Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review
S Grueso, R Viejo-Sobera - Alzheimer's research & therapy, 2021 - Springer
Background An increase in lifespan in our society is a double-edged sword that entails a
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
[HTML][HTML] Role of artificial intelligence in patient safety outcomes: systematic literature review
A Choudhury, O Asan - JMIR medical informatics, 2020 - medinform.jmir.org
Background: Artificial intelligence (AI) provides opportunities to identify the health risks of
patients and thus influence patient safety outcomes. Objective: The purpose of this …
patients and thus influence patient safety outcomes. Objective: The purpose of this …
Machine learning techniques for the diagnosis of Alzheimer's disease: A review
Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the
elderly population. Efficient automated techniques are needed for early diagnosis of …
elderly population. Efficient automated techniques are needed for early diagnosis of …
Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor
problems for people with a detrimental effect on the functioning of the nervous system. In …
problems for people with a detrimental effect on the functioning of the nervous system. In …
Sample-size determination methodologies for machine learning in medical imaging research: a systematic review
I Balki, A Amirabadi, J Levman… - Canadian …, 2019 - journals.sagepub.com
Purpose The required training sample size for a particular machine learning (ML) model
applied to medical imaging data is often unknown. The purpose of this study was to provide …
applied to medical imaging data is often unknown. The purpose of this study was to provide …
Early diagnosis of Alzheimer's disease based on deep learning: A systematic review
Background The improvement of health indicators and life expectancy, especially in
developed countries, has led to population growth and increased age-related diseases …
developed countries, has led to population growth and increased age-related diseases …
Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques
Neurodegenerative diseases have shown an increasing incidence in the older population in
recent years. A significant amount of research has been conducted to characterize these …
recent years. A significant amount of research has been conducted to characterize these …
Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach
VS Diogo, HA Ferreira, D Prata… - Alzheimer's Research & …, 2022 - Springer
Background Early and accurate diagnosis of Alzheimer's disease (AD) is essential for
disease management and therapeutic choices that can delay disease progression. Machine …
disease management and therapeutic choices that can delay disease progression. Machine …
Imaging biomarkers in neurodegeneration: current and future practices
PNE Young, M Estarellas, E Coomans… - Alzheimer's research & …, 2020 - Springer
There is an increasing role for biological markers (biomarkers) in the understanding and
diagnosis of neurodegenerative disorders. The application of imaging biomarkers …
diagnosis of neurodegenerative disorders. The application of imaging biomarkers …
Classification of Alzheimer's disease using ensemble of deep neural networks trained through transfer learning
Alzheimer's disease (AD) is one of the deadliest neurodegenerative diseases ailing the
elderly population all over the world. An ensemble of Deep learning (DL) models can learn …
elderly population all over the world. An ensemble of Deep learning (DL) models can learn …