Artificial intelligence-augmented ultrasound diagnosis of follicular-patterned thyroid neoplasms: a multicenter retrospective study.

ABSTRACT:

Background

Conventional diagnostic tools, including ultrasound, fine-needle aspiration cytology, and intraoperative frozen section pathology, may fail to reliably distinguish between benign and malignant FNs, leading to unnecessary or inadequate surgical interventions. We aimed to develop and validate a deep learning (DL) system for the preoperative diagnosis of follicular-patterned thyroid neoplasms (FNs) using routine ultrasound images, with the goal of improving diagnostic accuracy and reducing unnecessary procedures.

Methods

In this multicenter, retrospective study, we included 3817 patients (2877 [75.4%] female) with a definitive diagnosis of FNs from 11 centers across China. All patients underwent preoperative ultrasound examinations. The dataset comprised 9393 ultraso

SUBMITTER: Shen H 

PROVIDER: S-EPMC12281033 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature