Health Condition 1: X- New Technology
Conditions
Interventions
Intervention1: ELECTROCARDIOGRAM ANALYSIS USING ARTIFICAL INTELLIGENCE: Routine electrocardiograms Were ANALYZED with the help of artificial intelligence and compared with manual interpretation.
Inter
Sponsors
SRINIVAS UNIVERSITY
Eligibility
Inclusion criteria
Inclusion criteria: 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).
Exclusion criteria
Exclusion criteria: 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.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| 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 conditionsTimepoint: 6 months | — |
Secondary
| Measure | Time frame |
|---|---|
| 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 conditionsTimepoint: 6 months | — |
Countries
India
Contacts
Public ContactDr. Nirmala rajesh
Srinivas university
Outcome results
None listed