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

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

Published on: April 21, 2023

From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review.

Vivek R Sant¹, Ashwath Radhachandran², Vedrana Ivezic²

¹Division of Endocrine Surgery, UT Southwestern Medical Center, Dallas, TX 75390, USA.

The Journal of Clinical Endocrinology and Metabolism

|April 28, 2024

Summary

Artificial intelligence (AI) in thyroid nodule diagnostics shows promise but requires careful implementation. Local validation and data sanitization are crucial for AI models to ensure accurate clinical performance.

Keywords:

artificial intelligence diagnostics machine learning thyroid nodules

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Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Medicine
Oncology Diagnostics

Background:

The application of artificial intelligence (AI) for predicting clinical outcomes in thyroid nodule diagnostics has rapidly expanded.
A significant challenge lies in selecting and implementing the optimal AI model for specific patient populations and clinical workflows.

Approach:

A comprehensive literature search of PubMed and IEEE Xplore was performed for AI-driven diagnostic studies on thyroid nodules published between January 1, 2015, and January 1, 2023.
Studies were screened for English language, prospective or external validation, primary research, focus on thyroid nodules, AI utilization, and relevance to standard clinical practice.
Quality was assessed using the Oxford level of evidence, with 61 relevant studies identified.

Key Points:

External validation was a common feature across all 61 studies, with 16 being prospective.
AI models primarily utilized ultrasound (US) images for malignancy prediction, with some assisting physicians in risk assessment.
Commercial AI product S-Detect was the most extensively validated among Food and Drug Administration-approved products.

Conclusions:

AI models predominantly leverage US images to predict thyroid nodule malignancy.
Successful integration of AI into clinical practice necessitates local data sanitization and revalidation to guarantee reliable performance.