Analysis of upper airway CT-based radiomics in adult obstructive sleep apnea.

ABSTRACT:

Purpose

Computed tomography (CT) is a key tool for evaluating the upper airway in adult patients with obstructive sleep apnea (OSA). This study aimed to assess the value of CT-based radiomics for OSA evaluation.

Methods

A total of 79 OSA patients and 19 healthy controls (HCs) were recruited between January 2023 and June 2024 and underwent upper airway CT scans. Radiomic features were extracted from CT images, and data processed using Python. Radiomic models were then developed to evaluate and predict OSA using ten machine learning algorithms. Model performance was assessed using area under the curve (AUC) values, calibration, and decision curve analysis (DCA).

Results

The NaiveBayes machine learning algorithm based on radiomic features achieved the best result, and th

SUBMITTER: Lin M 

PROVIDER: S-EPMC12835338 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature