Related papers: Machine Learning-based Biological Ageing Estimatio…
Aging is a multidimensional process where phenotypes change at varying rates. Longitudinal studies of aging typically involve following a cohort of individuals over the course of several years. This design is hindered by cost, attrition,…
As global life expectancy increases, so does the burden of chronic diseases, yet individuals exhibit considerable variability in the rate at which they age. Identifying biomarkers that distinguish fast from slow ageing is crucial for…
Development and homeostasis in multicellular systems both require exquisite control over spatial molecular pattern formation and maintenance. Advances in spatially-resolved and high-throughput molecular imaging methods such as multiplexed…
This paper introduces a comprehensive model for age estimation, verification, and comparability, offering a comprehensive solution for a wide range of applications. It employs advanced learning techniques to understand age distribution and…
For more than two decades, advances in personalised medicine and precision healthcare have largely been based on genomics and other omics data. These strategies aim to tailor interventions to individual patient profiles, promising greater…
Human age estimation has attracted increasing researches due to its wide applicability in such as security monitoring and advertisement recommendation. Although a variety of methods have been proposed, most of them focus only on the…
Objectives: Age and gender estimation is crucial for various applications, including forensic investigations and anthropological studies. This research aims to develop a predictive system for age and gender estimation in living individuals,…
Multiple sclerosis is a disease that affects the brain and spinal cord, it can lead to severe disability and has no known cure. The majority of prior work in machine learning for multiple sclerosis has been centered around using Magnetic…
How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data) a multivariate auto-regressive function which given an image…
Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms. Increasing brain age with respect to chronological age can reflect increased vulnerability to neurodegeneration and cognitive…
Understanding the internal physiological changes accompanying the aging process is an important aspect of medical image interpretation, with the expected changes acting as a baseline when reporting abnormal findings. Deep learning has…
Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms. Increasing \textit{brain age gap} characterized by an elevated brain age relative to the chronological age can reflect…
Cardiovascular diseases are widespread among patients with chronic noncommunicable diseases and are one of the leading causes of death, including in the working age. The article presents the relevance of the development and application of…
Coronary Artery Disease (CAD) remains a leading cause of morbidity and mortality worldwide. Early detection is critical to recover patient outcomes and decrease healthcare costs. In recent years, machine learning (ML) advancements have…
In recent years, Artificial Intelligence (AI) has been widely used in medicine, particularly in the analysis of medical imaging, which has been driven by advances in computer vision and deep learning methods. This is particularly important…
For a medical diagnosis, health professionals use different kinds of pathological ways to make a decision for medical reports in terms of patients medical condition. In the modern era, because of the advantage of computers and technologies,…
Artificial Intelligence (AI) and Machine Learning (ML) are weaving their way into the fabric of society, where they are playing a crucial role in numerous facets of our lives. As we witness the increased deployment of AI and ML in various…
The present research tackles the difficulty of predicting osteoporosis risk via machine learning (ML) approaches, emphasizing the use of explainable artificial intelligence (XAI) to improve model transparency. Osteoporosis is a significant…
Chronological age of healthy people is able to be predicted accurately using deep neural networks from neuroimaging data, and the predicted brain age could serve as a biomarker for detecting aging-related diseases. In this paper, a novel 3D…
The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment. Clinical neuroscience and cancer imaging have been…