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The Thyroid Gland01:23

The Thyroid Gland

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The thyroid gland is a small, butterfly-shaped gland located in the neck and covers the anterior surface of the trachea. The gland has two lateral lobes connected by a thin tissue mass called the isthmus. Internally, each lobe comprises many small spherical structures known as thyroid follicles, surrounded by a network of blood vessels.
The follicles have a central cavity lined by simple cuboidal to squamous epithelial cells called follicular cells. These cells produce the glycoprotein...
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[A convolutional neural network based model for assisting pathological diagnoses on thyroid liquid-based cytology].

M H Ye1, W Y Chen1, B J Cai2

  • 1Department of Pathology, Hangzhou Medical College Zhejiang Provincial People's Hospital, Hangzhou 310014, China.

Zhonghua Bing Li Xue Za Zhi = Chinese Journal of Pathology
|April 8, 2021
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A new deep learning model assists thyroid cancer diagnosis from cytology slides, matching pathologist accuracy but significantly faster. This AI tool enhances diagnostic consistency and efficiency, potentially reducing missed diagnoses in pathology.

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Area of Science:

  • Computational pathology
  • Artificial intelligence in diagnostics
  • Thyroid cytology analysis

Background:

  • Pathological diagnosis of thyroid cytology specimens is crucial for patient management.
  • Current diagnostic methods can be time-consuming and subject to inter-observer variability.
  • Developing automated tools can improve efficiency and consistency in pathological assessments.

Purpose of the Study:

  • To develop and evaluate a convolutional neural network (CNN) model for assisting pathological diagnoses on thyroid liquid-based cytology specimens.
  • To compare the diagnostic performance and efficiency of the AI model against human pathologists.

Main Methods:

  • Collected and scanned 700 thyroid cytology slides for whole slide imaging (WSI).
  • Utilized YOLO network for detection and ResNet50 for classification within a deep learning framework.
  • Trained and tested the model on 512x512 patches at 10x and 40x magnifications, calculating diagnostic metrics.

Main Results:

  • The model achieved 90.01% accuracy, 89.31% sensitivity, and 92.51% specificity at 10x magnification.
  • Diagnostic time was significantly reduced to less than 1 second per case.
  • The 10x magnification model demonstrated higher reliability compared to the 40x model.

Conclusions:

  • The deep learning model's performance is comparable to pathologists' diagnostic capabilities, with superior efficiency.
  • This AI tool can enhance diagnostic consistency, improve efficiency, and lower the rate of missed diagnoses.
  • Future research should focus on larger datasets with diverse morphology to refine the model for clinical application.