Related papers: The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohorts

The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohorts

URL: http://arxiv.org/abs/2301.08719v1
Date: Fri, 20 Jan 2023 18:31:22 GMT
Title: The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohorts
Authors: A Badano, M Lago, E Sizikova, JG Delfino, S Guan, MA Anastasio and B Sahiner
Abstract summary: In silico imaging trials are computational studies that seek to ascertain the performance of a medical device. The benefits of in silico trials for evaluating new technology include significant resource and time savings. To conduct in silico trials, digital representations of humans are needed.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Randomized clinical trials, while often viewed as the highest evidentiary bar by which to judge the quality of a medical intervention, are far from perfect. In silico imaging trials are computational studies that seek to ascertain the performance of a medical device by collecting this information entirely via computer simulations. The benefits of in silico trials for evaluating new technology include significant resource and time savings, minimization of subject risk, the ability to study devices that are not achievable in the physical world, allow for the rapid and effective investigation of new technologies and ensure representation from all relevant subgroups. To conduct in silico trials, digital representations of humans are needed. We review the latest developments in methods and tools for obtaining digital humans for in silico imaging studies. First, we introduce terminology and a classification of digital human models. Second, we survey available methodologies for generating digital humans with healthy and diseased status and examine briefly the role of augmentation methods. Finally, we discuss the trade-offs of four approaches for sampling digital cohorts and the associated potential for study bias with selecting specific patient distributions.

Related papers

Patient-centered data science: an integrative framework for evaluating and predicting clinical outcomes in the digital health era [0.0]
This study proposes a novel, integrative framework for patient-centered data science in the digital health era. We developed a multidimensional model that combines traditional clinical data with patient-reported outcomes, social determinants of health, and multi-omic data to create comprehensive digital patient representations.
arXiv Detail & Related papers (2024-07-31T02:36:17Z)
Artificial Intelligence-based Decision Support Systems for Precision and Digital Health [0.49109372384514843]
We discuss the opportunity offered by AI, more specifically reinforcement learning, to current trends in healthcare. We focus on the area of adaptive interventions. This article has undergone anonymous review and is intended as a book chapter for the volume "Frontiers of Statistics and Data Science"
arXiv Detail & Related papers (2024-07-22T21:39:34Z)
TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
arXiv Detail & Related papers (2024-06-30T09:13:10Z)
A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
arXiv Detail & Related papers (2024-05-21T06:44:40Z)
Data Augmentation in Human-Centric Vision [54.97327269866757]
This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks. It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection. Our work categorizes data augmentation methods into two main types: data generation and data perturbation.
arXiv Detail & Related papers (2024-03-13T16:05:18Z)
A No-Reference Quality Assessment Method for Digital Human Head [56.17852258306602]
We develop a novel no-reference (NR) method based on Transformer to deal with digital human quality assessment (DHQA) Specifically, the front 2D projections of the digital humans are rendered as inputs and the vision transformer (ViT) is employed for the feature extraction. Then we design a multi-task module to jointly classify the distortion types and predict the perceptual quality levels of digital humans.
arXiv Detail & Related papers (2023-10-25T16:01:05Z)
Respiratory Disease Classification and Biometric Analysis Using Biosignals from Digital Stethoscopes [3.2458203725405976]
This work presents a novel approach leveraging digital stethoscope technology for automatic respiratory disease classification and biometric analysis. By leveraging one of the largest publicly available medical database of respiratory sounds, we train machine learning models to classify various respiratory health conditions. Our approach achieves high accuracy in both binary classification (89% balanced accuracy for healthy vs. diseased) and multi-class classification (72% balanced accuracy for specific diseases like pneumonia and COPD)
arXiv Detail & Related papers (2023-09-12T23:54:00Z)
Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
arXiv Detail & Related papers (2023-05-25T16:29:16Z)
SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments [12.019996672009375]
We show that creating realistic simulated images from human models is a viable alternative to large-scale in situ data collection. Because synthetic generation of training data from human-based models scales easily, we find that our model transfer paradigm for X-ray image analysis, which we refer to as SyntheX, can even outperform real data-trained models.
arXiv Detail & Related papers (2022-06-13T13:08:41Z)
Computer-Assisted Analysis of Biomedical Images [1.0116577992023341]
This thesis aims at proposing novel and advanced computer-assisted methods for biomedical image analysis. The ultimate goal of these research studies is to gain clinically and biologically useful insights that can guide differential diagnosis and therapies.
arXiv Detail & Related papers (2021-06-04T21:59:48Z)
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)

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