🧠 AI-Powered LF-MRI Enhances Brain Aging Detection in HIV

A recent study evaluated the feasibility of portable, low-field MRI (LF-MRI) combined with machine learning (ML) to assess brain atrophy and white matter changes in people with HIV (PWH) versus matched controls.

Why It Matters To Your Practice

▪ LF-MRI offers accessible, on-site neuroimaging for aging PWH at risk for cognitive decline.

▪ ML algorithms automate and expedite detection of subtle neuroanatomical changes.

▪ Supports earlier intervention and tailored management in outpatient settings.

▪ Potentially reduces reliance on high-field MRI, increasing clinical reach.

Clinical Implications

▪ Caudate, putamen, and white matter volumes were lower in PWH compared to vascular comorbidity controls.

▪ Hippocampal volumes were preserved in PWH, differentiating from typical Alzheimers patterns.

▪ No significant differences in white matter hyperintensities between groups.

▪ LF-MRI is viable for routine monitoring in neurology clinics.

Insights

▪ Automated ML segmentation (WMH-SynthSeg) in FreeSurfer delivers reliable volumetric data from LF-MRI.

▪ LF-MRI enables more frequent, cost-effective assessments versus traditional MRI.

▪ Distinct atrophy patterns may inform differential diagnosis in aging PWH.

▪ Technological advances could democratize neuroimaging in resource-limited settings.

The Bottom Line

▪ LF-MRI and AI-based segmentation are feasible and informative for monitoring brain aging in PWH.

▪ This approach could transform cognitive surveillance and care for at-risk populations.

▪ Further research is needed to validate findings and implement in clinical practice.

▪ Adoption of portable AI-enabled imaging may soon become standard in HIV care.