Archives: “Archives”

Rebecca Haberman, MD

https://www.coltonconsortium.org/directory/rebecca-haberman-md/

Assistant Professor, Department of Medicine, NYU Grossman School of Medicine / NYU Langone Health Associate Director, Psoriatic Arthritis Center, NYU Grossman School of Medicine / NYU Langone Health Assistant Director, […]

Abass Alavi, MD, PhD

https://www.coltonconsortium.org/directory/abass-alavi-md-phd/

Professor, Department of Radiology, Perelman School of Medicine, University of Pennsylvania Dr. Abass Alavi is a Professor of Radiology and Neurology and Director of Research Education in the Department of […]

Benjamin Abramoff, MD, MS

https://www.coltonconsortium.org/directory/benjamin-abramoff-md-ms/

Associate Professor, Department of Physical Medicine and Rehabilitation, Perelman School of Medicine, University of Pennsylvania Dr. Benjamin Abramoff is an Assistant Professor of Physical Medicine and Rehabilitation at the Perelman […]

Developing a Multi-parameter Prognostic Prediction Model for Disability Ranks and Progression of Patients with Multiple Sclerosis at the Early Stages of the Disease

https://www.coltonconsortium.org/projects/developing-a-multi-parameter-prognostic-prediction-model-for-disability-ranks-and-progression-of-patients-with-multiple-sclerosis-at-the-early-stages-of-the-disease/

Integrating clinical, imaging, and biological data from large real-world MS cohorts, this project builds a machine learning model to predict disability progression early and enable personalized treatment decisions.

A Data-Driven Framework for Predicting and Managing Flares in Inflammatory Bowel Disease

https://www.coltonconsortium.org/projects/a-data-driven-framework-for-predicting-and-managing-flares-in-inflammatory-bowel-disease/

Combining machine learning with microbiome and metabolomic data from 4,000+ IBD patients, this project develops tools to predict disease flares, identify therapeutic targets, and enable personalized flare prevention.

Shedding Light on the Invisible: A New Paradigm for Predicting Multiple Sclerosis Disease Progression Using Novel MRI Tools for Probing Pathology in Normal Appearing Tissues

https://www.coltonconsortium.org/projects/shedding-light-on-the-invisible-a-new-paradigm-for-predicting-multiple-sclerosis-disease-progression-using-novel-mri-tools-for-probing-pathology-in-normal-appearing-tissues/

Applying advanced quantitative MRI to detect pathology invisible to current clinical tools, this project builds an AI model to predict MS progression and enable earlier, more personalized diagnosis and treatment.

Early Detection and Diagnosis of Autoimmune Diseases Using Foundation AI Models

https://www.coltonconsortium.org/projects/early-detection-and-diagnosis-of-autoimmune-diseases-using-foundation-ai-models/

Applying self-supervised AI to multi-modal electronic health records — integrating clinical notes, labs, and imaging — this project builds scalable diagnostic models to detect autoimmune diseases earlier and more precisely.

Using Genetically Engineered Models to Study Pre-Autoimmune States

https://www.coltonconsortium.org/projects/using-genetically-engineered-models-to-study-pre-autoimmune-states/

Using the RIP-NINJA mouse model to study how PD-1 regulation in the pancreas prevents autoimmune induction and checkpoint therapy-induced diabetes.

Wearables and AI Framework for Inflammatory Arthritis Assessment and Management

https://www.coltonconsortium.org/projects/wearables-and-ai-framework-for-inflammatory-arthritis-assessment-and-management/

Using wearable sensors and machine learning to analyze real-world movement and sleep data, this project aims to predict treatment response earlier and enable more personalized care for inflammatory arthritis.

Biomarker Discovery for Early Prediction of Autoimmunity in Immunotherapy Patients Through Deep Immune Profiling and Temporal Graph Convolutional Networks

https://www.coltonconsortium.org/projects/biomarker-discovery-for-early-prediction-of-autoimmunity-in-immunotherapy-patients-through-deep-immune-profiling-and-temporal-graph-convolutional-networks/

Using temporal graph machine learning and deep immune profiling to predict which cancer immunotherapy patients will develop autoimmune adverse events before they occur.