Researchers have developed an artificial intelligence (AI)-based brain age prediction model to quantify deviations from a healthy brain-aging trajectory in patients with mild cognitive impairment, according to a study in Radiology: Artificial Intelligence. Amnestic mild cognitive impairment (aMCI) is a transition phase from normal aging to Alzheimer's disease (AD). People with aMCI have memory deficits that are more serious than normal for their age and education, but not severe enough to affect daily function. For the study, Ni Shu, PhD, from State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, in Beijing, and colleagues used a machine learning approach to train a brain age prediction model based on the T1-weighted MR images of 974 healthy adults aged from 49.3 to 95.4 years. The trained model was applied to estimate the predicted age difference (predicted age vs. actual age) of aMCI patients in the Beijing Aging Brain Rejuvenation Initiative (616 healthy controls and 80 aMCI patients) and the Alzheimer's Disease Neuroimaging Initiative (589 healthy controls and 144 aMCI patients) datasets.
Researchers have developed an artificial intelligence (AI)-based brain age prediction model to quantify deviations from a healthy brain-aging trajectory in patients with mild cognitive impairment, according to a study published in Radiology: Artificial Intelligence. The model has the potential to aid in early detection of cognitive impairment at an individual level. Amnestic mild cognitive impairment (aMCI) is a transition phase from normal aging to Alzheimer's disease (AD). People with aMCI have memory deficits that are more serious than normal for their age and education, but not severe enough to affect daily function. For the study, Ni Shu, Ph.D., from State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, in Beijing, China, and colleagues used a machine learning approach to train a brain age prediction model based on the T1-weighted MR images of 974 healthy adults aged from 49.3 to 95.4 years.
Traditional supervised learning with deep neural networks requires a tremendous amount of labelled data to converge to a good solution. For 3D medical images, it is often impractical to build a large homogeneous annotated dataset for a specific pathology. Self-supervised methods offer a new way to learn a representation of the images in an unsupervised manner with a neural network. In particular, contrastive learning has shown great promises by (almost) matching the performance of fully-supervised CNN on vision tasks. Nonetheless, this method does not take advantage of available meta-data, such as participant's age, viewed as prior knowledge. Here, we propose to leverage continuous proxy metadata, in the contrastive learning framework, by introducing a new loss called y-Aware InfoNCE loss. Specifically, we improve the positive sampling during pre-training by adding more positive examples with similar proxy meta-data with the anchor, assuming they share similar discriminative semantic features.With our method, a 3D CNN model pre-trained on $10^4$ multi-site healthy brain MRI scans can extract relevant features for three classification tasks: schizophrenia, bipolar diagnosis and Alzheimer's detection. When fine-tuned, it also outperforms 3D CNN trained from scratch on these tasks, as well as state-of-the-art self-supervised methods. Our code is made publicly available here.
Blockwise missing data occurs frequently when we integrate multisource or multimodality data where different sources or modalities contain complementary information. In this paper, we consider a high-dimensional linear regression model with blockwise missing covariates and a partially observed response variable. Under this semi-supervised framework, we propose a computationally efficient estimator for the regression coefficient vector based on carefully constructed unbiased estimating equations and a multiple blockwise imputation procedure, and obtain its rates of convergence. Furthermore, building upon an innovative semi-supervised projected estimating equation technique that intrinsically achieves bias-correction of the initial estimator, we propose nearly unbiased estimators for the individual regression coefficients that are asymptotically normally distributed under mild conditions. By carefully analyzing these debiased estimators, asymptotically valid confidence intervals and statistical tests about each regression coefficient are constructed. Numerical studies and application analysis of the Alzheimer's Disease Neuroimaging Initiative data show that the proposed method performs better and benefits more from unsupervised samples than existing methods.
Powerful algorithms used by Netflix, Amazon and Facebook can'predict' the biological language of cancer and neurodegenerative diseases like Alzheimer's, scientists have found. Big data produced during decades of research was fed into a computer language model to see if artificial intelligence can make more advanced discoveries than humans. Academics based at St John's College, University of Cambridge, found the machine-learning technology could decipher the'biological language' of cancer, Alzheimer's, and other neurodegenerative diseases. Their ground-breaking study has been published in the scientific journal PNAS today (April 8 2021) and could be used in the future to'correct the grammatical mistakes inside cells that cause disease'. Professor Tuomas Knowles, lead author of the paper and a Fellow at St John's College, said: "Bringing machine-learning technology into research into neurodegenerative diseases and cancer is an absolute game-changer. Ultimately, the aim will be to use artificial intelligence to develop targeted drugs to dramatically ease symptoms or to prevent dementia happening at all."
An artificial intelligence-based analysis of epigenetic patterns in blood samples might be able to identify people with Alzheimer's disease, a new study has found. Alzheimer's disease affects nearly 47 million people around the world but can be difficult to diagnose, particularly in its early stages when therapeutic interventions might have the greatest effect. "Drugs used in the late stage of the disease do not seem make much difference, so there is a tremendous amount of interest in diagnosis in the early stages of the disease," Khaled Imam, director of geriatric medicine at Beaumont Health and a co-author of the new study, said in a statement. Imam added that "blood is thought to be a desirable way of approaching this. And it would be relatively cheap and minimally invasive as compared to an MRI or spinal tap."
Paul De Sousa, head of life sciences at Massive Analytic and former researcher at Edinburgh University, writes about a study using artificial precognition AI to analyse results of protein biomarker tests associated with Alzheimer's disease progression. Accounting for over 30 million Disability Adjusted Life Years worldwide, Alzheimer's disease (AD) is a global societal challenge and a threat to healthcare systems around the world. A long history of failures of AD drug trials has highlighted the need for early detection and diagnosis to support patients and clinicians to implement the best life adjustments or medical interventions to alter the course of the disease and personalise the care of those at risk. Biomarkers are measurable indicators of the biological conditions of health, on which disease prognosis and diagnosis is founded. In AD there are a range of diagnostic procedures to detect these biomarkers including testing Cerebrospinal fluid (CSF) and PET scans for markers of amyloid-β and tau that can accurately detect AD pathology, but their cost and invasive nature preclude the broad accessibility required for early detection.
When it comes to neurodegenerative diseases, Alzheimer's disease is considered one of the worst. It promotes the onset of dementia--an irreversible decline in thinking, memory, and ability to perform simple everyday tasks--in around 60 to 70 percent of patients. Now, researchers have suggested that the disease can be divided into four distinct subtypes; potentially opening doors for individualized treatment among sufferers. In an international study, scientists illustrated that tau--proteins found in neurons that are associated with neurodegenerative conditions--spread through the brain in four distinct patterns. This leads to varied symptoms and outcomes among affected individuals. Machine learning (ML) was leveraged by the authors to distinguish between the different subtypes.
Twin support vector machine (TSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of TSVM and then focus on the various improvements and applications of TSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.
In this paper, we propose a new method to perform data augmentation in a reliable way in the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based variational autoencoder. Our approach combines a proper latent space modeling of the VAE seen as a Riemannian manifold with a new generation scheme which produces more meaningful samples especially in the context of small data sets. The proposed method is tested through a wide experimental study where its robustness to data sets, classifiers and training samples size is stressed. It is also validated on a medical imaging classification task on the challenging ADNI database where a small number of 3D brain MRIs are considered and augmented using the proposed VAE framework. In each case, the proposed method allows for a significant and reliable gain in the classification metrics. For instance, balanced accuracy jumps from 66.3% to 74.3% for a state-of-the-art CNN classifier trained with 50 MRIs of cognitively normal (CN) and 50 Alzheimer disease (AD) patients and from 77.7% to 86.3% when trained with 243 CN and 210 AD while improving greatly sensitivity and specificity metrics.