
Weill Cornell Medicine develops an AI tool to score and track disease severity in blood cancer from bone marrow imaging.
Key Details
- 1New AI-based method analyzes the spatial architecture and morphology of cells in bone marrow biopsies for myelodysplastic neoplasms (MDS).
- 2Produces the MDS-Microarchitectural Perturbation Score (MDS-MAPS) from 82 features to indicate disease severity.
- 3Tool leverages routinely collected samples and standard imaging protocols, making hospital adoption feasible.
- 4MDS-MAPS shows improved ability to classify disease progression compared to current pathologist methods.
- 5Planned validation studies in larger patient cohorts and other related conditions are underway.
- 6Research also offers insights into gene mutations (e.g., TP53) and their impact on bone marrow structure.
Why It Matters

Source
EurekAlert
Related News

UVA's $4M NIH Grant Backs AI to Optimize Cardiac MRI Therapies
UVA secures $4 million NIH funding to develop GenStrain, an AI model using MRI data to personalize cardiac pacing therapies.

Machine Learning Models Predict Cancer Risk with Routine Health Data
Researchers developed machine learning models using routinely collected health data to predict individual risk for 20 common cancers, outperforming traditional methods for several cancer types.

Duke Receives $25M to Lead National Trustworthy AI Hub for Dementia and Aging
Duke University has launched a $25 million national center to make AI for aging and dementia clinically robust and trustworthy.