Deep learning for real-time detection of breast cancer presenting pathological nipple discharge by ductoscopy.

ABSTRACT:

Objective

As a common breast cancer-related complaint, pathological nipple discharge (PND) detected by ductoscopy is often missed diagnosed. Deep learning techniques have enabled great advances in clinical imaging but are rarely applied in breast cancer with PND. This study aimed to design and validate an Intelligent Ductoscopy for Breast Cancer Diagnostic System (IDBCS) for breast cancer diagnosis by analyzing real-time imaging data acquired by ductoscopy.

Materials and methods

The present multicenter, case-control trial was carried out in 6 hospitals in China. Images for consecutive patients, aged ≥18 years, with no previous ductoscopy, were obtained from the involved hospitals. All individuals with PND confirmed from breast lesions by ductoscopy were eligible. Images from

SUBMITTER: Xu F 

PROVIDER: S-EPMC10073663 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature