Highlights
- Medical imaging provides high resolution ‘live’ information of the brain and allows the use of information related to the disease contained in the image.
- Imaging uses deep learning techniques to classify the functional and structural architecture of the brain.
- It helps to understand the neurodegenerative process involved in the development of the disease and also development of effective treatments.
New computer based diagnostic tool will help in quicker diagnosis of nuerodegenrative disorders.
Alzheimer's disease is the most common neurodegenerative disease in the elderly, which currently affects more than 40 million people.
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Alzheimers Disease
Alzheimer''s disease is a progressive neurodegenerative disease affecting memory and thinking and making the person increasingly dependent on others.
Alzheimer''s disease is a progressive neurodegenerative disease affecting memory and thinking and making the person increasingly dependent on others.
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‘Computer Aided Diagnosis forms an important tool for the early diagnosis of Alzheimer’s Disease thus allowing the application of treatments that can be simpler and effective.’
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There is no cure for this condition and early diagnosis is crucial for the treatment of the disease and to help in the development of new medicines.
Alzherimer's disease is closely linked to structural and functional changes in the brain.
The structural changes related to Alzheimer's disease occurs in the gray matter, which is responsible for processing information and the functional changes occurs in the white matter, which connects the different regions of the brain through fibers.
![Risk Factors for Alzheimers Disease]( "Risk Factors for Alzheimers Disease")
But in spite of the scientific advancement made, diagnosis and treatment still remains a challenge.
Using CAD to Understand Functional and Structural Changes in Alzheimer's Disease
Computer aided diagnosis (CAD) helps physicians to understand the content obtained in tests carried out in patients, which allows a simpler and more effective application of the treatment.
One such procedure is medical imaging, which provides high resolution "live" information on the subject matter and allows the use of information related to the disease contained in the image.
The BioSip research team Andrés Ortiz, Jorge Munilla, Juan Górriz and Javier Ramírez, belonging to the University of Malaga, in collaboration with a group of researchers from the University of Granada, has been studying biomedical images and signals for years.
The study helps in the diagnosis of Alzheimer's by the fusion of functional and structural images based on the use of the deep learning technique.
By automatically extracting the affected regions of interest, the artificial Intelligence (AI) technique will enable computers to differentiate the brain of a healthy person from that of an ill person.
The researchers explain, "the study uses deep learning techniques to calculate brain function predictors and magnetic resonance imaging to prevent Alzheimer's disease. To do this, we have used different neural networks with which to model each region of the brain to combine them afterwards".
Classification Based on Deep Learning Architectures
Deep Learning architectures is applied to brain regions as defined by the Automated Anatomical Labeling (AAL), a digital atlas of the human brain.
Based on the deep learning architectures, the study explores the construction of classification methods.
The images of gray matter from each brain area have been divided according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks.
This helps powerful classification architecture to automatically extract the most relevant features of a set of images. The proposed method has been evaluated using a large database from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
The results of this work show the potential of AI techniques to reveal patterns associated with the disease.
This method can be used to understand the neurodegenerative process involved in the development of the disease, besides being useful as a starting point for the development of more effective medical treatments.
The techniques developed may also help in improvement of accuracy in the diagnosis of other forms of dementia such as Parkinson's disease.
The study 'Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease' is published in International Journal Of Neural Systems.
Reference
Source: Medindia
The structural changes related to Alzheimer's disease occurs in the gray matter, which is responsible for processing information and the functional changes occurs in the white matter, which connects the different regions of the brain through fibers.
Risk Factors for Alzheimers Disease
Cognitively normal adults exhibiting atrophy of their temporal lobe or damage to blood vessels in the brain are more likely to develop Alzheimer's disease.
Cognitively normal adults exhibiting atrophy of their temporal lobe or damage to blood vessels in the brain are more likely to develop Alzheimer's disease.
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But in spite of the scientific advancement made, diagnosis and treatment still remains a challenge.
Using CAD to Understand Functional and Structural Changes in Alzheimer's Disease
Computer aided diagnosis (CAD) helps physicians to understand the content obtained in tests carried out in patients, which allows a simpler and more effective application of the treatment.
One such procedure is medical imaging, which provides high resolution "live" information on the subject matter and allows the use of information related to the disease contained in the image.
The BioSip research team Andrés Ortiz, Jorge Munilla, Juan Górriz and Javier Ramírez, belonging to the University of Malaga, in collaboration with a group of researchers from the University of Granada, has been studying biomedical images and signals for years.
The study helps in the diagnosis of Alzheimer's by the fusion of functional and structural images based on the use of the deep learning technique.
By automatically extracting the affected regions of interest, the artificial Intelligence (AI) technique will enable computers to differentiate the brain of a healthy person from that of an ill person.
The researchers explain, "the study uses deep learning techniques to calculate brain function predictors and magnetic resonance imaging to prevent Alzheimer's disease. To do this, we have used different neural networks with which to model each region of the brain to combine them afterwards".
Classification Based on Deep Learning Architectures
Deep Learning architectures is applied to brain regions as defined by the Automated Anatomical Labeling (AAL), a digital atlas of the human brain.
Based on the deep learning architectures, the study explores the construction of classification methods.
The images of gray matter from each brain area have been divided according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks.
This helps powerful classification architecture to automatically extract the most relevant features of a set of images. The proposed method has been evaluated using a large database from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
The results of this work show the potential of AI techniques to reveal patterns associated with the disease.
This method can be used to understand the neurodegenerative process involved in the development of the disease, besides being useful as a starting point for the development of more effective medical treatments.
The techniques developed may also help in improvement of accuracy in the diagnosis of other forms of dementia such as Parkinson's disease.
The study 'Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease' is published in International Journal Of Neural Systems.
Reference
- Andrés Ortiz et al. Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease. International Journal Of Neural Systems; (2017) doi.org/10.1142/S0129065716500258
Source: Medindia
Brain Imaging Helps Distinguish Between Depression and Dementia
Study evaluates if neuroimaging can distinguish persons with depression from those with dementia or both conditions.
Study evaluates if neuroimaging can distinguish persons with depression from those with dementia or both conditions.
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 Test Your Knowledge on Dementia Dementia is a leading cause of disability in older people. It is a condition where brain cells are permanently damaged or functionally impaired. Till 2015, about 47.5 million people suffer from dementia across the globe. There are various conditions that lead to dementia and it is important to get aware of its causes, effects and vulnerability. So how much do you know dementia? Take our advanced q | Advertisement
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