Pioneering Leadership in AI, Computational Medicine, and Data Analytics

RRI brings together physicians, data scientists, mathematicians, engineers, theoretical physicists, and analysts, all united by one purpose: to transform ground-breaking research into transformative clinical care. Viewing complex problems through multiple lenses broadens our perspective and opens new avenues of inquiry. The simple words “I have a thought” can spark a discovery or change the trajectory of a research project.

Our Focus Areas

We use AI, computational medicine, and advanced analytics to develop new clinical strategies, treatments, protocols, and diagnostic tools that support clinicians and improve outcomes for patients. We operate at the intersection of clinical data, machine data, and real-world practice, with access to a large patient population and one of the world's largest and richest renal datasets. Because we have access to data that spans over 20 countries, we also have a unique opportunity to answer global questions and evaluate the effectiveness of specific healthcare interventions.

Research Innovations

Our interdisciplinary approach, the quality of our data, and our agile development process all contribute to the breadth and depth of our innovations. We've pioneered models designed to optimize anemia management and help reduce hospitalizations, and identify dialysis patients most likely to benefit from home therapy. We’ve published over 600 peer-reviewed studies since 2000, contributing not only to innovation but to a growing body of evidence-based science that shapes practice and policy.

Latest Research & News

Latest Research

  • Zijun Dong, Hanjie Zhang, Lin-Chun Wang, Andrea Nandorine Ban, Sarah Ren, Lela Tisdale, Maggie Han, Denzil Douglas, Piotr Starakiewicz, Norbert Shtaynberg, Nicholas Fuca, Andrzej Kozyra, Sindhuri Prakash-Polet, Valeria G Bittencourt, Laura Rosales M, Stephan Thijssen, Dean C Preddie, Peter Kotanko

    RESULTSWe analyzed 720 images from 120 incenter hemodialysis patients (six images per patient). Mean patient age was 62 years, 66 were male. Physicians classified 26 patients (22%) having "Advanced" aneurysms and 94 (78%) having "Not Advanced" aneurysms. The ACA achieved an AUROC of 0.908 (95% confidence interval: 0.843-0.963). At the maximum Youden index, ACA classified 42 aneurysms (35%) as "Advanced" and 78 (65%) as "Not Advanced," yielding a sensitivity of 96% and a specificity of 82%.CONCLUSIONThe ACA demonstrated promising accuracy in this initial masked evaluation. Further validation in larger, independent cohorts and under real-world conditions is warranted before broader clinical deployment.BACKGROUNDIn hemodialysis patients, arteriovenous fistulas (AVF) are the preferred vascular access for most hemodialysis patients. However, aneurysms of the AVF can lead to severe complications, including rupture and life-threatening exsanguination. To address this risk, we recently developed, trained, and validated a convolutional neural network capable of classifying digital images of arteriovenous aneurysms captured via mobile devices as showing either "Advanced" or "Not Advanced" aneurysms. This cloud-based aneurysm classification application (ACA) aims to facilitate early detection of advanced aneurysms, potentially reducing morbidity and improving outcomes. Our study aimed to compare the diagnostic performance of ACA with in-person aneurysm assessments by physicians specialized in vascular access care.METHODSWe conducted a single-masked, multicenter, observational, cross-sectional study at two vascular care centers with a catchment area of 21 hemodialysis clinics. Using mobile devices, research staff captured images of arteriovenous accesses and uploaded them to the cloud. The ACA then computed the probability of "Advanced" and "Not Advanced" aneurysms. Separately, physicians masked to the ACA results performed thorough physical and ultrasound examinations of the AVF and classified aneurysms as "Advanced" or "Not Advanced". Physician classifications served as the ground truth for ACA performance analysis.

No Results Found

Latest News

No Results Found

The MONDO Initiative

RRI is a founding member of the MONitoring Dialysis Outcomes (MONDO) initiative, an international consortium of kidney care providers, academic institutions, and subject matter experts dedicated to advancing data-driven research on kidney disease. MONDO integrates clinical data, domain expertise, and advanced analytics to:

• Deepen understanding of patient characteristics, practice patterns, and physiology
• Evaluate the real-world generalizability of therapies and technologies
• Develop advanced models and AI-driven solutions to improve outcomes