI ECG result (probability 0-100%) from the patient's recently recorded 12 lead ECG which is routinely available for all patients with a recorded 12 lead ECG at our medical system.	12 months
Other	Artificial intelligence detection of silent/paroxysmal atrial fibrillation by single-lead watch-based ECG	A previously developed AI algorithm to predict potential underlying cardiac pathology assess from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo single-lead watch-based ECG recording. This neural network uses PQRST complexes to yield a probability of silent/paroxysmal atrial fibrillation which may not be readily apparent via manual review. Each recorded single-lead watch-based ECG will undergo evaluation by this neural network and will produce a probability of silent/paroxysmal atrial fibrillation (0-100%) for each individual patient. This probability will be compared to the AI ECG result (probability 0-100%) from the patient's recently recorded 12 lead ECG which is routinely available for all patients with a recorded 12 lead ECG at our medical system.	12 months
Other	Artificial intelligence detection of aortic stenosis by single-lead watch-based ECG	A previously developed AI algorithm to predict potential underlying cardiac pathology assess from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo single-lead watch-based ECG recording. This neural network uses PQRST complexes to yield a probability of aortic stenosis which may not be readily apparent via manual review. Each recorded single-lead watch-based ECG will undergo evaluation by this neural network and will produce a probability of aortic stenosis (0-100%) for each individual patient. This probability will be compared to the AI ECG result (probability 0-100%) from the patient's recently recorded 12 lead ECG which is routinely available for all patients with a recorded 12 lead ECG at our medical system.	12 months
Other	Artificial intelligence determination of patient age by single-lead watch-based ECG	A previously developed AI algorithm to predict patient age from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo single-lead watch-based ECG recording. This neural network uses PQRST complexes to yield an ECG-predicted age. Each recorded single-lead watch-based ECG will undergo evaluation by this neural network and determine "ECG age" for each individual patient. This single-lead "ECG age" will be compared to the AI ECG "age" result determined from the patient's recently recorded 12 lead ECG which is routinely available for all patients with a recorded 12 lead ECG at our medical system.	12 months
Other	Artificial intelligence detection of amyloidosis by single-lead watch-based ECG	A previously developed AI algorithm to predict potential underlying cardiac pathology assess from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo single-lead watch-based ECG recording. This neural network uses PQRST complexes to yield a probability of amyloidosis which may not be readily apparent via manual review. Each recorded single-lead watch-based ECG will undergo evaluation by this neural network and will produce a probability of amyloidosis (0-100%) for each individual patient. This probability will be compared to the AI ECG result (probability 0-100%) from the patient's recently recorded 12 lead ECG which is routinely available for all patients with a recorded 12 lead ECG at our medical system.	12 months
Primary	Comparison of 12 lead ECG features to single-lead watch-based ECG features	The ECG interval differences (in milliseconds) between 12 Lead and collected single-lead watch-based ECG for PR, QRS, QT intervals will be determined and compared for each patient.	12 months
Primary	Arrhythmia comparison of 12 lead ECG to single-lead watch-based ECG	A physician interpretation of patients' 12 lead ECG and single-lead watch-based ECG will be performed to determined underlying rhythm (i.e. sinus rhythm, atrial fibrillation etc) from each, and the results from these modalities will be compared.	12 months
Secondary	Arrhythmia classification by physician overread of single-lead watch-based ECG	A physician interpretation of the patient's single-lead watch-based ECG will occur as described in "Outcome 2." The results of this ECG interpretation (i.e. sinus rhythm, atrial fibrillation, or inconclusive) will be compared to the watch/app-based rhythm auto-classification for each recorded single-lead watch-based ECG.	12 months

External Links

•   Mayo Clinic Clinical Trials