The Usefulness of Artificial Intelligence in Interpreting Electrocardiograms (ECGs) in Patients with Acute Coronary Syndrome.
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 240
- 试验地点
- 1
- 主要终点
- 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
研究概览
简要总结
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Other
- 盲法
- Participant, Investigator, Outcome Assessor and Date-entry Operator Blinded
入排标准
- 年龄范围
- 18.00 Year(s) 至 80.00 Year(s)（—）
- 性别
- All
入选标准
- •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).
排除标准
- •Patients not willing to participate
- •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.
结局指标
主要结局
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
时间窗: 6 months
次要结局
- 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.
研究者
Thirumurugan E
Srinivas university, Manglore, Karnataka.