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
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.
•EurekAlert
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.
•EurekAlert
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.