AIMC Topic: Thyroid Nodule
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A Preliminary Study Comparing the Performance of Thyroid Molecular Tests to a Deep Learning Algorithm in Predicting Malignancy in Indeterminate Thyroid Fine Needle Aspiration Biopsies.
Thyroid : official journal of the American Thyroid Association
Mar 4, 2024
Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population.
Nature communications
Mar 4, 2024
Artificial Intelligence (AI) models for medical diagnosis often face challenges of generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid ultrasound dataset with significant diagnostic performance disparities acro...
Deep learning diagnostic performance and visual insights in differentiating benign and malignant thyroid nodules on ultrasound images.
Experimental biology and medicine (Maywood, N.J.)
Jan 26, 2024
This study aims to construct and evaluate a deep learning model, utilizing ultrasound images, to accurately differentiate benign and malignant thyroid nodules. The objective includes visualizing the model's process for interpretability and comparing ...
Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study.
BMC cancer
Nov 23, 2023
BACKGROUND: Calcification is a common phenomenon in both benign and malignant thyroid nodules. However, the clinical significance of calcification remains unclear. Therefore, we explored a more objective method for distinguishing between benign and m...
The performance of deep learning on thyroid nodule imaging predicts thyroid cancer: A systematic review and meta-analysis of epidemiological studies with independent external test sets.
Diabetes & metabolic syndrome
Oct 25, 2023
BACKGROUND AND AIMS: It is still controversial whether deep learning (DL) systems add accuracy to thyroid nodule imaging classification based on the recent available evidence. We conducted this study to analyze the current evidence of DL in thyroid n...
Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study.
European radiology
Oct 11, 2023
OBJECTIVES: This study aimed to propose a deep learning (DL)-based framework for identifying the composition of thyroid nodules and assessing their malignancy risk.
Value of Artificial Intelligence in Improving the Accuracy of Diagnosing TI-RADS Category 4 Nodules.
Ultrasound in medicine & biology
Aug 30, 2023
OBJECTIVE: Considerable heterogeneity is observed in the malignancy rates of thyroid nodules classified as category 4 according to the Thyroid Imaging Reporting and Data System (TI-RADS). This study was aimed at comparing the diagnostic performance o...
Stratifying High-Risk Thyroid Nodules Using a Novel Deep Learning System.
Experimental and clinical endocrinology & diabetes : official journal, German Society of Endocrinology [and] German Diabetes Association
Aug 21, 2023
INTRODUCTION: The current ultrasound scan classification system for thyroid nodules is time-consuming, labor-intensive, and subjective. Artificial intelligence (AI) has been shown to increase the accuracy of predicting the malignancy rate of thyroid ...
Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation.
Journal of digital imaging
Aug 14, 2023
Thyroid nodules occur in up to 68% of people, 95% of which are benign. Of the 5% of malignant nodules, many would not result in symptoms or death, yet 600,000 FNAs are still performed annually, with a PPV of 5-7% (up to 30%). Artificial intelligence ...
The auxiliary diagnosis of thyroid echogenic foci based on a deep learning segmentation model: A two-center study.
European journal of radiology
Aug 11, 2023
OBJECTIVE: The aim of this study is to develop AI-assisted software incorporating a deep learning (DL) model based on static ultrasound images. The software aims to aid physicians in distinguishing between malignant and benign thyroid nodules with ec...
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