Effectiveness of convolutional neural networks in the interpretation of pulmonary cytologic images in endobronchial ultrasound procedures.

ABSTRACT:

Background

Rapid on-site cytologic evaluation (ROSE) helps to improve the diagnostic accuracy in endobronchial ultrasound (EBUS) procedures. However, cytologists are seldom available to perform ROSE in many institutions. Recent studies have investigated the application of deep learning in cytologic image analysis. As such, the present study analyzed lung cytologic images obtained by EBUS procedures, and employed deep-learning methods to distinguish between benign and malignant cells and to semantically segment malignant cells.

Methods

Ninety-seven patients who underwent 104 EBUS procedures were enrolled. Four hundred and ninety-nine lung cytologic images obtained via ROSE, including 425 malignant and 74 benign, and most malignant were lung adenocarcinoma (64.3%). All the ima

SUBMITTER: Lin CK 

PROVIDER: S-EPMC8683546 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature