Redefining Idiopathic Normal Pressure Hydrocephalus Using AI-Driven Brain Volumetry

Area of Science:

• Neuroimaging
• Artificial Intelligence
• Neurology

Background:

• Idiopathic normal pressure hydrocephalus (iNPH) presents diagnostic challenges, often mimicking cerebral atrophy or neurodegenerative diseases.
• Distinguishing iNPH from other conditions is complex due to a continuum of ventricular enlargement and mixed pathologies.
• Conventional neuroradiological markers have limitations including subjectivity and poor predictive value.

Purpose of the Study:

• To introduce artificial intelligence-based brain volumetry (AI-BrV) as a novel quantitative approach for iNPH assessment.
• To highlight AI-BrV's potential to overcome limitations of traditional 2D imaging markers.
• To explore AI-BrV's utility in improving differential diagnosis and predicting outcomes in iNPH.

Main Methods:

• AI-BrV enables automated, precise, and reproducible 3D quantification of brain structures, including CSF spaces and brain matter volumes.
• AI-BrV facilitates the derivation of composite indices and ratios for disease phenotyping.
• Pipelines allow retrospective analysis of large datasets and integration with clinical and machine-learning frameworks.

Main Results:

• AI-BrV addresses limitations of traditional methods like the Evans Index and DESH pattern.
• Quantitative structural assessment via AI-BrV offers improved precision and reproducibility.
• AI-BrV facilitates normative modeling and validation of volumetric biomarkers.

Conclusions:

• AI-BrV represents a paradigm shift in the quantitative structural assessment of iNPH.
• This technology promises enhanced patient selection, refined disease categorization, and informed treatment decisions for iNPH.
• AI-BrV offers a framework for a more precise, reproducible, and evidence-based approach to iNPH diagnosis and management.