TY - JOUR
T1 - Artificial Intelligence in the Management of Patients with Respiratory Failure Requiring Mechanical Ventilation: A Scoping Review
AU - Viderman, Dmitriy
AU - Ayazbay, Ainur
AU - Kalzhan, Bakhtiyar
AU - Bayakhmetova, Symbat
AU - Tungushpayev, Meiram
AU - Abdildin, Yerkin
PY - 2024/12/1
Y1 - 2024/12/1
N2 - Background: Mechanical ventilation (MV) is one of the most frequently used organ replacement modalities in the intensive care unit (ICU). Artificial intelligence (AI) presents substantial potential in optimizing mechanical ventilation management. The utility of AI in MV lies in its ability to harness extensive data from electronic monitoring systems, facilitating personalized care tailored to individual patient needs. This scoping review aimed to consolidate and evaluate the existing evidence for the application of AI in managing respiratory failure among patients necessitating MV. Methods: The literature search was conducted in PubMed, Scopus, and the Cochrane Library. Studies investigating the utilization of AI in patients undergoing MV, including observational and randomized controlled trials, were selected. Results: Overall, 152 articles were screened, and 37 were included in the analysis. We categorized the goals of AI in the included studies into the following groups: (1) prediction of requirement in MV; (2) prediction of outcomes in MV; (3) prediction of weaning from MV; (4) prediction of hypoxemia after extubation; (5) prediction models for MV–associated severe acute kidney injury; (6) identification of long-term outcomes after prolonged MV; (7) prediction of survival. Conclusions: AI has been studied in a wide variety of patients with respiratory failure requiring MV. Common applications of AI in MV included the assessment of the performance of ML for mortality prediction in patients with respiratory failure, prediction and identification of the most appropriate time for extubation, detection of patient-ventilator asynchrony, ineffective expiration, and the prediction of the severity of the respiratory failure.
AB - Background: Mechanical ventilation (MV) is one of the most frequently used organ replacement modalities in the intensive care unit (ICU). Artificial intelligence (AI) presents substantial potential in optimizing mechanical ventilation management. The utility of AI in MV lies in its ability to harness extensive data from electronic monitoring systems, facilitating personalized care tailored to individual patient needs. This scoping review aimed to consolidate and evaluate the existing evidence for the application of AI in managing respiratory failure among patients necessitating MV. Methods: The literature search was conducted in PubMed, Scopus, and the Cochrane Library. Studies investigating the utilization of AI in patients undergoing MV, including observational and randomized controlled trials, were selected. Results: Overall, 152 articles were screened, and 37 were included in the analysis. We categorized the goals of AI in the included studies into the following groups: (1) prediction of requirement in MV; (2) prediction of outcomes in MV; (3) prediction of weaning from MV; (4) prediction of hypoxemia after extubation; (5) prediction models for MV–associated severe acute kidney injury; (6) identification of long-term outcomes after prolonged MV; (7) prediction of survival. Conclusions: AI has been studied in a wide variety of patients with respiratory failure requiring MV. Common applications of AI in MV included the assessment of the performance of ML for mortality prediction in patients with respiratory failure, prediction and identification of the most appropriate time for extubation, detection of patient-ventilator asynchrony, ineffective expiration, and the prediction of the severity of the respiratory failure.
KW - classification
KW - intubation
KW - machine and deep learning methods
KW - medical outcomes
KW - prediction
KW - respiratory failure
UR - http://www.scopus.com/inward/record.url?scp=85213241877&partnerID=8YFLogxK
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U2 - 10.3390/jcm13247535
DO - 10.3390/jcm13247535
M3 - Review article
AN - SCOPUS:85213241877
SN - 2077-0383
VL - 13
JO - Journal of Clinical Medicine
JF - Journal of Clinical Medicine
IS - 24
ER -