Artificial intelligence (AI) algorithms, particularly deep learning, are automatic and sophisticated methods that recognize complex patterns in imaging data providing high qualitative assessments. Several machine-learning and deep-learning models using imaging techniques have been recently developed and validated to predict difficult airways. Despite advances in AI modeling. In this review article, we describe the advantages of using AI models. We explore how these methods could impact clinical practice. Finally, we discuss predictive modeling for difficult laryngoscopy using machine-learning and the future approach with intelligent intubation devices.

The Future of Artificial Intelligence Using Images and Clinical Assessment for Difficult Airway Management / De Rosa, Silvia; Bignami, Elena; Bellini, Valentina; Battaglini, Denise. - In: ANESTHESIA AND ANALGESIA. - ISSN 0003-2999. - 2024:(2024), pp. 1-9. [10.1213/ane.0000000000006969]

The Future of Artificial Intelligence Using Images and Clinical Assessment for Difficult Airway Management

De Rosa, Silvia;
2024-01-01

Abstract

Artificial intelligence (AI) algorithms, particularly deep learning, are automatic and sophisticated methods that recognize complex patterns in imaging data providing high qualitative assessments. Several machine-learning and deep-learning models using imaging techniques have been recently developed and validated to predict difficult airways. Despite advances in AI modeling. In this review article, we describe the advantages of using AI models. We explore how these methods could impact clinical practice. Finally, we discuss predictive modeling for difficult laryngoscopy using machine-learning and the future approach with intelligent intubation devices.
2024
De Rosa, Silvia; Bignami, Elena; Bellini, Valentina; Battaglini, Denise
The Future of Artificial Intelligence Using Images and Clinical Assessment for Difficult Airway Management / De Rosa, Silvia; Bignami, Elena; Bellini, Valentina; Battaglini, Denise. - In: ANESTHESIA AND ANALGESIA. - ISSN 0003-2999. - 2024:(2024), pp. 1-9. [10.1213/ane.0000000000006969]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/427230
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