Context It is not clear how to integrate artificial intelligence (AI)-based models into diagnostic
workflows. Objective To develop and validate a deep-learning–based AI model (AI-Thyroid) …

Context Thyroid nodule ultrasound-based risk stratification schemas rely on the presence of
high-risk sonographic features. However, some malignant thyroid nodules have benign …

Background Breast tumor is a fatal threat to the health of women. Ultrasound (US) is a
common and economical method for the diagnosis of breast cancer. Breast imaging …

Purpose Ultrasound evaluation of thyroid nodules is the preferred technique, but it is
dependent on operator interpretation, leading to inter-observer variability. The current study …

Background Fluorodeoxyglucose uptake on positron emission tomography imaging has
been shown to be an independent risk factor for malignancy in thyroid nodules. More …

In recent years, deep convolutional neural networks (DCNNs) have shown promising
performance in medical image analysis, including breast lesion classification in 2D …

Abstract Purpose Beyond the Thyroid Imaging Reporting and Data System (TIRADS)
classification of thyroid nodules, additional factors must be weighed in the decision to …

Background Interpretation of ultrasound findings of thyroid nodules is subjective and labor-
intensive for radiologists. Artificial intelligence (AI) is a relatively objective and efficient …

Objectives To decrease call burden on pediatric neuroradiologists, we developed guidelines
for appropriate use of MR overnight. These guidelines were implemented using triage by in …

Introduction: The British Thyroid Association Ultrasound-classification is a risk stratification
model which grades thyroid nodules in U2–5 based on their sonographic appearance …