Journal of critical care
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Journal of critical care · Aug 2024
Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks.
This study aims to design, validate and assess the accuracy a deep learning model capable of differentiation Chest X-Rays between pneumonia, acute respiratory distress syndrome (ARDS) and normal lungs. ⋯ A CNN-based deep learning model showed clinically significant performance, providing potential for faster ARDS identification. Future research should prospectively evaluate these tools in a clinical setting.
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Journal of critical care · Aug 2024
Development of a machine learning model for prediction of the duration of unassisted spontaneous breathing in patients during prolonged weaning from mechanical ventilation.
Treatment of patients undergoing prolonged weaning from mechanical ventilation includes repeated spontaneous breathing trials (SBTs) without respiratory support, whose duration must be balanced critically to prevent over- and underload of respiratory musculature. This study aimed to develop a machine learning model to predict the duration of unassisted spontaneous breathing. ⋯ The developed machine learning model showed informed results when predicting the spontaneous breathing capacity of a patient in prolonged weaning, however lacking prognostic quality required for direct translation to clinical use.
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Journal of critical care · Aug 2024
Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research.
With increasing computing power, artificial intelligence (AI) and machine learning (ML) have prospered, which facilitate the analysis of large datasets, especially those found in critical care. It is important to define these terminologies, to inform a standardized approach to critical care research. This manuscript hopes to clarify these terms with examples from medical literature. ⋯ ML, a subset of AI, is typically focused on supervised or unsupervised learning tasks in which the output is based on inputs and derived from iterative pattern recognition algorithms, while AI is the overall ability of a machine to "think" or mimic human behavior; and to analyze data free from human influence. Even with successful implementation, advanced AI and ML algorithms have faced challenges in adoption into practice, mainly due to their lack of interpretability, which hinders trust, buy-in, and engagement from clinicians. Consequently, traditional algorithms, such as linear and logistic regression, that may have reduced predictive power but are highly interpretable, continue to be widely used.
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Journal of critical care · Aug 2024
What every intensivist should know about..Patient safety huddles in the ICU.
Patient safety huddles are brief, multidisciplinary conversations that focus on a specific topic or event. Huddles have been shown to improve communication among healthcare providers in a variety of settings, including the intensive care unit (ICU). This paper presents key features of patient safety huddles and describes the ways in which huddle techniques may be particularly relevant to the practice of critical care.