Towards prehospital risk stratification using deep learning for ECG interpretation in suspected acute coronary syndrome
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Chest pain is one of the most common reasons for consulting emergency medical services (EMS) and an ECG should be obtained at first medical contact. Based on the initial prehospital ECG, patients can be differentiated into two working diagnoses: chest pain with ST segment elevation (STEMI) or chest pain without persistent ST segment elevation (suspected non-ST-elevation acute coronary syndrome (NSTE-ACS)).
A new AI framework can detect neurological disorders by analyzing speech with over 90% accuracy. The model, called CTCAIT, captures subtle patterns in voice that may indicate early symptoms of diseases like Parkinson’s, Huntington’s, and Wilson disease.
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