Anaesthesia, critical care & pain medicine · 2025
Artificial intelligence in airway management: A systematic review and meta-analysis.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод в очереди
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Abosamak Mohamed F, Zaki Hany A, Shaban Eman E, Shaban Amira, Shaban Ahmed, Hodhod Haitham, Ponappan Benny
Аннотация
BACKGROUND: Airway management is the cornerstone of anesthesia care. Complications of difficult airways are usually fatal to patients. Artificial intelligence (AI) has shown promising results in enhancing clinicians' performance in various settings. We therefore aimed to summarize the current evidence on the use of AI models in the prediction of a difficult airway.
METHODS: We searched two databases, PubMed and Science Direct, for all relevant articles published until March 2025. Statistical software R version 4.4.2 was then utilized to meta-analyze the area under receiver operating curves (AUROC) to identify the best-performing models.
RESULTS: After the eligibility assessment, 13 studies met the inclusion criteria and were thus included in the review. Only two studies developed models for patients in the ED, and the remaining 11 studies developed models for patients undergoing different surgeries under general anesthesia. The deep learning model with the best discriminative ability for difficult airways was VGG (AUC 0.84; 95% CI [0.83, 0.84] I2 = 0%). For the traditional machine learning models, those with good discriminative ability for difficult airways included SVM (AUC 0.80; 95% CI [0.65, 0.96] I2 = 99.7%) and NB (AUC 0.81; 95% CI [0.51, 1.10] I2 = 99.3%).
CONCLUSIONS: Our study found that while some AI models have good discriminative ability (AUC ≥ 0.80) for difficult airways, most of them have just average discriminative ability AUC < 0.80. This, therefore, indicates a need to develop models with better discriminative ability and to validate the developed models.
METHODS: We searched two databases, PubMed and Science Direct, for all relevant articles published until March 2025. Statistical software R version 4.4.2 was then utilized to meta-analyze the area under receiver operating curves (AUROC) to identify the best-performing models.
RESULTS: After the eligibility assessment, 13 studies met the inclusion criteria and were thus included in the review. Only two studies developed models for patients in the ED, and the remaining 11 studies developed models for patients undergoing different surgeries under general anesthesia. The deep learning model with the best discriminative ability for difficult airways was VGG (AUC 0.84; 95% CI [0.83, 0.84] I2 = 0%). For the traditional machine learning models, those with good discriminative ability for difficult airways included SVM (AUC 0.80; 95% CI [0.65, 0.96] I2 = 99.7%) and NB (AUC 0.81; 95% CI [0.51, 1.10] I2 = 99.3%).
CONCLUSIONS: Our study found that while some AI models have good discriminative ability (AUC ≥ 0.80) for difficult airways, most of them have just average discriminative ability AUC < 0.80. This, therefore, indicates a need to develop models with better discriminative ability and to validate the developed models.