Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images.

ABSTRACT:

Background

Functional adrenal tumors (FATs) are mainly diagnosed by biochemical analysis. Traditional imaging tests have limitations and cannot be used alone to diagnose FATs. In this study, we aimed to establish an artificially intelligent diagnostic model based on computed tomography (CT) images to distinguish different types of FATs.

Methods

A cohort study of 375 patients diagnosed with hyperaldosteronism (HA), Cushing's syndrome (CS), and pheochromocytoma in our center between March 2015 and June 2020 was conducted. Retrospectively, patients were randomly divided into three data sets: the training set (270 cases), the testing set (60 cases), and the retrospective trial set (45 cases). An artificially intelligent diagnostic model based on CT images was established by tran

SUBMITTER: Alimu P 

PROVIDER: S-EPMC10102777 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature