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Alzheimer's Disease


Opportunities for machine learning use in cystic fibrosis care

AIHub

Accurately predicting how an individual's chronic illness is going to progress is critical to delivering better-personalised, precision medicine. Yet there is an enormous challenge in accurately predicting the clinical trajectories of people for chronic health conditions such as cystic fibrosis (CF), cancer, cardiovascular disease and Alzheimer's disease. AI technology developed by the Cambridge Centre for AI in Medicine and their colleagues offers a glimpse of the future of precision medicine, and the predictive power which may be available to clinicians caring for individuals with the life-limiting condition cystic fibrosis. "Prediction problems in healthcare are fiendishly complex," said Professor Mihaela van der Schaar, Director of the Cambridge Centre for AI in Medicine (CCAIM). "Even machine learning approaches, which deal in complexity, struggle to deliver meaningful benefits to patients and clinicians, and to medical science more broadly. Off-the-shelf machine learning solutions, so useful in many areas, simply do not cut it in predictive medicine."


Biomechanical modelling of brain atrophy through deep learning

arXiv.org Machine Learning

We present a proof-of-concept, deep learning (DL) based, differentiable biomechanical model of realistic brain deformations. Using prescribed maps of local atrophy and growth as input, the network learns to deform images according to a Neo-Hookean model of tissue deformation. The tool is validated using longitudinal brain atrophy data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and we demonstrate that the trained model is capable of rapidly simulating new brain deformations with minimal residuals. This method has the potential to be used in data augmentation or for the exploration of different causal hypotheses reflecting brain growth and atrophy.


Health: Cheese and wine can REDUCE risk of Alzheimer's age-related cognitive decline, study shows

Daily Mail - Science & tech

Eating cheese regularly, lamb once a week and indulging in a daily glass of red wine can help stave off Alzheimer's and age-related cognitive decline, a study concluded. US researchers analysed the diet and cognitive powers of nearly 1,800 Britons over the period of a decade to identify foods that might have beneficial effects. They found that the best way to reduce the risk of dementia is via a healthy lifestyle -- and eating foods that increase the levels of proteins in the brain that protect it. In contrast, they warned that risk can be increased -- among those already susceptible to of Alzheimer's and cognitive decline -- by eating too much salt. Eating cheese regularly, lamb once a week and indulging in a daily glass of red wine can help to stave off age-related cognitive decline, a study has concluded.


EEG Plus Machine-learning Identifies Mild Cognitive Impairment...

#artificialintelligence

Using non-invasive, dry-electroencephalography (EEG) and machine-learning computer algorithms, researchers were able to distinguish between Parkinson's disease patients with and without mild cognitive impairments with 80% accuracy, early results of a study found. Follow-up assessments will be conducted after 12 months to validate the method's ability to predict cognitive impairment in this patient population. The results were presented in a poster, "The Identification of Mild Cognitive Impairment in Parkinson's disease using EEG and Machine Learning, "at the Alzheimer's Association International Conference 2020. The poster abstract was published in the journal Alzheimer's & Dementia. Cognitive impairment is a common symptom of Parkinson's disease characterized by difficulties in executive function, attention, vision, word-finding, and problems with learning and remembering information.


How artificial intelligence could ameliorate the diagnosis of patients with Alzheimer's

#artificialintelligence

A recent study released in Nature Reviews: Neurology found that Alzheimer's disease could be diagnosed faster and efficiently using artificial intelligence (AI). The study, conducted by the University of Sheffield, looks at the use of AI technologies, like machine learning, in healthcare to reduce the workflow and economic effects of traditional methods for detecting neurodegenerative diseases. In their study, the use of machine learning in assessing cognitive function was initiated in conjunction with biotech company BenevolentAI. The Sheffield team, along with BenevolentAI, demonstrated in their findings how machine learning algorithms could be efficient for the detection of the brain regions implicated before the onset of rapid cognitive decline or development of Alzheimer's. "Widespread implementation of AI technologies can help, for example, predict which patients showing mild cognitive impairment will go on to develop Alzheimer's disease, or how severely their motor skills will decline over time," said Laura Ferraiuolo, the study's lead author.


AI uses retinal scans to spot Alzheimer's - Futurity

#artificialintelligence

You are free to share this article under the Attribution 4.0 International license. A form of artificial intelligence designed to interpret a combination of retinal images successfully identified a group of patients known to have Alzheimer's disease, researchers report. The findings suggest the approach could one day be used as a predictive tool, according to the new study. The novel computer software looks at retinal structure and blood vessels on images of the inside of the eye that have been correlated with cognitive changes. The findings provide proof-of-concept that machine learning analysis of certain types of retinal images has the potential to offer a non-invasive way to detect Alzheimer's disease in symptomatic individuals.


AI Model Uses Retinal Scans to Predict Alzheimer's Disease

#artificialintelligence

The novel computer software looks at retinal structure and blood vessels on images of the inside of the eye that have been correlated with cognitive changes. The findings, appearing last week in the British Journal of Ophthalmology, provide proof-of-concept that machine learning analysis of certain types of retinal images has the potential to offer a non-invasive way to detect Alzheimer's disease in symptomatic individuals. "Diagnosing Alzheimer's disease often relies on symptoms and cognitive testing," said senior author Sharon Fekrat, M.D., retina specialist at the Duke Eye Center. "Additional tests to confirm the diagnosis are invasive, expensive, and carry some risk. Having a more accessible method to identify Alzheimer's could help patients in many ways, including improving diagnostic precision, allowing entry into clinical trials earlier in the disease course, and planning for necessary lifestyle adjustments."


AI model uses retinal scans to predict Alzheimer's disease

#artificialintelligence

A form of artificial intelligence designed to interpret a combination of retinal images was able to successfully identify a group of patients who were known to have Alzheimer's disease, suggesting the approach could one day be used as a predictive tool, according to an interdisciplinary study from Duke University. The novel computer software looks at retinal structure and blood vessels on images of the inside of the eye that have been correlated with cognitive changes. The findings, appearing last week in the British Journal of Ophthalmology, provide proof-of-concept that machine learning analysis of certain types of retinal images has the potential to offer a non-invasive way to detect Alzheimer's disease in symptomatic individuals. "Diagnosing Alzheimer's disease often relies on symptoms and cognitive testing," said senior author Sharon Fekrat, M.D., retina specialist at the Duke Eye Center. "Additional tests to confirm the diagnosis are invasive, expensive, and carry some risk. Having a more accessible method to identify Alzheimer's could help patients in many ways, including improving diagnostic precision, allowing entry into clinical trials earlier in the disease course, and planning for necessary lifestyle adjustments."


Dynamic Image for 3D MRI Image Alzheimer's Disease Classification

arXiv.org Artificial Intelligence

We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. We show our proposed CNN model achieves $9.5\%$ better Alzheimer's disease classification accuracy than the baseline 3D models. We also show that our method allows for efficient training, requiring only 20% of the training time compared to 3D CNN models. The code is available online: https://github.com/UkyVision/alzheimer-project.


Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems

arXiv.org Artificial Intelligence

Remarkable success of modern image-based AI methods and the resulting interest in their applications in critical decision-making processes has led to a surge in efforts to make such intelligent systems transparent and explainable. The need for explainable AI does not stem only from ethical and moral grounds but also from stricter legislation around the world mandating clear and justifiable explanations of any decision taken or assisted by AI. Especially in the medical context where Computer-Aided Diagnosis can have a direct influence on the treatment and well-being of patients, transparency is of utmost importance for safe transition from lab research to real world clinical practice. This paper provides a comprehensive overview of current state-of-the-art in explaining and interpreting Deep Learning based algorithms in applications of medical research and diagnosis of diseases. We discuss early achievements in development of explainable AI for validation of known disease criteria, exploration of new potential biomarkers, as well as methods for the subsequent correction of AI models. Various explanation methods like visual, textual, post-hoc, ante-hoc, local and global have been thoroughly and critically analyzed. Subsequently, we also highlight some of the remaining challenges that stand in the way of practical applications of AI as a clinical decision support tool and provide recommendations for the direction of future research.