Application of Artificial Intelligence techniques for the detection of Alzheimer’s disease using structural MRI images

Zhao, Xinxing; Ang, Candice Ke En; Acharya, U. Rajendra; Cheong, Kang Hao

doi:10.1016/j.bbe.2021.02.006

Cited by 49 publications

(20 citation statements)

References 108 publications

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“…However, classifying pMCI and sMCI patients is challenging due to the subtle anatomical differences in the brain [13]. The four conventional feature extraction approaches usually mentioned in the literature for classifying pMCI and sMCI are voxel-based, slice-based, ROIbased, and patch-based [14], although they are not entirely mutually exclusive. In this section, before surveying recent advances in deep learning-based methods for classifying pMCI and sMCI, we will briefly review these four approaches by discussing the advantages and disadvantages of each group.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Indeed, because non-affected regions and normal slices must be chosen as the reference distribution, they cannot account for the disease and may be considered an anomaly [22]. Furthermore, choosing separate 2D slices may neglect the spatial dependencies of voxels in adjacent slices due to inter/intra anatomical variances in the brain images [14]. However, sliced-based techniques allow for the usage of a broader range of conventional and deep learning-based approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Pölsterl et al [35] proposed the dynamic affine feature map transform, an auxiliary module for CNNs that dynamically incites or represses each feature map of a convolutional layer based on both image and tabular biomarkers. A more detailed overview of deep learning algorithms for Alzheimer's disease classification can be found in [36], [14], [37].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Ashtari-Majlan

Seifi

Dehshibi

2022

IEEE J. Biomed. Health Inform.

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Early diagnosis of Alzheimer's disease and its prodromal stage, also known as mild cognitive impairment (MCI), is critical since some patients with progressive MCI will develop the disease. We propose a multi-stream deep convolutional neural network fed with patch-based imaging data to classify stable MCI and progressive MCI. First, we compare MRI images of Alzheimer's disease with cognitively normal subjects to identify distinct anatomical landmarks using a multivariate statistical test. These landmarks are then used to extract patches that are fed into the proposed multi-stream convolutional neural network to classify MRI images. Next, we train the architecture in a separate scenario using samples from Alzheimer's disease images, which are anatomically similar to the progressive MCI ones and cognitively normal images to compensate for the lack of progressive MCI training data. Finally, we transfer the trained model weights to the proposed architecture in order to fine-tune the model using progressive MCI and stable MCI data. Experimental results on the ADNI-1 dataset indicate that our method outperforms existing methods for MCI classification, with an F1-score of 85.96%.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

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

Ashtari-Majlan

Seifi

Dehshibi

2022

IEEE J. Biomed. Health Inform.

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“…Conventional ML, such as SVM, relies on laborious brain segmentation that requires complex image preprocessing techniques [82]. This challenge is addressed by DL approaches [83], which discover intricate structures in data without requiring prior feature selection or data preprocessing.…”

Section: Mri Biomarkersmentioning

confidence: 99%

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

Seo

Lee

2022

Life

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Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.

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“…In the healthcare domain, cloud-based image analysis or storing including a AI based classification is probably one of the most progressive areas. For example, Zhao et al introduce methods for AI techniques on MRI pictures for Alzheimer's classification [30]. In the discussion by Zhao et al, they mention the advantages of cloud-based processing, mainly referring to the advantage of a larger joint data pool and a related more reliable diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Requirements and Architecture of a Cloud Based Insomnia Therapy and Diagnosis Platform: A Smart Cities Approach

Reichenpfader

Hanke

2021

Smart Cities

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Insomnia is the most common sleep disorder worldwide. Its effects generate economic costs in the millions but could be effectively reduced using digitally provisioned cognitive behavioural therapy. However, traditional acquisition and maintenance of the necessary technical infrastructure requires high financial and personnel expenses. Sleep analysis is still mostly done in artificial settings in clinical environments. Nevertheless, innovative IT infrastructure, such as mHealth and cloud service solutions for home monitoring, are available and allow context-aware service provision following the Smart Cities paradigm. This paper aims to conceptualise a digital, cloud-based platform with context-aware data storage that supports diagnosis and therapy of non-organic insomnia. In a first step, requirements needed for a remote diagnosis, therapy, and monitoring system are identified. Then, the software architecture is drafted based on the above mentioned requirements. Lastly, an implementation concept of the software architecture is proposed through selecting and combining eleven cloud computing services. This paper shows how treatment and diagnosis of a common medical issue could be supported effectively and cost-efficiently by utilising state-of-the-art technology. The paper demonstrates the relevance of context-aware data collection and disease understanding as well as the requirements regarding health service provision in a Smart Cities context. In contrast to existing systems, we provide a cloud-based and requirement-driven reference architecture. The applied methodology can be used for the development, design, and evaluation of other remote and context-aware diagnosis and therapy systems. Considerations of additional aspects regarding cost, methods for data analytics as well as general data security and safety are discussed.

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Application of Artificial Intelligence techniques for the detection of Alzheimer’s disease using structural MRI images

Cited by 49 publications

References 108 publications

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

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

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

Requirements and Architecture of a Cloud Based Insomnia Therapy and Diagnosis Platform: A Smart Cities Approach

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