# Research on predictive model for tracheal tube sizes in adult double-lumen endotracheal intubation based on radiomics and artificial intelligence

## TL;DR

This paper develops an AI model using CT scans to predict tracheal tube sizes for safer and more efficient intubation procedures in adults.

## Contribution

The novel contribution is the use of radiomics and AI, specifically the Baidu Wenxin ERNIE model, to predict tracheal tube sizes with high accuracy.

## Key findings

- The Baidu Wenxin ERNIE model achieved an accuracy of 0.77 in predicting tracheal tube sizes.
- Radiomic features extracted from CT scans were effectively used to train the predictive model.
- The model offers a rapid and efficient method for airway assessment in clinical settings.

## Abstract

This study aims to develop a predictive model for tracheal tube sizes in adult double-lumen endotracheal intubation using radiomics and artificial intelligence (AI) technologies to enhance the safety and efficiency of intubation procedures.

A retrospective study design was adopted. Computed tomography (CT) imaging data of the neck and chest from 500 adult patients were collected, and radiomic features were extracted. After a rigorous screening, 390 patients were included in the analysis. Radiomics techniques were applied to analyze CT images and extract features related to tracheal tube size selection. Predictive models were constructed using AI algorithms, including random forests, decision tree, support vector machines, and Baidu Wenxin ERNIE.

Among the models constructed, the Baidu Wenxin ERNIE model exhibited the best predictive performance, achieving an accuracy of 0.77 on the test set. Primary evaluation metrics, including accuracy, precision, recall, and F1-score, were compared to determine the optimal predictive model.

This study successfully developed a predictive model for tracheal tube sizes in adult double-lumen endotracheal intubation based on radiomics and AI, demonstrating high predictive accuracy. This model has the potential to provide clinicians with a convenient, rapid, and efficient method of airway assessment, thereby enhancing the safety and efficiency of double-lumen endotracheal intubation.