Computer-assisted mitotic count using a deep learning-based algorithm improves interobserver reproducibility and accuracy.

ABSTRACT: The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists

SUBMITTER: Bertram CA 

PROVIDER: S-EPMC8928234 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature