Optimized deep learning model for diagnosing tonsil and adenoid hypertrophy through X-rays.

ABSTRACT:

Objective

To explore the application of a deep learning model based on lateral nasopharyngeal X-rays in diagnosing tonsillar and adenoid hypertrophy.

Methods

A retrospective study was conducted using DICOM images of lateral nasopharyngeal X-rays from pediatric outpatients aged 2-12 at our hospital from July 2014 to July 2024. The study included patients exhibiting varying degrees of respiratory obstruction symptoms (disease group). Initially, 1006 images were collected, but after excluding low-quality images and standardizing the imaging phase, 819 images remained. These images were divided into training and validation sets in an 8:2 ratio. The independent test set is consisted of 484 images. We delineated the target areas for tonsils and adenoids and used a YOLOv8n-based mo

SUBMITTER: Wu Z 

PROVIDER: S-EPMC11932914 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature