ComPRePS: an automated cloud-based image analysis tool to democratize AI in digital pathology

Sayat Mimar; Anindya S. Paul; Nicholas Lucarelli; Samuel Border; Ahmed Naglah; Laura Barisoni; Jeffrey Hodgin; Avi Z. Rosenberg; William Clapp; Pinaki Sarder

doi:10.1117/12.3008469

3 April 2024 ComPRePS: an automated cloud-based image analysis tool to democratize AI in digital pathology

Sayat Mimar, Anindya S. Paul, Nicholas Lucarelli, Samuel Border, Ahmed Naglah, Laura Barisoni, Jeffrey Hodgin, Avi Z. Rosenberg, William Clapp, Pinaki Sarder

Author Affiliations +

Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 129330Z (2024) https://doi.org/10.1117/12.3008469
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

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Sayat Mimar, Anindya S. Paul, Nicholas Lucarelli, Samuel Border, Ahmed Naglah, Laura Barisoni, Jeffrey Hodgin, Avi Z. Rosenberg, William Clapp, and Pinaki Sarder "ComPRePS: an automated cloud-based image analysis tool to democratize AI in digital pathology", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 129330Z (3 April 2024); https://doi.org/10.1117/12.3008469

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