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Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices.

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Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.

Purpose: Intraoperative hypotension is associated with adverse outcomes. Predicting and proactively managing hypotension can reduce its incidence. Previously, hypotension prediction algorithms using artificial intelligence were developed for invasive arterial blood pressure monitors.

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Chronic Obstructive Pulmonary Disease (COPD), as the third leading cause of death worldwide, is a major global health issue. The early detection and grading of COPD are pivotal for effective treatment. Traditional spirometry tests, requiring considerable physical effort and strict adherence to quality standards, pose challenges in COPD diagnosis.

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Regional Ventilation-Perfusion Matching by Electrical Impedance Tomography After Single Lung Transplant.

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