Review on Alzheimer Disease Detection Methods: Automatic Pipelines and Machine Learning Techniques

Shukla, Amar; Tiwari, Rajeev; Tiwari, Shamik

doi:10.3390/sci5010013

Cited by 38 publications

(12 citation statements)

References 134 publications

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“…Recent research studies have analyzed the usage of different ML methods to potentially detect or assess cognitive decline [18,19]. The methodologies vary from traditional ML approaches to advanced deep learning models, using different types of data, including genetic data, neuroimaging data, neuropsychological assessments, and, more recently, data from wearable devices [20].…”

Section: Related Workmentioning

confidence: 99%

Graph Learning and Deep Neural Network Ensemble for Supporting Cognitive Decline Assessment

Antonesi,

Rancea,

Cioara

et al. 2023

Technologies

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Cognitive decline represents a significant public health concern due to its severe implications on memory and general health. Early detection is crucial to initiate timely interventions and improve patient outcomes. However, traditional diagnosis methods often rely on personal interpretations or biases, may not detect the early stages of cognitive decline, or involve invasive screening procedures; thus, there is a growing interest in developing non-invasive methods benefiting also from the technological advances. Wearable devices and Internet of Things sensors can monitor various aspects of daily life together with health parameters and can provide valuable data regarding people’s behavior. In this paper, we propose a technical solution that can be useful for potentially supporting cognitive decline assessment in early stages, by employing advanced machine learning techniques for detecting higher activity fragmentation based on daily activity monitoring using wearable devices. Our approach also considers data coming from wellbeing assessment questionnaires that can offer other important insights about a monitored person. We use deep neural network models to capture complex, non-linear relationships in the daily activities data and graph learning for the structural wellbeing information in the questionnaire answers. The proposed solution is evaluated in a simulated environment on a large synthetic dataset, the results showing that our approach can offer an alternative as a support for early detection of cognitive decline during patient-assessment processes.

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Section: Related Workmentioning

confidence: 99%

Graph Learning and Deep Neural Network Ensemble for Supporting Cognitive Decline Assessment

Antonesi,

Rancea,

Cioara

et al. 2023

Technologies

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“…Brain imaging data, including Magnetic Resonance Imaging (MRI), is extensively utilized to investigate brain function (Du et al (2018); Shukla et al (2023b)). The approach's premise is that scrutinizing neuro-imaging data and detecting anomalies allows one to unravel the brain's workings.…”

Section: Introductionmentioning

confidence: 99%

BrainMNet: A Unified Neural Network Architecture for Brain Image Classification

Ghosh,

Gupta

2024

Preprint

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In brain-related diseases, including Brain Tumours and Alzheimer's, accurate and timely diagnosis is crucial for effective medical intervention. Current state-of-the-art (SOTA) approaches in medical imaging predominantly focus on diagnosing a single brain disease at a time. However, recent research has uncovered intricate connections between various brain diseases, realizing that treating one condition may lead to the development of others. Consequently, there is a growing need for accurate diagnostic systems addressing multiple brain-related diseases. Designing separate models for different diseases, however, can impose substantial overhead. To tackle this challenge, our paper introduces BrainMNet, an innovative neural network architecture explicitly tailored for classifying brain images. The primary objective is to propose a single, robust framework capable of diagnosing a spectrum of brain-related diseases. The paper comprehensively validates BrainMNet's efficacy, specifically in diagnosing Brain tumours and Alzheimer's disease. Remarkably, the proposed model workflow surpasses current SOTA methods, demonstrating a substantial enhancement in accuracy and precision. Furthermore, it maintains a balanced performance across different classes in the Brain tumour and Alzheimer's dataset, emphasizing the versatility of our architecture for precise disease diagnosis. BrainMNet undergoes an ablation study to optimize its choice of the optimal optimizer, and a data growth analysis verifies its performance on small datasets, simulating real-life scenarios where data progressively increases over time. Thus, this paper signifies a significant stride toward a unified solution for diagnosing diverse brain-related diseases.

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“…Machine Learning techniques have rapidly emerged as pivotal methodologies for identifying AD patients in recent years, demonstrating remarkable success. Binary class classification for AD achieves notably high accuracy when classifying AD/CN, AD/MCI, as well as CN/MCI [5]. However, distinguishing pMCI from sMCI remains a key challenge due to overlapping characteristics, a lack of discernible biomarkers derived from unstructured data, and the challenge of generalizing characteristics from diverse data modalities persists.…”

Section: Introductionmentioning

confidence: 99%

Prediction Of Mild Cognitive Impairment to Alzheimer’s Disease Conversion Via Machine Learning

Chen

2024

HSET

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Alzheimer’s Disease (AD) is a progressive neurodegenerative condition characterized by deterioration of cognitive functions. Although there does not exist a cure for AD, early diagnosis and intervention during stages of Mild Cognitive Impairment (MCI) can be incredibly beneficial in slowing its development. However, predicting the conversion from MCI to AD remains a challenging task. This paper aims to provide an overview of the current research on predicting MCI to AD conversion, with a focus on machine learning methodologies to aid feature extraction and classification. Through a literature review, we aim to offer insights into the latest state-of-the-art techniques to predict MCI to AD conversion as well as prevailing trends in this field including deep learning, transfer learning, contrastive learning, and graph neural networks. We conclude from our study that utilizing AD/HC classes for feature extraction is a promising approach for generating discriminative features for stable MCI and progressive MCI classification. In addition, employing multi-modality models is instrumental in attaining a robust validation framework for the early diagnosis of AD.

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Review on Alzheimer Disease Detection Methods: Automatic Pipelines and Machine Learning Techniques

Cited by 38 publications

References 134 publications

Graph Learning and Deep Neural Network Ensemble for Supporting Cognitive Decline Assessment

Graph Learning and Deep Neural Network Ensemble for Supporting Cognitive Decline Assessment

BrainMNet: A Unified Neural Network Architecture for Brain Image Classification

Prediction Of Mild Cognitive Impairment to Alzheimer’s Disease Conversion Via Machine Learning

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