Machine Learning-based Biological Ageing Estimation Technologies: A Survey

• URL: http://arxiv.org/abs/2206.12650v1
• Date: Sat, 25 Jun 2022 13:38:39 GMT
• Title: Machine Learning-based Biological Ageing Estimation Technologies: A Survey
• Authors: Zhaonian Zhang, Richard Jiang, Danny Crookes and Paul Chazot
• Abstract summary: We will mainly review three age prediction methods by using machine learning (ML) They are based on blood biomarkers, facial images, and structural features. The prediction accuracy is not very good, which cannot make a great contribution to the medical field.
• Score: 2.9554549423413303
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, there are various methods of estimating Biological Age (BA) have been developed. Especially with the development of machine learning (ML), there are more and more types of BA predictions, and the accuracy has been greatly improved. The models for the estimation of BA play an important role in monitoring healthy aging, and could provide new tools to detect health status in the general population and give warnings to sub-healthy people. We will mainly review three age prediction methods by using ML. They are based on blood biomarkers, facial images, and structural neuroimaging features. For now, the model using blood biomarkers is the simplest, most direct, and most accurate method. The face image method is affected by various aspects such as race, environment, etc., the prediction accuracy is not very good, which cannot make a great contribution to the medical field. In summary, we are here to track the way forward in the era of big data for us and other potential general populations and show ways to leverage the vast amounts of data available today.

Related papers

• Lifestyle-Informed Personalized Blood Biomarker Prediction via Novel Representation Learning [7.845988771273181]
  We introduce a novel framework for predicting future blood biomarker values. Our proposed method learns a similarity-based embedding space that captures the complex relationship between biomarkers and lifestyle factors.
  arXiv  Detail & Related papers  (2024-07-09T23:52:53Z)
• Human Health Indicator Prediction from Gait Video [34.24448186464565]
  We propose to employ gait videos to predict health indicators, which are more prevalent in surveillance and home monitoring scenarios. To better suit the health indicator prediction task, we bring forward Global-Local Aware aNdsymmetric Centro (GLANCE) module. Experiments demonstrate that the proposed paradigm achieves state-of-the-art results for predicting health indicators on MoVi.
  arXiv  Detail & Related papers  (2022-12-25T19:10:37Z)
• Neurodevelopmental Phenotype Prediction: A State-of-the-Art Deep Learning Model [0.0]
  We apply a deep neural network to analyse the cortical surface data of neonates. Our goal is to identify neurodevelopmental biomarkers and to predict gestational age at birth based on these biomarkers.
  arXiv  Detail & Related papers  (2022-11-16T11:15:23Z)
• Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders [55.41644538483948]
  Automated classification and similarity retrieval aid physicians in decision-making to diagnose possible genetic conditions as early as possible. Previous work has addressed the problem as a classification problem and used deep learning methods. In this study, we used a facial recognition model trained on a large corpus of healthy individuals as a pre-task and transferred it to facial phenotype recognition.
  arXiv  Detail & Related papers  (2022-10-23T11:52:57Z)
• Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction [102.23901690661916]
  Low-dimensional embedding spaces can be derived from the UK Biobank population dataset to enhance data-scarce prediction of health indicators, lifestyle and demographic characteristics. Performances gains from semisupervison approaches will probably become an important ingredient for various medical data science applications.
  arXiv  Detail & Related papers  (2021-10-12T16:25:50Z)
• Voxel-level Importance Maps for Interpretable Brain Age Estimation [70.5330922395729]
  We focus on the task of brain age regression from 3D brain Magnetic Resonance (MR) images using a Convolutional Neural Network, termed prediction model. We implement a noise model which aims to add as much noise as possible to the input without harming the performance of the prediction model. We test our method on 13,750 3D brain MR images from the UK Biobank, and our findings are consistent with the existing neuropathology literature.
  arXiv  Detail & Related papers  (2021-08-11T18:08:09Z)
• Evaluating the performance of personal, social, health-related, biomarker and genetic data for predicting an individuals future health using machine learning: A longitudinal analysis [0.0]
  The aim of the study is to apply a machine learning approach to identify the relative contribution of personal, social, health-related, biomarker and genetic data as predictors of future health in individuals. Two machine learning approaches were used to build predictive models: deep learning via neural networks and XGBoost. Results found that health-related measures had the strongest prediction of future health status, with genetic data performing poorly.
  arXiv  Detail & Related papers  (2021-04-26T12:31:40Z)
• Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition [65.77962788209103]
  We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge. It enforces the model to focus on learning the subset-specific knowledge. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
  arXiv  Detail & Related papers  (2021-04-22T13:39:33Z)
• Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation [18.503467872057424]
  The concept of biological age (BA) is hard to grasp mainly due to the lack of a clearly defined reference standard. We propose a new imaging-based framework for organ-specific BA estimation.
  arXiv  Detail & Related papers  (2020-09-22T19:04:02Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Patch-based Brain Age Estimation from MR Images [64.66978138243083]
  Brain age estimation from Magnetic Resonance Images (MRI) derives the difference between a subject's biological brain age and their chronological age. Early detection of neurodegeneration manifesting as a higher brain age can potentially facilitate better medical care and planning for affected individuals. We develop a new deep learning approach that uses 3D patches of the brain as well as convolutional neural networks (CNNs) to develop a localised brain age estimator.
  arXiv  Detail & Related papers  (2020-08-29T11:50:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.