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| http://dx.doi.org/10.1007/s00408-018-0148-0 | DOI Listing |
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AI Model Based on Diaphragm Ultrasound to Improve the Predictive Performance of Invasive Mechanical Ventilation Weaning: Prospective Cohort Study.
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Department of Critical Care Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong Provincial Geriatrics Institute, No. 106, Zhongshaner Rd, Guangzhou, 510080, China, 86 15920151904.
Background: Point-of-care ultrasonography has become a valuable tool for assessing diaphragmatic function in critically ill patients receiving invasive mechanical ventilation. However, conventional diaphragm ultrasound assessment remains highly operator-dependent and subjective. Previous research introduced automatic measurement of diaphragmatic excursion and velocity using 2D speckle-tracking technology.
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September 2025
Perinatal Institute, Division of Neonatology and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
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Impact of individualized nursing interventions on ventilator weaning and respiratory outcomes in ICU patients with severe pneumonia: A retrospective cohort study.
Medicine (Baltimore)
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Department of Structural Heart Disease Center, Wuhan University Central South Hospital, Wuhan, Hubei, China.
Severe pneumonia frequently necessitates intensive care, with mechanical ventilation serving as a cornerstone of treatment. Despite its life-saving benefits, ventilator weaning poses considerable challenges and is often associated with complications. This study evaluates the efficacy of a tailored nursing approach in enhancing weaning outcomes and accelerating pulmonary recovery.
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Department of Intensive Care Unit, Maternal and Child Health Hospital of Hubei Province, Wuhan, China.
Background: To explore the value of the diaphragm thickness fraction (TF) and diaphragm mobility (DM) measured by ultrasound for predicting ventilator withdrawal success in patients with acute respiratory distress syndrome (ARDS) after cardiac surgery.
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Transformer-based multimodal precision intervention model for enhancing diaphragm function in elderly patients.
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General Surgery Department, The Second Hospital of Jilin University, Changchun, Jilin, China.
Diaphragm dysfunction represents a significant complication in elderly patients undergoing mechanical ventilation, often resulting in extended intensive care stays, unsuccessful weaning attempts, and increased healthcare expenditures. To address the deficiency of precise, real-time decision support in this context, a novel artificial intelligence framework is proposed, integrating imaging, physiological signals, and ventilator parameters. Initially, a hierarchical Transformer encoder is employed to extract modality-specific embeddings, followed by an attention-guided cross-modal fusion module and a temporal network for dynamic trend prediction.
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