Early prediction of Alzheimer’s disease using artificial intelligence and cortical features on T1WI sequences

TL;DR

This paper presents a new AI model that uses MRI scans to predict Alzheimer's disease progression, helping doctors make early treatment decisions.

Contribution

A novel morphology-network-clinical model and multi-predictor nomogram for early Alzheimer’s prediction using cortical MRI features.

Findings

The morphology-network-clinical model achieved the highest concordance index of 0.951 in training data.

The model maintained strong performance in validation data with a concordance index of 0.880.

The model could aid in early personalized treatment decisions for Alzheimer’s disease.

Abstract

Accurately predicting the progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is a challenging task, which is crucial for helping develop personalized treatment plans to improve prognosis.

To develop new technology for the early prediction of AD using artificial intelligence and cortical features on MRI.

A total of 162 MCI patients were included from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. By using a 3D-MPRAGE sequence, T1W images for each patient were acquired. All patients were randomly divided into a training set (n = 112) and a validation set (n = 50) at a ratio of 7:3. Morphological features of the cerebral cortex were extracted with FreeSurfer software. Network features were extracted from gray matter with the GRETNA toolbox. The network, morphology, network-clinical, morphology-clinical, morphology-network and morphology-network-clinical models were developed by multivariate Cox proportional hazard model. The performance of each model was assessed by the concordance index (C-index).

In the training group, the C-indexes of the network, morphology, network-clinical, morphology-clinical, morphology-network and morphology-network-clinical models were 0.834, 0.926, 0.915, 0.949, 0.928, and 0.951, respectively. The C-indexes of those models in the validation group were 0.765, 0.784, 0.849, 0.877, 0.884, and 0.880, respectively. The morphology-network-clinical model performed the best. A multi-predictor nomogram with high accuracy for individual AD prediction (C-index = 0.951) was established.

The early occurrence of AD could be accurately predicted using our morphology-network-clinical model and the multi-predictor nomogram. This could help doctors make early and personalized treatment decisions in clinical practice, which showed important clinical significance.

Figures