Longitudinal Boundary Sharpness Coefficient Slopes Predict Time to Alzheimer's Disease Conversion in Mild Cognitive Impairment: A Survival Analysis Using the ADNI Cohort

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Longitudinal Boundary Sharpness Coefficient Slopes predict time to Alzheimer's disease conversion in mild cognitive impairment

advanced Published 30 Mar 2026
Action Steps
  1. Measure Boundary Sharpness Coefficient (BSC) from structural MRI scans
  2. Calculate longitudinal BSC slopes to assess boundary degradation over time
  3. Apply survival analysis to predict time to Alzheimer's disease conversion in mild cognitive impairment patients
  4. Validate results using the ADNI cohort dataset
Who Needs to Know This

Data scientists and AI engineers on a healthcare team can benefit from this research as it provides a new method for predicting Alzheimer's disease conversion, allowing for earlier intervention and more effective treatment planning.

Key Insight

💡 Longitudinal BSC slopes can predict time to Alzheimer's disease conversion, enabling earlier intervention and treatment planning

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🧠 AI predicts Alzheimer's conversion time using MRI scans #Alzheimers #AIinHealthcare

Key Takeaways

Longitudinal Boundary Sharpness Coefficient Slopes predict time to Alzheimer's disease conversion in mild cognitive impairment

Full Article

Title: Longitudinal Boundary Sharpness Coefficient Slopes Predict Time to Alzheimer's Disease Conversion in Mild Cognitive Impairment: A Survival Analysis Using the ADNI Cohort

Abstract:
arXiv:2603.26007v1 Announce Type: cross Abstract: Predicting whether someone with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) is crucial in the early stages of neurodegeneration. This uncertainty limits enrollment in clinical trials and delays urgent treatment. The Boundary Sharpness Coefficient (BSC) measures how well-defined the gray-white matter boundary looks on structural MRI. This study measures how BSC changes over time, namely, how fast the boundary degrades
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