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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The use of high-flow nasal cannula (HFNC) oxygen therapy is gaining popularity for the treatment of acute respiratory failure (ARF). However, limited evidence exists regarding the effectiveness of HFNC for hypoxemic ARF in patients with blunt chest trauma (BCT). ⋯ In BCT patients with mild-moderate hypoxemic ARF, the usage of HFNC did not lead to higher rate of treatment failure when compared to NIV. HFNC was found to offer better comfort and tolerance than NIV, suggesting it may be a promising new respiratory support therapy for BCT patients with mild-moderate ARF.