MUSC researchers used machine learning on fMRI scans to predict which smokers would benefit from repetitive transcranial magnetic stimulation (rTMS) for quitting smoking.
Key Details
- 1MUSC study combines machine learning and fMRI to personalize rTMS for smoking cessation.
- 242 participants took part in an earlier study, split into real vs sham TMS groups.
- 3The salience network's connectivity in the brain, analyzed by AI, correlated best with positive rTMS outcomes.
- 4Machine learning enabled predictions of individual responsiveness to rTMS based on brain network analysis.
- 5Study published in Brain Connectivity; NIH grant support cited.
- 6The research establishes groundwork for precision neuromodulation and larger future trials.
Why It Matters
This study demonstrates how imaging AI can enhance precision medicine by analyzing fMRI scans to tailor neuromodulation therapies. The approach could extend to other substance use disorders, highlighting a broader potential impact for radiology-based AI tools.
Source
EurekAlert
Related News
•EurekAlert
AI Predicts Risks for Outpatient Stem Cell Therapy in Myeloma
Researchers use machine learning to predict adverse events during stem cell therapy for multiple myeloma, improving outpatient safety.
•EurekAlert
USC Unveils Joint Biomedical Engineering Department Bridging Medicine, Engineering, and Imaging
USC's medical and engineering schools launch a joint biomedical engineering department to accelerate interdisciplinary research and innovation, including imaging and AI.
•EurekAlert
AI-Enhanced CT Heart Fat Measurement Boosts Cardiovascular Risk Prediction
AI-derived measurement of heart fat from CT scans significantly improves long-term cardiovascular disease risk prediction.