An artificial intelligence ultrasound system's ability to distinguish benign from malignant follicular-patterned lesions.

ABSTRACT:

Objectives

To evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid lesions (FPTL), including follicular thyroid carcinoma (FTC), adenomatoid hyperplasia nodule (AHN) and follicular thyroid adenoma (FTA) and compare the diagnostic performance with radiologists of different experience levels.

Methods

We retrospectively reviewed 607 patients with 699 thyroid nodules that included 168 malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, two senior) and that of AI automatic diagnosis system in malignancy diagnosis of FPTL in terms of sensitivity, specificity and accuracy

SUBMITTER: Xu D 

PROVIDER: S-EPMC9660226 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature