Vision transformers for the prediction of mild cognitive impairment to Alzheimer’s disease progression using mid-sagittal sMRI

Abstract

BackgroundAlzheimer’s disease (AD) is one of the most common causes of neurodegenerative disease affecting over 50 million people worldwide. However, most AD diagnosis occurs in the moderate to late stage, which means that the optimal time for treatment has already passed. Mild cognitive impairment (MCI) is an intermediate state between cognitively normal people and AD patients. Therefore, the accurate prediction in the conversion process of MCI to AD may allow patients to start preventive intervention to slow the progression of the disease. Nowadays, neuroimaging techniques have been developed and are used to determine AD-related structural biomarkers. Deep learning approaches have rapidly become a key methodology applied to these techniques to find biomarkers.MethodsIn this study, we aimed to investigate an MCI-to-AD prediction method using Vision Transformers (ViT) to structural magnetic resonance images (sMRI). The Alzheimer’s Disease Neuroimaging Initiative (ADNI) database containing 598 MCI subjects was used to predict MCI subjects’ progression to AD. There are three main objectives in our study: (i) to propose an MRI-based Vision Transformers approach for MCI to AD progression classification, (ii) to evaluate the performance of different ViT architectures to obtain the most advisable one, and (iii) to visualize the brain region mostly affect the prediction of deep learning approach to MCI progression.ResultsOur method achieved state-of-the-art classification performance in terms of accuracy (83.27%), specificity (85.07%), and sensitivity (81.48%) compared with a set of conventional methods. Next, we visualized the brain regions that mostly contribute to the prediction of MCI progression for interpretability of the proposed model. The discriminative pathological locations include the thalamus, medial frontal, and occipital—corroborating the reliability of our model.ConclusionIn conclusion, our methods provide an effective and accurate technique for the prediction of MCI conversion to AD. The results obtained in this study outperform previous reports using the ADNI collection, and it suggests that sMRI-based ViT could be efficiently applied with a considerable potential benefit for AD patient management. The brain regions mostly contributing to prediction, in conjunction with the identified anatomical features, will support the building of a robust solution for other neurodegenerative diseases in future.

SNeurodCNN: Structure-focused Neurodegeneration Convolutional Neural Network for Modeling and Classification of Alzheimer’s Disease

“…The parasagittal view showed that an additional structural change was possible in the parietal lobe, which was not obvious in the midsagittal slices. The SNeurodCNN model region is most sensitive to the frontal lobe, occipital lobe, cerebellum, and parietal lobe, which is consistent with the ndings of clinical studies [25][26][27][28][29][30][31] and, in some cases, consistent with regions identi ed in one of the DL-based studies 7 ; however, our study further con rms the signi cance of cerebral atrophy on brain structure. Therefore, these ndings show that our proposed SNeurodCNN can signi cantly contribute to AD diagnosis and prognosis.…”

“…The evaluation of neurodegeneration using the midsagittal and parasagittal planes of the brain results in remarkable performance. These ndings surpass those of previous studies 7,10 . SNeurodCNN has shown promise in capturing neurodegenerative features from MR images.…”

“…Considering that structural changes in the brain and AD pathology are more prominent in the sagittal plane, their e cacy in AD classi cation has long been established 7,12 . This study explored two sagittal plane views, the midsagittal and parasagittal viewpoints, from the ADNI volumetric data in the sagittal plane.…”

“…The sagittal plane provides the most visible information for AD diagnosis 7 . Brain neurodegeneration is evident in the sagittal plane, particularly in the frontal lobe, cerebellum, occipital lobe, thalamus, and corpus callosum, where learning and memory, mental function, motor function, and sensory function 7 can be signi cantly impacted.…”

Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024

“…For example, a 2021 study showed that a vision transformer could accurately detect COVID-19 from chest computed tomography (CT) scans, outperforming established CNN architectures [139]. Other recent works have explored the use of vision transformers in analyzing mammograms [140], brain MRI scans [141], and many more medical images.…”