Virtual imaging trials improved the transparency and reliability of AI systems in COVID-19 imaging

• URL: http://arxiv.org/abs/2308.09730v2
• Date: Sun, 31 Mar 2024 19:28:25 GMT
• Title: Virtual imaging trials improved the transparency and reliability of AI systems in COVID-19 imaging
• Authors: Fakrul Islam Tushar, Lavsen Dahal, Saman Sotoudeh-Paima, Ehsan Abadi, W. Paul Segars, Ehsan Samei, Joseph Y. Lo,
• Abstract summary: The credibility of AI models in medical imaging is often challenged by issues and obscured clinical insights. We propose a virtual imaging trial framework, employing a diverse collection of medical images that are both clinical and simulated.
• Score: 1.6040478776985583
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The credibility of AI models in medical imaging is often challenged by reproducibility issues and obscured clinical insights, a reality highlighted during the COVID-19 pandemic by many reports of near-perfect artificial intelligence (AI) models that all failed to generalize. To address these concerns, we propose a virtual imaging trial framework, employing a diverse collection of medical images that are both clinical and simulated. In this study, COVID-19 serves as a case example to unveil the intrinsic and extrinsic factors influencing AI performance. Our findings underscore a significant impact of dataset characteristics on AI efficacy. Even when trained on large, diverse clinical datasets with thousands of patients, AI performance plummeted by up to 20% in generalization. However, virtual imaging trials offer a robust platform for objective assessment, unveiling nuanced insights into the relationships between patient- and physics-based factors and AI performance. For instance, disease extent markedly influenced AI efficacy, computed tomography (CT) out-performed chest radiography (CXR), while imaging dose exhibited minimal impact. Using COVID-19 as a case study, this virtual imaging trial study verified that radiology AI models often suffer from a reproducibility crisis. Virtual imaging trials not only offered a solution for objective performance assessment but also extracted several clinical insights. This study illuminates the path for leveraging virtual imaging to augment the reliability, transparency, and clinical relevance of AI in medical imaging.

Related papers

• Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography [0.0]
  We evaluate radiomics-based and deep learning-based approaches for disease detection in chest radiography.<n>Deep learning models learn directly from image data, while radiomics-based models extract handcrafted features.<n>These findings provide statistically validated, data-driven recommendations for model selection in diagnostic AI.
  arXiv  Detail & Related papers  (2025-04-16T16:54:37Z)
• Artificial Intelligence-Driven Prognostic Classification of COVID-19 Using Chest X-rays: A Deep Learning Approach [0.0]
  This study presents a high-accuracy deep learning model for classifying COVID-19 severity (Mild, Moderate, and Severe) using Chest X-ray images. Our model achieved an average accuracy of 97%, with specificity of 99%, sensitivity of 87%, and an F1-score of 93.11%. These results demonstrate the model's potential for real-world clinical applications.
  arXiv  Detail & Related papers  (2025-03-17T15:27:21Z)
• 2D and 3D Deep Learning Models for MRI-based Parkinson's Disease Classification: A Comparative Analysis of Convolutional Kolmogorov-Arnold Networks, Convolutional Neural Networks, and Graph Convolutional Networks [0.0]
  This study applies Convolutional Kolmogorov-Arnold Networks (ConvKANs) to Parkinson's Disease diagnosis. ConvKANs integrate learnable activation functions into convolutional layers, for PD classification using structural MRI. The first 3D implementation of ConvKANs for medical imaging is presented, comparing their performance to Convolutional Neural Networks (CNNs) and Graph Convolutional Networks (GCNs) These findings highlight ConvKANs' potential for PD detection, emphasize the importance of 3D analysis in capturing subtle brain changes, and underscore cross-dataset generalization challenges.
  arXiv  Detail & Related papers  (2024-07-24T16:04:18Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature Statistics [54.08757792080732]
  We propose integrating deep features from pre-trained visual models with a statistical analysis model to achieve opinion-unaware BIQA (OU-BIQA) Our proposed model exhibits superior consistency with human visual perception compared to state-of-the-art BIQA models.
  arXiv  Detail & Related papers  (2024-05-29T06:09:34Z)
• Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma [4.578027879885667]
  This research aims to improve glioblastoma survival prediction by integrating MR images, clinical and molecular-pathologic data in a transformer-based deep learning model. The model employs self-supervised learning techniques to effectively encode the high-dimensional MRI input for integration with non-imaging data using cross-attention.
  arXiv  Detail & Related papers  (2024-05-21T17:44:48Z)
• Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection [0.0]
  The objective is to leverage state-of-the-art convolutional neural networks (CNNs) on a large dataset of brain MRI scans for segmentation. The proposed methodology applies pre-processing techniques for enhanced performance and generalizability.
  arXiv  Detail & Related papers  (2024-04-06T15:09:49Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution [47.38982697349244]
  Dense regression is a widely used approach in computer vision for tasks such as image super-resolution, enhancement, depth estimation, etc. We propose incorporating active learning into dense regression models to address this problem. Active learning allows models to select the most informative samples for labeling, reducing the overall annotation cost while improving performance.
  arXiv  Detail & Related papers  (2023-05-27T16:33:43Z)
• Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection [2.359557447960552]
  Vision transformers (ViT) have emerged in recent years as an alternative to CNNs for several computer vision applications. We tested variants of the ViT architecture for a range of desired neuroimaging downstream tasks based on difficulty. We achieved a performance boost of 5% and 9-10% upon fine-tuning vision transformer models pre-trained on synthetic and real MRI scans.
  arXiv  Detail & Related papers  (2023-03-14T20:18:12Z)
• Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
  We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
  arXiv  Detail & Related papers  (2022-05-19T17:34:18Z)
• Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography [2.924350993741562]
  We created the CVIT-COVID dataset including 180 virtually imaged computed tomography (CT) images from simulated COVID-19 and normal phantom models. We evaluated the performance of an open-source, deep-learning model from the University of Waterloo trained with multi-institutional data. We validated the model's performance against open clinical data of 305 CT images to understand virtual vs. real clinical data performance.
  arXiv  Detail & Related papers  (2022-03-07T00:11:53Z)
• Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications [72.52228843498193]
  In training deep learning models, a compromise often must be made between performance and trust. In this work, we integrate a new surrogate loss with self-supervised learning for computer-aided screening of COVID-19 patients.
  arXiv  Detail & Related papers  (2021-12-14T21:16:52Z)
• Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence [79.038671794961]
  We launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK.
  arXiv  Detail & Related papers  (2021-11-18T00:43:41Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability [54.86482395312936]
  Deep learning (DL) classification models were trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We trained nine identical DL-based classification models by using combinations of the datasets with a 72% train, 8% validation, and 20% test data split. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better.
  arXiv  Detail & Related papers  (2021-02-18T21:14:52Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)

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.