Aperio iQC: Automated artifact detection and rescan for image quality control in digital pathology

Introduction: Digital and histological artifacts in whole slide images (WSIs) remain a key challenge in digital pathology, impacting image analysis and workflow efficiency. Artifacts such as missing or clipped tissue, image striping, out of focus regions, pen marks, and air bubbles can obscure critical histopathologic features and disrupt slide review. Current digital slide quality control is largely manual, labor intensive, time consuming, and subject to human variability. As whole slide imaging adoption and case volumes increase, scalable and objective quality assessment with automated correction has become essential.

Design: To address this gap, we developed Aperio iQC software, an automated digital slide quality control solution that detects common digital and histological artifacts and incorporates a targeted, configurable automated rescan workflow for digital artifacts, including missing or clipped tissue, image striping, and out of focus. This enables early identification and correction of compromised slides while minimizing unnecessary manual rescans. Workflow impact was evaluated using the artifact resolution rate, defined as the proportion of initially detected digital artifacts no longer flagged following rescanning. A previously validated AI model was used as a surrogate benchmark; Aperio iQC software has demonstrated 93.7-99.8% accuracy, 94.7-99.6% sensitivity, and 92.6-100% specificity for six common digital and histological artifacts, in routine practice.

Results: In this validation study, 1242 slides were scanned on the Aperio GT family of scanners. Due to the low natural incidence of digital artifacts (8-13%), digitally induced missing or clipped tissue artifact was introduced to supplement the dataset. Aperio iQC achieved a 100% automated rescan rate and resolved 92.95% of digital artifacts without human intervention, demonstrating reduced manual rescans and improved slide quality with implications for laboratory efficiency and turnaround time.

Learning Objectives:

1. Enable accurate, automated detection of digital and histological artifacts to ensure consistent slide quality at scale.
2. Implement a targeted, configurable automated rescan workflow to correct digital artifacts and reduce manual rescans.
3. Evaluate workflow impact by measuring artifact resolution rate, slide quality improvement, and efficiency gains.