Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 20, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.7K

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

Zhiqing Wu1, Ran Zhuo1, Yali Yang2

  • 1Department of Pediatric Surgery, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.

Frontiers in Oncology
|March 26, 2025
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The endoplasmic reticulum is a target organelle for trivalent dimethylarsinic acid (DMAIII)-induced cytotoxicity.

Toxicology and applied pharmacology·2012
Same author

(E)-1-{4-[Bis(4-bromo-phen-yl)meth-yl]piperazin-1-yl}-3-(4-eth-oxy-phen-yl)prop-2-en-1-one.

Acta crystallographica. Section E, Structure reports online·2012
Same author

(E)-1-{4-[Bis(4-bromo-phen-yl)meth-yl]piperazin-1-yl}-3-(4-methyl-phen-yl)prop-2-en-1-one.

Acta crystallographica. Section E, Structure reports online·2012
Same author

(E)-3-(1,3-Benzodioxol-5-yl)-1-{4-[bis-(4-meth-oxy-phen-yl)meth-yl]piperazin-1-yl}prop-2-en-1-one.

Acta crystallographica. Section E, Structure reports online·2012
Same author

Economic evaluation of first-line treatments for metastatic renal cell carcinoma: a cost-effectiveness analysis in a health resource-limited setting.

PloS one·2012
Same author

Metabolism studies of casticin in rats using HPLC-ESI-MS(n).

Biomedical chromatography : BMC·2012
Same journal

Feasibility of uniportal thoracoscopic sublobar resection without chest tube drainage: a retrospective cohort study.

Frontiers in oncology·2026
Same journal

Real-world effectiveness and safety of carfilzomib, pomalidomide, and dexamethasone in relapsed/refractory multiple myeloma: a retrospective analysis from China.

Frontiers in oncology·2026
Same journal

Caregiver satisfaction with early integrated palliative care in oncology: secondary outcomes from the PALLiON cluster-RCT.

Frontiers in oncology·2026
Same journal

Intracranial mesenchymal tumor with FET::CREB fusion: a rare case report.

Frontiers in oncology·2026
Same journal

The multifaceted roles of mitochondria and their therapeutic transformation: a new perspective on triple-negative breast cancer treatment.

Frontiers in oncology·2026
Same journal

Trastuzumab emtansine versus trastuzumab plus pertuzumab for HER2-positive breast cancer with residual disease after neoadjuvant therapy: a real-world study.

Frontiers in oncology·2026
See all related articles

A deep learning model using lateral nasopharyngeal X-rays accurately diagnoses pediatric tonsillar and adenoid hypertrophy. The YOLOv8-ResNet fusion model offers improved diagnostic accuracy and consistency for this common condition.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pediatric Otolaryngology

Background:

  • Tonsillar and adenoid hypertrophy are common causes of pediatric respiratory obstruction.
  • Accurate diagnosis is crucial for effective treatment and management.
  • Traditional diagnostic methods can be subjective and time-consuming.

Purpose of the Study:

  • To evaluate a deep learning model for diagnosing tonsillar and adenoid hypertrophy using lateral nasopharyngeal X-rays.
  • To compare the performance of different convolutional neural network models in classifying hypertrophy severity.
  • To identify an optimal deep learning model for clinical application.

Main Methods:

  • A retrospective study analyzed 819 lateral nasopharyngeal X-ray images from pediatric outpatients (aged 2-12).
Keywords:
ResNet18YOLOv8adenoidartificial intelligence in medicinediagnostic imagingtonsillar

More Related Videos

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

1.7K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.3K

Related Experiment Videos

Last Updated: May 20, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.7K
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

1.7K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.3K
  • A YOLOv8n model performed object detection for tonsils and adenoids, followed by classification using various CNNs.
  • Model performance was assessed using ROC-AUC, accuracy, precision, recall, and F1 score on training, validation, and test sets.

    • Main Results:

    • The YOLOv8-ResNet fusion model demonstrated excellent performance in diagnosing tonsillar and adenoid hypertrophy.
    • This combined model significantly improved diagnostic accuracy and consistency.
    • ResNet18, within the fusion model, was highlighted for its efficiency and effectiveness.

    Conclusions:

    • Deep learning models, specifically the YOLOv8n-ResNet18 combination, show significant advantages for diagnosing pediatric tonsillar and adenoid hypertrophy.
    • This AI-driven approach enhances diagnostic capabilities, offering a more objective and consistent assessment.
    • The findings support the clinical utility of this model in pediatric respiratory obstruction evaluation.