Large-scale validation study of an improved semiautonomous urine cytology assessment tool: AutoParis-X.

ABSTRACT:

Background

Adopting a computational approach for the assessment of urine cytology specimens has the potential to improve the efficiency, accuracy, and reliability of bladder cancer screening, which has heretofore relied on semisubjective manual assessment methods. As rigorous, quantitative criteria and guidelines have been introduced for improving screening practices (e.g., The Paris System for Reporting Urinary Cytology), algorithms to emulate semiautonomous diagnostic decision-making have lagged behind, in part because of the complex and nuanced nature of urine cytology reporting.

Methods

In this study, the authors report on the development and large-scale validation of a deep-learning tool, AutoParis-X, which can facilitate rapid, semiautonomous examination of urine cytol

SUBMITTER: Levy JJ 

PROVIDER: S-EPMC11251731 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature