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Related Concept Videos

Classification of Epithelial Tissues: Overview01:22

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The Thyroid Gland01:23

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Related Experiment Video

Updated: Sep 16, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Multi-modal convolutional neural network-based thyroid cytology classification and diagnosis.

Dandan Yang1, Tianlun Li1, Lu Li1

  • 1The Department of Pathology, Affiliated Hospital of Jining Medical University, Jining, China.

Human Pathology
|July 5, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence aids thyroid nodule diagnosis. A new model integrating cytologic and ultrasound images (CI-DUF) improves accuracy over models using only cytologic images (CI) for classifying thyroid fine-needle aspiration cytology.

Keywords:
Artificial intelligenceCNNMulti-modalPathologyThyroid cytologyUltrasound

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Area of Science:

  • Medical imaging
  • Artificial intelligence in pathology
  • Thyroid cytology

Background:

  • Accurate diagnosis of thyroid nodules is essential for treatment planning.
  • Artificial intelligence (AI) can enhance the efficiency and accuracy of cytological diagnoses.
  • Ultrasound-guided fine-needle aspiration is a key diagnostic procedure.

Purpose of the Study:

  • To develop and evaluate a novel diagnostic model (CI-DUF) integrating cytologic images and digital ultrasound features for thyroid fine-needle aspiration cytology.
  • To compare the performance of the CI-DUF model against a model using only cytologic images (CI).

Main Methods:

  • Retrospective analysis of 825 thyroid cytologic images from 384 patients.
  • Development of two AI models: one using cytologic images (CI) and another integrating cytologic and digital ultrasound features (CI-DUF).
  • Models were trained and tested using an 8:2 data split.

Main Results:

  • The CI-DUF model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.9326, compared to 0.9119 for the CI model.
  • The CI-DUF model demonstrated significantly improved accuracy, sensitivity, and specificity.
  • Enhanced classification performance for papillary carcinoma, follicular neoplasm, medullary carcinoma, and benign thyroid lesions.

Conclusions:

  • The CI-DUF model, leveraging multi-modal information, offers superior diagnostic performance in thyroid cytology.
  • This integrated approach shows significant potential for clinical application in thyroid nodule diagnosis.