Brazilian and French researchers have developed an imaging-based AI tool to predict how multiple sclerosis patients will respond to natalizumab treatment.
Key Details
- 1Combines high-content cell imaging and machine learning to analyze patient blood samples before natalizumab therapy.
- 2Study used over 400 cell morphological features, with 130 key characteristics for prediction.
- 3Tool achieved 92% accuracy in discovery and 88% in validation cohorts for predicting drug response.
- 4Non-responders showed distinct actin remodeling and cell morphology (more elongated CD8+ T cells).
- 5Findings published in Nature Communications, suggesting potential for broader disease and drug applications.
Why It Matters

Source
EurekAlert
Related News

NIH-Backed AI Model Predicts Cancer Survival Using Single-Cell Data
Researchers have developed scSurvival, a machine learning tool that uses single-cell tumor data to accurately predict cancer patient survival and identify high-risk cell populations.

AI Pathology Model Outperforms PD-L1 in Predicting NSCLC Immunotherapy Response
MD Anderson's Path-IO machine learning platform accurately predicts immunotherapy responses in metastatic non-small cell lung cancer, surpassing current biomarker standards.

Deep Learning Pathomics Platform Improves Immunotherapy Prediction in Lung Cancer
A deep learning pathomics platform accurately predicts immunotherapy response in metastatic NSCLC using routine pathology slides.