Multi-modality MRI for Alzheimer’s disease detection using deep learning

Houria, Latifa; Belkhamsa, Noureddine; Cherfa, Assia; Cherfa, Yazid

doi:10.1007/s13246-022-01165-9

Cited by 23 publications

(7 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Discrimination of NDD patients from controls could be performed by the application of an SVM based on ROI-or TOI-based FA values of previously defined NDD-associated brain structures (AD – Zhang et al, 2011 ; Houria et al, 2022 ; PD – Haller et al, 2012 ; Huang et al, 2023 ; ALS – Chen et al, 2020 ; Kocar et al, 2021 ; Münch et al, 2022 ).…”

Section: Postprocessing-related Contributions To Results Of Dti Studi...mentioning

confidence: 99%

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Müller,

Kassubek

2024

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

show abstract

Section: Postprocessing-related Contributions To Results Of Dti Studi...mentioning

confidence: 99%

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Müller,

Kassubek

2024

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…MCI, an early AD manifestation, can also be detected by DL models. MRI-based DL [ 79 , 80 , 84 , 86 ], PET-based DL [ 91 ] and computing models [ 99 ] can differentiate between MCI associated with AD and normal controls, with accuracies ranging from 71.4% to 99.6% and an AUC ranging from 62.45% to 62.59%.…”

Section: Resultsmentioning

confidence: 99%

Potential Ocular Biomarkers for Early Detection of Alzheimer’s Disease and Their Roles in Artificial Intelligence Studies

Chaitanuwong

Singhanetr²,

Chainakul

et al. 2023

Neurol Ther

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is the leading cause of dementia worldwide. Early detection is believed to be essential to disease management because it enables physicians to initiate treatment in patients with early-stage AD (early AD), with the possibility of stopping the disease or slowing disease progression, preserving function and ultimately reducing disease burden. The purpose of this study was to review prior research on the use of eye biomarkers and artificial intelligence (AI) for detecting AD and early AD. The PubMed database was searched to identify studies for review. Ocular biomarkers in AD research and AI research on AD were reviewed and summarized. According to numerous studies, there is a high likelihood that ocular biomarkers can be used to detect early AD: tears, corneal nerves, retina, visual function and, in particular, eye movement tracking have been identified as ocular biomarkers with the potential to detect early AD. However, there is currently no ocular biomarker that can be used to definitely detect early AD. A few studies that used AI with ocular biomarkers to detect AD reported promising results, demonstrating that using AI with ocular biomarkers through multimodal imaging could improve the accuracy of identifying AD patients. This strategy may become a screening tool for detecting early AD in older patients prior to the onset of AD symptoms.

show abstract

“…sMRI is a non-invasive brain imaging technique that can investigate changes in brain structure [150]. Changes in brain structure due to worsening cognitive impairment may include atrophy of specific brain regions, loss of brain tissue, and changes in the shape and size of certain brain structures [151,152].…”

Section: Structural Mri Datamentioning

confidence: 99%

A Survey of Deep Learning for Alzheimer’s Disease

Zhou

Wang

et al. 2023

MAKE

View full text Add to dashboard Cite

Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

show abstract

Multi-modality MRI for Alzheimer’s disease detection using deep learning

Cited by 23 publications

References 40 publications

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Potential Ocular Biomarkers for Early Detection of Alzheimer’s Disease and Their Roles in Artificial Intelligence Studies

A Survey of Deep Learning for Alzheimer’s Disease

Contact Info

Product

Resources

About