Deep Ordinal Ranking for Multi-Category Diagnosis of Alzheimer's Disease using Hippocampal MRI data

TL;DR

This paper introduces a deep ordinal ranking model that leverages hippocampal MRI data to distinguish between multiple stages of Alzheimer's disease, improving early diagnosis by capturing the inherent severity order.

Contribution

The study proposes a novel deep ordinal ranking approach for multi-category AD diagnosis, focusing on disease severity and hippocampal morphology, outperforming traditional classification methods.

Findings

The model achieves higher accuracy than traditional multi-category classifiers.

It effectively captures the ordinal nature of disease progression.

Results are validated on a large ADNI cohort.

Abstract

Increasing effort in brain image analysis has been dedicated to early diagnosis of Alzheimer's disease (AD) based on neuroimaging data. Most existing studies have been focusing on binary classification problems, e.g., distinguishing AD patients from normal control (NC) elderly or mild cognitive impairment (MCI) individuals from NC elderly. However, identifying individuals with AD and MCI, especially MCI individuals who will convert to AD (progressive MCI, pMCI), in a single setting, is needed to achieve the goal of early diagnosis of AD. In this paper, we propose a deep ordinal ranking model for distinguishing NC, stable MCI (sMCI), pMCI, and AD at an individual subject level, taking into account the inherent ordinal severity of brain degeneration caused by normal aging, MCI, and AD, rather than formulating the classification as a multi-category classification problem. The proposed deep ordinal ranking model focuses on the hippocampal morphology of individuals and learns informative and discriminative features automatically. Experiment results based on a large cohort of individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) indicate that the proposed method can achieve better performance than traditional multi-category classification techniques using shape and radiomics features from structural magnetic resonance imaging (MRI) data.