AIMC Topic: Intubation, Intratracheal

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Development and external validation of an interpretable machine learning model for the prediction of intubation in the intensive care unit.

Scientific reports
Given the limited capacity to accurately determine the necessity for intubation in intensive care unit settings, this study aimed to develop and externally validate an interpretable machine learning model capable of predicting the need for intubation...

Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.

Scientific reports
The need for intubation in methanol-poisoned patients, if not predicted in time, can lead to irreparable complications and even death. Artificial intelligence (AI) techniques like machine learning (ML) and deep learning (DL) greatly aid in accurately...

Improving difficult direct laryngoscopy prediction using deep learning and minimal image analysis: a single-center prospective study.

Scientific reports
Accurate prediction of difficult direct laryngoscopy (DDL) is essential to ensure optimal airway management and patient safety. The present study proposed an AI model that would accurately predict DDL using a small number of bedside pictures of the p...

A review of the current status and progress in difficult airway assessment research.

European journal of medical research
A difficult airway is a situation in which an anesthesiologist with more than 5 years of experience encounters difficulty with intubation or mask ventilation. According to the 2022 American Society of Anesthesiologists Practice Guidelines for the Man...

Reliable prediction of difficult airway for tracheal intubation from patient preoperative photographs by machine learning methods.

Computer methods and programs in biomedicine
BACKGROUND: Estimating the risk of a difficult tracheal intubation should help clinicians in better anaesthesia planning, to maximize patient safety. Routine bedside screenings suffer from low sensitivity.

Development of an Artificial Intelligence-Based Image Recognition System for Time-Sequence Analysis of Tracheal Intubation.

Anesthesia and analgesia
BACKGROUND: Total intubation time (TIT) is an objective indicator of tracheal intubation (TI) difficulties. However, large variations in TIT because of diverse initial and end targets make it difficult to compare studies. A video laryngoscope (VLS) c...

Deep Learning-Based Localization and Detection of Malpositioned Endotracheal Tube on Portable Supine Chest Radiographs in Intensive and Emergency Medicine: A Multicenter Retrospective Study.

Critical care medicine
OBJECTIVES: We aimed to develop a computer-aided detection (CAD) system to localize and detect the malposition of endotracheal tubes (ETTs) on portable supine chest radiographs (CXRs).

Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study.

Anaesthesia
While videolaryngoscopy has resulted in better overall success rates of tracheal intubation, airway assessment is still an important prerequisite for safe airway management. This study aimed to create an artificial intelligence model to identify diff...

Artificial Intelligence for Assessment of Endotracheal Tube Position on Chest Radiographs: Validation in Patients From Two Institutions.

AJR. American journal of roentgenology
Timely and accurate interpretation of chest radiographs obtained to evaluate endotracheal tube (ETT) position is important for facilitating prompt adjustment if needed. The purpose of our study was to evaluate the performance of a deep learning (DL...