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]
File in questo prodotto:
File Dimensione Formato  
derosa_bignami_2024_aa.pdf

accesso aperto

Descrizione: online first
Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 879.04 kB
Formato Adobe PDF
879.04 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/427230
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
  • OpenAlex ND
social impact