ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.

Area of Science:

• Digital pathology
• Computational biology
• Artificial intelligence in healthcare

Background:

• High-resolution whole-slide imaging (WSI) generates gigapixel-scale data.
• Deep neural network (DNN)-based AI and improved computing enable advanced image analysis.
• Standardized data management principles like FAIR are crucial for biomedical research.

Purpose of the Study:

• To democratize AI-based image analysis in digital pathology.
• To introduce ComPRePS, an automated Computational Renal Pathology Suite.
• To enhance disease diagnostics and prognostics for precision medicine.

Main Methods:

• Developed ComPRePS, an end-to-end automated suite.
• Integrated massive scalability, cloud computing, and a web-based user interface.
• Utilized sophisticated AI algorithms for WSI analysis and feature extraction.

Main Results:

• ComPRePS provides on-demand cloud computing and data management for WSIs.
• The platform facilitates easy data upload, storage, and visualization.
• AI algorithms identify functional tissue units and extract relevant image features.

Conclusions:

• ComPRePS offers a scalable and user-friendly solution for AI-driven digital pathology.
• The suite supports improved diagnostics and prognostics in renal pathology.
• This platform advances precision medicine through accessible AI-based image analysis.