| CTRI Number |
CTRI/2025/03/082773 [Registered on: 19/03/2025] Trial Registered Prospectively |
| Last Modified On: |
19/03/2025 |
| Post Graduate Thesis |
Yes |
| Type of Trial |
Interventional |
|
Type of Study
|
Medical Device
Diagnostic |
| Study Design |
Randomized Factorial Trial |
|
Public Title of Study
|
Electrocardiogram findings identification using artificial intelligence method |
|
Scientific Title of Study
|
The Usefulness of Artificial Intelligence in Interpreting Electrocardiograms (ECGs) in Patients with Acute Coronary Syndrome. |
| Trial Acronym |
NIL |
|
Secondary IDs if Any
|
| Secondary ID |
Identifier |
| NIL |
NIL |
|
|
Details of Principal Investigator or overall Trial Coordinator (multi-center study)
|
| Name |
Thirumurugan E |
| Designation |
Research scholar |
| Affiliation |
Srinivas university, Manglore, Karnataka. |
| Address |
Room no. 1, Cardiac catheterization laboratory, Srinivas University, Mukka, Manglore, Karanataka, India.
Dakshina Kannada
KARNATAKA
574146
India |
| Phone |
7358581003 |
| Fax |
|
| Email |
thiruahs1002@gmail.com |
|
Details of Contact Person
Scientific Query
|
| Name |
Dr. Nirmala rajesh |
| Designation |
Associate Professor |
| Affiliation |
Srinivas university |
| Address |
Room no. 1
Cardiac catheterization Laboratory
Srinivas University, Manglore, Karnataka
Dakshina Kannada
KARNATAKA
574146
India |
| Phone |
9900096319 |
| Fax |
|
| Email |
nimmiraj.naidu@gmail.com |
|
Details of Contact Person
Public Query
|
| Name |
Dr. Nirmala rajesh |
| Designation |
Associate Professor |
| Affiliation |
Srinivas university |
| Address |
Room no. 1
Cardiac catheterization Laboratory
Srinivas University, Manglore, Karnataka
Dakshina Kannada
KARNATAKA
574146
India |
| Phone |
9900096319 |
| Fax |
|
| Email |
nimmiraj.naidu@gmail.com |
|
|
Source of Monetary or Material Support
|
| Srinivas university
Room no. 1
Cardiac catheterization Laboratory
Srinivas University, Manglore, Karnataka, Pincode-574146 |
|
|
Primary Sponsor
|
| Name |
SRINIVAS UNIVERSITY |
| Address |
Room no. 1
Cardiac catheterization Laboratory
Srinivas University, Manglore, Karnataka, Pincode-574146 |
| Type of Sponsor |
Private medical college |
|
|
Details of Secondary Sponsor
|
|
|
Countries of Recruitment
|
India |
|
Sites of Study
|
| No of Sites = 1 |
| Name of Principal
Investigator |
Name of Site |
Site Address |
Phone/Fax/Email |
| Dr Nirmala rajesh |
Srinivas university |
Room no. 01, Electrocardiogram Lab, Suratkal, Manglore, Karanatakka, India
Dakshina Kannada
KARNATAKA |
9900096319
nimmiraj.naidu@gmail.com |
|
|
Details of Ethics Committee
|
| No of Ethics Committees= 1 |
| Name of Committee |
Approval Status |
| INSTITUTIONAL ETHICAL COMMITTEE, SRINIVAS UNIVERSITY |
Approved |
|
|
Regulatory Clearance Status from DCGI
|
|
|
Health Condition / Problems Studied
|
| Health Type |
Condition |
| Patients |
(1) ICD-10 Condition: X||New Technology, |
|
|
Intervention / Comparator Agent
|
| Type |
Name |
Details |
| Intervention |
Electrocardiogram analysis using artifical intelligence |
Routine electrocardiograms were analyzed with the help of artificial intelligence and compared with manual interpretation.
Total duration-6 months
Frequency- daily |
| Comparator Agent |
Electrocardiogram analysis using medical professionals |
Routine electrocardiograms were analyzed with the help of medical professionals and compared with artificial intelligence interpretation.
Total duration-6 months
Frequency-daily |
|
|
Inclusion Criteria
|
| Age From |
18.00 Year(s) |
| Age To |
80.00 Year(s) |
| Gender |
Both |
| Details |
The study population will consist of adult patients aged over 18 years to 80 years who present to the emergency department (ED) with chest discomfort or those who are clinically suspected of experiencing an Acute Coronary Syndrome (ACS). |
|
| ExclusionCriteria |
| Details |
1. Patients not willing to participate
2. Patients with traumatic chest pain or other conditions distinct from myocardial infarction (such as pneumothorax), as well as those transferred from another hospital with confirmed acute myocardial infarction, will be excluded. |
|
|
Method of Generating Random Sequence
|
Other |
|
Method of Concealment
|
Case Record Numbers |
|
Blinding/Masking
|
Participant, Investigator, Outcome Assessor and Date-entry Operator Blinded |
|
Primary Outcome
|
| Outcome |
TimePoints |
| 1. The development of an AI-based ECG analysis model has the potential to significantly improve the quality and speed of ECG analysis, which could lead to more effective and timely diagnosis and treatment of cardiac conditions |
6 months |
|
|
Secondary Outcome
|
| Outcome |
TimePoints |
1. The rising occurrence of myocardial infarction (MI) necessitates innovative diagnostic methods to improve patient outcomes. While traditional diagnostic approaches are practical, they often suffer from diagnosis delays, clinician experience variability, and resource limitations.
2. The development of an AI-based ECG analysis model has the potential to significantly improve the quality and speed of ECG analysis, which could lead to more effective and timely diagnosis and treatment of cardiac conditions |
6 months |
|
|
Target Sample Size
|
Total Sample Size="240"
Sample Size from India="240"
Final Enrollment numbers achieved (Total)= "Applicable only for Completed/Terminated trials"
Final Enrollment numbers achieved (India)="Applicable only for Completed/Terminated trials" |
|
Phase of Trial
|
N/A |
|
Date of First Enrollment (India)
|
31/03/2025 |
| Date of Study Completion (India) |
Applicable only for Completed/Terminated trials |
| Date of First Enrollment (Global) |
31/03/2025 |
| Date of Study Completion (Global) |
Applicable only for Completed/Terminated trials |
|
Estimated Duration of Trial
|
Years="0"
Months="6"
Days="0" |
|
Recruitment Status of Trial (Global)
|
Not Yet Recruiting |
| Recruitment Status of Trial (India) |
Not Yet Recruiting |
|
Publication Details
|
N/A |
|
Individual Participant Data (IPD) Sharing Statement
|
Will individual participant data (IPD) be shared publicly (including data dictionaries)?
Response - NO
|
|
Brief Summary
|
Cardiovascular diseases (CVDs) continue to account for a significant number of fatalities globally, underlining the pressing need for early detection and intervention. Electrocardiography (ECG) represents a fundamental tool in cardiac diagnostics, providing valuable information concerning the heart’s electrical activity. However, interpreting ECG signals can often be complex and time-consuming, requiring specialized expertise. Recent advancements in artificial intelligence (AI) and machine learning offer an avenue to automate and enhance ECG analysis, thus potentially improving diagnostic precision and overall patient outcomes. The primary objective of the present study is to develop an electrocardiogram (ECG) analysis model that is powered by artificial intelligence (AI). OBJECTIVES: ü To investigate the feasibility of using AI to analyze ECG signals and to determine the potential benefits of this technology in terms of enhancing diagnostic accuracy. Justification for study: The rising occurrence of myocardial infarction (MI) necessitates innovative diagnostic methods to improve patient outcomes. While traditional diagnostic approaches are practical, they often suffer from diagnosis delays, clinician experience variability, and resource limitations. |