Deep learning model for diagnosis of thyroid nodules with size less than 1 cm: A multicenter, retrospective study.

ABSTRACT:

Objective

To develop a ultrasound images based dual-channel deep learning model to achieve accurate early diagnosis of thyroid nodules less than 1 cm.

Methods

A dual-channel deep learning model called thyroid nodule transformer network (TNT-Net) was proposed. The model has two input channels for transverse and longitudinal ultrasound images of thyroid nodules, respectively. A total of 9649 nodules from 8455 patients across five hospitals were retrospectively collected. The data were divided into a training set (8453 nodules, 7369 patients), an internal test set (565 nodules, 512 patients), and an external test set (631 nodules, 574 patients).

Results

TNT-Net achieved an area under the curve (AUC) of 0.953 (95 % confidence interval (CI): 0.934, 0.969) on the internal t

SUBMITTER: Feng N 

PROVIDER: S-EPMC11566704 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature