A Multi-Stream Convolutional Neural Network for Classification of Progressive MCI in Alzheimer’s Disease Using Structural MRI Images

Ashtari-Majlan, Mona; Seifi, Abbas; Dehshibi, Mohammad Mahdi

doi:10.1109/jbhi.2022.3155705

Cited by 39 publications

(18 citation statements)

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“…Our method consistently outperforms previous studies in three indicators of classification performance including sensitivity (Sen), specificity (Spec), and accuracy (Acc). The VIT-S archives 83.27% in accuracy and 85.07% which is about a 1 and 4% improvement, respectively in comparison with the other studies (Eskildsen et al, 2013;Basaia et al, 2019;Bae et al, 2021;Zhang et al, 2021;Zhu et al, 2021;Ashtari-Majlan et al, 2022). VIT-B also shows a significant performance enhancement in specificity with 82.22%.…”

Section: Classification Performance On Adnimentioning

confidence: 67%

“…In this study, we investigated a comparative study focusing on the predictive performance of vision transformers based on mid-sagittal slices sMRI data of the ADNI. Our proposed method outperformed the current state-of-the-art MRI-based studies on MCI progression diagnosis ( Basaia et al, 2019 ; Bae et al, 2021 ; Zhu et al, 2021 ; Ashtari-Majlan et al, 2022 ) with an accuracy of 83.27%, specificity of 85.07%, and sensitivity of 81.48%. These results imply that using vision transformers equipped with attention power could achieve better classification performance compared with the current CNN architecture.…”

Section: Discussionmentioning

confidence: 80%

“…VIT-B also shows a significant performance enhancement in specificity with 82.22%. Although Ashtari-Majlan et al (2022) have shown higher specificity (99.70%), their results are lower in both accuracy and sensitive indicators. Figure 4 shows the confusion matrix of the VIT-S model which has the best result among our methods with an AUC of 0.87.…”

Section: Results and Analysismentioning

confidence: 86%

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

Hoang

Kim

2023

Front. Aging Neurosci.

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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.

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Section: Classification Performance On Adnimentioning

confidence: 67%

Section: Discussionmentioning

confidence: 80%

Section: Results and Analysismentioning

confidence: 86%

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

Hoang

Kim

2023

Front. Aging Neurosci.

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“…Early detection of mild cognitive impairment (MCI) and the prodromal stage of Alzheimer's disease are crucial since some people with MCI will go on to acquire Alzheimer's disease. A multi-stream deep convolutional neural network may be used by researchers to distinguish between MCI that is stable and MCI that is developing [16]. Researchers first contrast MRI scans of individuals with Alzheimer's disease with those of individuals with normal cognition in order to detect anatomical features.…”

Section: In-depth Review Of Different Models For Identification Of Hu...mentioning

confidence: 99%

Review Of Models & Techniques For Identification Of Human Health Diseases

2022

pnr

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Human health diseases are caused due to genetic disorders, environmental conditions, presence of Alpha synuclein bodies, etc. Identification of this disease can be done via estimation of body tremors, slowed movements, fever, change in corpuscles, rigid muscles, impaired posture & balance, pain analysis, loss of automatic movements, speech changes, and writing changes. To estimate these effects, researchers have proposed a wide variety of machine learning techniques that can analyze speech signals, electroencephalogram (EEG) signals, electrocardiogram (ECG) signals, handwriting variations, gait identification & classification, etc. These models are highly variant in terms of their internal operating characteristics, which makes it ambiguous for clinical designers to identify optimal models for their deployments. Furthermore, these models also showcase highly variant performance levels as per their input feature sets. Thus, it is difficult to identify optimal models for a given set of input parameters. To overcome these issues, this paper initially reviews a wide variety of Human Health disease identification models in terms of their contextual nuances, operating advantages, functional limitations, quantitative characteristics, and deployment-specific future scopes. This will assist readers to identify functional models that can be applied for their operation-specific use cases. After referring this comparison, readers will be presented with a comparative analysis of these models in terms of their detection accuracy, classification delay, input datasets, computational complexity, and scalability levels. Based on this comparison, readers will be able to identify optimally performing models as per their qualitative characteristics.

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“…However, the detection of the CSF biomarkers is invasive and may cause discomfort and side effects in subjects, thus limiting the application of this method in early AD diagnosis. In addition, classification of MCI into progressive MCI (pMCI) and stable MCI (sMCI) is also currently popular method for MCI conversion risk prediction [20]- [24]. These studies construct deep learning models based on the extracted neuroimaging features, such as tissue volumes and cortical thickness, etc.…”

Section: Introductionmentioning

confidence: 99%

Risk Prediction of Alzheimer’s Disease Conversion in Mild Cognitive Impaired Population Based on Brain Age Estimation

Liu

Dong

Sun

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Alzheimer's disease (AD) is one of the most common neurodegenerative diseases in the world. To reduce the incidence of AD, it's essential to quantify the AD conversion risk of mild cognitive impaired (MCI) individuals. Here, we propose an AD conversion risk estimation system (CRES), which contains an automated MRI feature extractor, brain age estimation (BAE) module, and AD conversion risk estimation module. The CRES is trained on 634 normal controls (NC) from the public IXI and OASIS cohorts, then it is evaluated on 462 subjects (106 NC, 102 stable MCI (sMCI), 124 progressive MCI (pMCI) and 130 AD) from the ADNI dataset. Experimental results show that the MRI derived age gap (AG, chronological age subtracted from the estimated brain age) significantly distinguish NC, sMCI, pMCI and AD groups with pvalue = 0.000017. Considering AG as the primary factor, incorporating gender and Minimum Mental State Examination (MMSE) for more robust Cox multi-variate hazard analysis, we concluded that each additional year in AG is associated with 4.57% greater AD conversion risk for the MCI group. Furthermore, a nomogram was drawn to describe MCI conversion risk at the individual level in the next 1 year, 3 years, 5 years and even 8 years from baseline. This work demonstrates that CRES can estimate AG based on MRI data, evaluate AD conversion risk of the MCI subjects, and identify the individuals with high AD conversion risk, which is valuable for effective intervention and diagnosis within an early period.

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A Multi-Stream Convolutional Neural Network for Classification of Progressive MCI in Alzheimer’s Disease Using Structural MRI Images

Cited by 39 publications

References 50 publications

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

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

Review Of Models & Techniques For Identification Of Human Health Diseases

Risk Prediction of Alzheimer’s Disease Conversion in Mild Cognitive Impaired Population Based on Brain Age Estimation

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