The American journal of emergency medicine
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Review
Artificial neural networks for ECG interpretation in acute coronary syndrome: A scoping review.
The electrocardiogram (ECG) is a crucial diagnostic tool in the Emergency Department (ED) for assessing patients with Acute Coronary Syndrome (ACS). Despite its widespread use, the ECG has limitations, including low sensitivity of the STEMI criteria to detect Acute Coronary Occlusion (ACO) and poor inter-rater reliability. Emerging ECG features beyond the traditional STEMI criteria show promise in improving early ACO diagnosis, but complexity hinders widespread adoption. The potential integration of Artificial Neural Networks (ANN) holds promise for enhancing diagnostic accuracy and addressing reliability issues in ECG interpretation for ACO symptoms. ⋯ The interpretation of ECGs in patients with suspected ACS using ANN appears to be accurate and potentially superior when compared to human interpreters and computerised algorithms. This appears consistent across various ANN models and outcome variables. Future investigations should emphasise ANN interpretation of ECGs in patients with ACO, where rapid and accurate diagnosis can significantly benefit patients through timely access to reperfusion therapies.
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Review
Artificial neural networks for ECG interpretation in acute coronary syndrome: A scoping review.
The electrocardiogram (ECG) is a crucial diagnostic tool in the Emergency Department (ED) for assessing patients with Acute Coronary Syndrome (ACS). Despite its widespread use, the ECG has limitations, including low sensitivity of the STEMI criteria to detect Acute Coronary Occlusion (ACO) and poor inter-rater reliability. Emerging ECG features beyond the traditional STEMI criteria show promise in improving early ACO diagnosis, but complexity hinders widespread adoption. The potential integration of Artificial Neural Networks (ANN) holds promise for enhancing diagnostic accuracy and addressing reliability issues in ECG interpretation for ACO symptoms. ⋯ The interpretation of ECGs in patients with suspected ACS using ANN appears to be accurate and potentially superior when compared to human interpreters and computerised algorithms. This appears consistent across various ANN models and outcome variables. Future investigations should emphasise ANN interpretation of ECGs in patients with ACO, where rapid and accurate diagnosis can significantly benefit patients through timely access to reperfusion therapies.
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Review Meta Analysis
Diagnostic accuracy of 3-item stroke scale for detection of cerebral large vessel occlusion: A systematic review and meta-analysis.
Prompt identification of large vessel occlusion (LVO) in acute ischemic stroke (AIS) is crucial for expedited endovascular therapy (EVT) and improved patient outcomes. Prehospital stroke scales, such as the 3-Item Stroke Scale (3I-SS), could be beneficial in detecting LVO in suspected patients. This meta-analysis evaluates the diagnostic accuracy of 3I-SS for LVO detection in AIS. ⋯ 3I-SS demonstrates good diagnostic accuracy in identifying LVO stroke and may be valuable in the prompt identification of patients for direct transfer to comprehensive stroke centers.
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Review Meta Analysis
Diagnostic accuracy of 3-item stroke scale for detection of cerebral large vessel occlusion: A systematic review and meta-analysis.
Prompt identification of large vessel occlusion (LVO) in acute ischemic stroke (AIS) is crucial for expedited endovascular therapy (EVT) and improved patient outcomes. Prehospital stroke scales, such as the 3-Item Stroke Scale (3I-SS), could be beneficial in detecting LVO in suspected patients. This meta-analysis evaluates the diagnostic accuracy of 3I-SS for LVO detection in AIS. ⋯ 3I-SS demonstrates good diagnostic accuracy in identifying LVO stroke and may be valuable in the prompt identification of patients for direct transfer to comprehensive stroke centers.
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Cavernous sinus thrombosis (CST) is a serious condition that carries with it a high rate of morbidity and mortality. ⋯ An understanding of CST can assist emergency clinicians in diagnosing and managing this potentially deadly disease.