Year: 2024
Type: preprint
Abstract: Digital pathology using whole slide imaging (WSI) and artificial intelligence (AI) has the potential to transform diagnostic workflows, but adoption remains limited by technical complexity and scalability. We developed the Computational Renal Pathology Suite (ComPRePS), a scalable cloud-based platform that automates WSI ingestion, compartmental segmentation, feature extraction, and AI-assisted interpretation through an integrated high-performance architecture. ComPRePS was evaluated in two use cases. First, using 213 procurement biopsies, we compared conventional assessments with automated AI analyses and a hybrid AI-assisted expert workflow. ComPRePS AI-assisted methods achieved higher precision and significantly improved interobserver agreement for key lesions, including global glomerulosclerosis, interstitial fibrosis and tubular atrophy, and arterial intimal thickening. Second, ComPRePS enabled high-throughput quantitative profiling of glomerular and tubular features across minimal change disease, diabetic nephropathy, and amyloid nephropathy revealing disease-specific phenotypic patterns inaccessible to manual evaluation. Overall, ComPRePS improves reproducibility, interpretability, and objectivity in renal pathology, bridging computation with clinical practice.
Authors: Anindya S. Paul, Luis A. Rodrigues, Suhas Katari Chaluva Kumar, David Manthey, Samuel P. Border, Clara Pardinhas, Carolina Pimenta, Vitor Sousa, Arnaldo Figueiredo, Rui Alves, Briana A. Santo, Will Dunklin, Sumanth Devarasetty, Haitham Abdelazim, Agnes B. Fogo, Avi Z. Rosenberg, Oleksandr Moskalenko, Pinaki Sarder, Pinaki Sarder
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Computational Pathology of Proteinuric Diseases, G2253811589, A Computational IMage Analysis Platform (CIMAP) for HuBMAP, G6137325400, A Cloud Based Distributed Tool for Computational Renal Pathology, PREcision Medicine through IntErrogation of Rna in the kidnEy (PREMIERE), Computational Image Analysis of Renal Transplant Biopsies to Predict Graft Outcome, University of Illinois at Chicago KPMP CKD Recruitment Site, AKI Matched Phenotype Linked Evaluation with Tissue (AMPLE-Tissue), Central Hub for Kidney Precision Medicine, Boston Chronic Kidney Disease Research Biopsy Center, Cleveland Precision Medicine Chronic Kidney Disease Cohort, KPMP Kidney Mapping and Atlas Project (KMAP), Geographic and Environmental Health Equity in Kidney Precision Medicine
OpenAlex ID: W4393230491