AIMC Topic: Radiography, Thoracic
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A New Benchmark: Clinical Uncertainty and Severity Aware Labeled Chest X-Ray Images With Multi-Relationship Graph Learning.
IEEE transactions on medical imaging
Jan 2, 2025
Chest radiography, commonly known as CXR, is frequently utilized in clinical settings to detect cardiopulmonary conditions. However, even seasoned radiologists might offer different evaluations regarding the seriousness and uncertainty associated wit...
Weakly Supervised Object Detection in Chest X-Rays With Differentiable ROI Proposal Networks and Soft ROI Pooling.
IEEE transactions on medical imaging
Jan 2, 2025
Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision. The successes of multiple instance learning in this task for natural images, howev...
Generalizability, robustness, and correction bias of segmentations of thoracic organs at risk in CT images.
European radiology
Dec 31, 2024
OBJECTIVE: This study aims to assess and compare two state-of-the-art deep learning approaches for segmenting four thoracic organs at risk (OAR)-the esophagus, trachea, heart, and aorta-in CT images in the context of radiotherapy planning.
Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points.
Scientific reports
Dec 28, 2024
The purpose of this study was to evaluate whether the optimal operating points of adult-oriented artificial intelligence (AI) software differ for pediatric chest radiographs and to assess its diagnostic performance. Chest radiographs from patients un...
Consensus Between Radiologists, Specialists in Internal Medicine, and AI Software on Chest X-Rays in a Hospital-at-Home Service: Prospective Observational Study.
JMIR formative research
Dec 24, 2024
BACKGROUND: Home hospitalization is a care modality growing in popularity worldwide. Telemedicine-driven hospital-at-home (HAH) services could replace traditional hospital departments for selected patients. Chest x-rays typically serve as a key diagn...
Automated measurement of cardiothoracic ratio based on semantic segmentation integration model using deep learning.
Medical & biological engineering & computing
Dec 21, 2024
The objective of this study is to investigate the efficacy of the semantic segmentation model in predicting cardiothoracic ratio (CTR) and heart enlargement and compare its consistency with the reference standard. A total of 650 consecutive chest rad...
AI-assisted detection for chest X-rays (AID-CXR): a multi-reader multi-case study protocol.
BMJ open
Dec 20, 2024
INTRODUCTION: A chest X-ray (CXR) is the most common imaging investigation performed worldwide. Advances in machine learning and computer vision technologies have led to the development of several artificial intelligence (AI) tools to detect abnormal...
Diagnostic Accuracy of Artificial Intelligence-Based Chest X-Ray reading for screening of Tuberculosis.
Journal of Nepal Health Research Council
Dec 19, 2024
BACKGROUND: Tuberculosis remains a public health challenge in Nepal and ranks as the seventh leading cause of death in the country. The END Tuberculosis strategy stresses - the screening for symptoms alone may not suffice; additional screening tools ...
Comparative study of DCNN and image processing based classification of chest X-rays for identification of COVID-19 patients using fine-tuning.
Journal of medical engineering & technology
Dec 9, 2024
The conventional detection of COVID-19 by evaluating the CT scan images is tiresome, often experiences high inter-observer variability and uncertainty issues. This work proposes the automatic detection and classification of COVID-19 by analysing the ...
Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study.
JMIR formative research
Dec 5, 2024
BACKGROUND: While deep learning classifiers have shown remarkable results in detecting chest X-ray (CXR) pathologies, their adoption in clinical settings is often hampered by the lack of transparency. To bridge this gap, this study introduces the neu...
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