Collaborating Authors

New AI tool can predict Alzheimer's risk


Toronto: A team of scientists, including one of an Indian-origin, has successfully trained a new Artificial Intelligence (AI) algorithm that may soon help doctors to make accurate predictions regarding cognitive decline leading to Alzheimer's disease and provide intervention. The team, from the McGill University in Canada, designed an algorithm that learns signatures from magnetic resonance imaging (MRI), genetics, and clinical data. This specific algorithm can help predict whether an individual's cognitive faculties are likely to deteriorate towards Alzheimer's in the next five years. "At the moment, there are limited ways to treat Alzheimer's and the best evidence we have is for prevention. Our AI methodology could have significant implications as a'doctor's assistant' that would help stream people onto the right pathway for treatment," Mallar Chakravarty, assistant professor at the University's Department of Psychiatry.

High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer's Disease Progression Prediction

Neural Information Processing Systems

Alzheimer disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Regression analysis has been studied to relate neuroimaging measures to cognitive status. However, whether these measures have further predictive power to infer a trajectory of cognitive performance over time is still an under-explored but important topic in AD research. We propose a novel high-order multi-task learning model to address this issue. The proposed model explores the temporal correlations existing in data features and regression tasks by the structured sparsity-inducing norms.

The Overview of Artificial Intelligence in Medicine


There are currently developed algorithms for fractures and malignancies detection based on X-ray, CT and MRI image recognition. Also, there are a number of commercialized AI algorithms for predicting injury patterns and predicting postoperative complications following orthopedic and trauma procedures. This is one of the medical fields with the most abundant AI implementation. AI algorithms are being used for suicide prediction and for depression and anxiety treatment, a feature performed by chatbots. Used for early diagnostics of chronic diseases such as Multiple sclerosis, Alzheimer's disease, and Parkinson's disease, and for a number of acute neurological diseases such as brain tissue ischemia, intracranial hemorrhage, and hydrocephalus.

IBM and Pfizer claim AI can predict Alzheimer's onset with 71% accuracy


Pfizer and IBM researchers claim to have developed a machine learning technique that can predict Alzheimer's disease years before symptoms develop. By analyzing small samples of language data obtained from clinical verbal tests, the team says their approach achieved 71% accuracy when tested against a group of cognitively healthy people. Alzheimer's disease begins with vague, often misinterpreted signs of mild memory loss followed by a slow, progressively serious decline in cognitive ability and quality of life. According to the nonprofit Alzheimer's Association, more than 5 million Americans of all ages have Alzheimer's, and every state is expected to see at least a 14% rise in the prevalence of Alzheimer's between 2017 and 2025. Due to the nature of Alzheimer's disease and how it takes hold in the brain, it's likely that the best way to delay its onset is through early intervention.

MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks Machine Learning

Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in survival analysis are often constrained by strong parametric assumptions and limited in their ability to learn from high-dimensional data, while existing neural network models are not readily-adapted to the longitudinal setting. This paper develops a novel convolutional approach that addresses these drawbacks. We present MATCH-Net: a Missingness-Aware Temporal Convolutional Hitting-time Network, designed to capture temporal dependencies and heterogeneous interactions in covariate trajectories and patterns of missingness. To the best of our knowledge, this is the first investigation of temporal convolutions in the context of dynamic prediction for personalized risk prognosis. Using real-world data from the Alzheimer's Disease Neuroimaging Initiative, we demonstrate state-of-the-art performance without making any assumptions regarding underlying longitudinal or time-to-event processes attesting to the model's potential utility in clinical decision support.