Predicting conversion from mild cognitive impairment to Alzheimer's disease using a Vision Transformer and hippocampal MRI slices

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Abstract

Convolutional neural networks (CNNs) have been the standard for computer vision tasks including applications in Alzheimers disease (AD). Recently, Vision Transformers (ViTs) have been introduced, which have emerged as a strong alternative to CNNs. A common precursor stage of AD is a syndrome called mild cognitive impairment (MCI). However, not all individuals diagnosed with MCI progress to AD. In this investigation, we aimed to assess whether a ViT can reliably predict converters versus non-converters. A transfer learning approach was used for model training, by applying a pretrained ViT model, fine-tuned on the ADNI dataset. The cohort comprised 575 individuals (299 stable MCI; 276 progressive MCI who converted within 36 months), from whom axial T1-weighted MRI slices covering the hippocampal region were used as model input. Results showed an average area under the receiver operating characteristic curve (AUC-ROC) on the test set of 0.74 {+/-} 0.02 (mean {+/-} SD), an accuracy of 0.69 {+/-} 0.03, a sensitivity of 0.65 {+/-} 0.07, a specificity of 0.72 {+/-} 0.06, and an F1-score for the progressive MCI class of 0.67 {+/-} 0.04. These findings demonstrate that a ViT approach achieves reasonable classification accuracy for predicting the conversion from MCI to AD by specifically focusing on the hippocampal region.

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