AI-Powered Automated Quality Control of Whole Slide Images by Artifact Detect Tool

Currently, quality control (QC) of whole slide images (WSIs) in a digital pathology workflow is a manual process.

An image analyst performs QC of select WSIs to detect artifacts. Some examples of such artifacts include- tissue fold, broken slide/coverslip, markings, air bubbles or image stitching errors. Technologist must manually add tag to the cases with quality issues and deliver corresponding glass slides for pathologist’s review or fix the issue and re-scan the slides.

This study was undertaken to evaluate effectiveness of an AI tool for automated detection of artifacts in WSIs.