Abstract 5442: SlideQC: An AI-based tool for automated quality control of whole-slide digital pathology images

Abstract

Abstract Introduction: Artifacts are often introduced during tissue collection and processing, slide preparation, and/or when generating whole slide images (WSI). The presence of artifacts has a negative impact on the digital pathology workflow as artifacts may hinder diagnostic reporting and can lead to false positive and false negative results when using image analysis algorithms or computer-aided diagnosis systems. Manual quality control of WSI is a time-consuming procedure and therefore automated quality control tools, which report and exclude artifacts, are highly desirable to streamline digital pathology workflows. To automate the quality control step, we developed SlideQC, an AI-based quality control tool that automatically detects, reports, and outlines artifacts such as air bubbles, dust/debris, folds, out-of-focus,and pen marks, in both research and clinical workflows. Methods: SlideQC was trained with a DenseNet-based network using 1984 annotations for artifacts including air bubbles, dust/debris, folds, out-of-focus, and pen markers, across 254 Haematoxylin and Eosin (H&E) stained WSI from more than 9 tissue types. A set of 2048 annotations from synthetically generated out-of-focus images was added to supplement the training data. The performance of the SlideQC was evaluated on an external test cohort of 49 WSI H&E images sourced from the open-source database ‘HistoQCRepo’, across 375 annotations (tissue and artifact), and compared with the performance of HistoQC, an open-source quality control tool for digital pathology slides. Results: On the external test cohort, SlideQC showed high sensitivity, specificity, and F1-score with average values of 0.93, 0.99, and 0.93, across the five artifact types. In the same cohort, HistoQC attained an average sensitivity, specificity, and F1-score of 0.65, 0.79, and 0.54, respectively. Conclusions: SlideQC achieved high sensitivity, specificity, and F1-score on an external test cohort. SlideQC can add efficiency gains to a workflow by performing quality control on 100% of slides rather than the currently manually performed on only a subset of the slides in clinical pathology departments. SlideQC can allowthe triaging and alerting of slides containing a high level of artifact within a digital pathology workflow. The tool can also be used to exclude the artifact region from downstream analysis by subsequent image analysis algorithms. Citation Format: Daniela Rodrigues, Stefan Reinhard, Therese Waldburger, Daniel Martin, Suzana Couto, Inti Zlobec, Peter Caie, Erik Burlingame. SlideQC: An AI-based tool for automated quality control of whole-slide digital pathology images. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5442.