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

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Published on: April 21, 2023

Extracting Thyroid Nodules Characteristics from Ultrasound Reports Using Transformer-based Natural Language

Aman Pathak¹, Zehao Yu¹, Daniel Paredes¹

¹Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|January 15, 2024

Summary

This study introduces a new AI model, GatorTron, for extracting thyroid nodule characteristics from ultrasound reports. GatorTron significantly improves the identification of key features for thyroid cancer evaluation.

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Area of Science:

Medical Informatics
Natural Language Processing
Oncology

Background:

Thyroid nodule characteristics are crucial for thyroid cancer evaluation but are often in unstructured clinical narratives.
Previous natural language processing (NLP) methods extracted limited characteristics (<9) using rule-based systems.

Purpose of the Study:

To systematically categorize and extract a comprehensive set of 16 clinically relevant thyroid nodule characteristics from ultrasound reports.
To compare the performance of state-of-the-art transformer-based NLP models for this task.

Main Methods:

A multidisciplinary team developed annotation guidelines and a corpus for thyroid nodule characteristics.
Five transformer-based NLP models (BERT, RoBERTa, LongFormer, DeBERTa, GatorTron) were evaluated.
The GatorTron model, a large language model, was specifically trained and tested.

Main Results:

GatorTron achieved the highest F1-scores: 0.8851 (strict) and 0.9495 (lenient) for characteristic extraction.
GatorTron also achieved an F1-score of 0.9321 for linking characteristics to nodules.
GatorTron outperformed other clinical transformer models in extracting 16 thyroid nodule characteristics.

Conclusions:

Transformer-based NLP, particularly GatorTron, can effectively extract a large number of clinically relevant thyroid nodule characteristics from ultrasound reports.
This approach enables better assessment of report documentation quality and patient outcomes using electronic health records.