SlideInspect: From Pixel-Level Artifact Detection to Actionable Quality Metrics in Digital Pathology

The presence of artifacts in whole slide images (WSIs), such as tissue folds, air bubbles, and out- of-focus regions, can significantly impact WSI digitization, pathologists' evaluation, and the accuracy of downstream analyses. We present SlideInspect, a novel AI-based framework for comprehensive artifact detection and quality control in digital pathology. Our system leverages deep learning techniques to segment multiple artifact types across diverse tissue types and staining methods. SlideInspect provides a hierarchical output: a color-coded slide quality indicator (green, yellow, red) with recommended actions (no action, re-scan, re- mount, re-cut) based on artifact type and extent, and pixel-level segmentation masks for detailed analysis. The system operates at multiple magnifications (1.25× for tissue segmentation, 5× for artifact detection) and also incorporates stain quality assessment for histological stain evaluation. We validated SlideInspect on a large, multi-centric, multi-scanner dataset of over 3000 WSIs, demonstrating robust performance across different tissue types, staining methods, and scanning platforms. The system achieves high segmentation accuracy for various artifacts while maintaining computational efficiency (average processing time: 72.7 s per WSI). Pathologist evaluations confirmed the clinical relevance and accuracy of SlideInspect's quality assessments. By providing actionable insights at multiple levels of granularity, SlideInspect significantly improves the efficiency and standardization of digital pathology workflows. Its vendor-agnostic design and multi-stain capability make it suitable for integration into diverse clinical and research settings.