Automated diagnosis and management of follicular thyroid nodules based on the devised small-datasets interpretable foreground optimization network deep learning: A multicenter diagnostic study.

ABSTRACT:

Background

Currently, follicular thyroid carcinoma (FTC) has relatively low incidence with a lack of effective preoperative diagnostic means. To reduce the need for diagnostic invasive procedures and to address information deficiencies inherent in a small-dataset, we utilized interpretable foreground optimization network deep learning to develop a reliable preoperative FTC detection system.

Methods

In this study, a deep learning model (FThyNet) was established using preoperative ultrasound images. Data on patients in training and internal validation cohort (n=432) were obtained from XXX Hospital, China. Data on patients in external validation cohort (n=71) were obtained from four other clinical centers. We evaluated the predictive performance of FThyNet and its ability to ge

SUBMITTER: Yang Z 

PROVIDER: S-EPMC10498847 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature