Abstract
Background: Mechanical ventilation in prematurely born infants, particularly if prolonged, can cause long term complications including bronchopulmonary dysplasia. Timely extubation then is essential, yet predicting its success remains challenging. Artificial intelligence (AI) may provide a potential solution.
Content: A narrative review was undertaken to explore AI's role in predicting extubation success in prematurely born infants. Across the eleven studies analysed, the range of reported area under the receiver operator characteristic curve (AUC) for the selected prediction models was between 0.7 and 0.87. Only two studies implemented an external validation procedure. Comparison to the results of clincial predictors was made in two studies. One group reported a logistic regression model that outperformed clinical predictors on decision tree analysis, while another group reported clinical predictors outperformed their artifical neural network model (AUCs: ANN 0.68 versus clinical predictors 0.86). Amongst the studies there was an heterogenous selection of variables for inclusion in prediction models, as well as variations in definitions of extubation failure.
Summary: Although there is potential for AI to enhance extubation success, no model’s performance has yet surpassed that of clinical predictors.
Outlook: Future studies should incorporate external validation to increase the applicability of the models to clinical settings.
Content: A narrative review was undertaken to explore AI's role in predicting extubation success in prematurely born infants. Across the eleven studies analysed, the range of reported area under the receiver operator characteristic curve (AUC) for the selected prediction models was between 0.7 and 0.87. Only two studies implemented an external validation procedure. Comparison to the results of clincial predictors was made in two studies. One group reported a logistic regression model that outperformed clinical predictors on decision tree analysis, while another group reported clinical predictors outperformed their artifical neural network model (AUCs: ANN 0.68 versus clinical predictors 0.86). Amongst the studies there was an heterogenous selection of variables for inclusion in prediction models, as well as variations in definitions of extubation failure.
Summary: Although there is potential for AI to enhance extubation success, no model’s performance has yet surpassed that of clinical predictors.
Outlook: Future studies should incorporate external validation to increase the applicability of the models to clinical settings.
| Original language | English |
|---|---|
| Journal | Journal of Perinatal Medicine |
| Volume | 52 |
| Publication status | Published - 2023 |