Statistical testing on generative AI anomaly detection tools in Alzheimer's Disease diagnosis
This work addresses the need for reliable diagnostic tools for clinicians in Alzheimer's Disease, though it is incremental as it builds on existing generative AI methods with a focus on statistical validation.
The paper tackles the problem of unreliable statistical testing in generative AI anomaly detection for Alzheimer's Disease diagnosis by proposing selective inference to control false discovery rates, showing it successfully maintains statistical power while keeping p-values under desired alpha levels compared to traditional methods with inflated p-values.
Alzheimer's Disease is challenging to diagnose due to our limited understanding of its mechanism and large heterogeneity among patients. Neurodegeneration is studied widely as a biomarker for clinical diagnosis, which can be measured from time series MRI progression. On the other hand, generative AI has shown promise in anomaly detection in medical imaging and used for tasks including tumor detection. However, testing the reliability of such data-driven methods is non-trivial due to the issue of double-dipping in hypothesis testing. In this work, we propose to solve this issue with selective inference and develop a reliable generative AI method for Alzheimer's prediction. We show that compared to traditional statistical methods with highly inflated p-values, selective inference successfully controls the false discovery rate under the desired alpha level while retaining statistical power. In practice, our pipeline could assist clinicians in Alzheimer's diagnosis and early intervention.