AIMC Topic: Radiography, Thoracic
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Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset.
Medical image analysis
Feb 23, 2015
We present new pulmonary nodule segmentation algorithms for computed tomography (CT). These include a fully-automated (FA) system, a semi-automated (SA) system, and a hybrid system. Like most traditional systems, the new FA system requires only a sin...
Radiography, Thoracic
Radiographic Image Enhancement
Sensitivity and Specificity
Lung Neoplasms
Solitary Pulmonary Nodule
Radiographic Image Interpretation, Computer-Assisted
Regression Analysis
Humans
Pattern Recognition, Automated
Neural Networks, Computer
Databases, Factual
Algorithms
Data Interpretation, Statistical
Tomography, X-Ray Computed
Reproducibility of Results
Evaluating Artificial Intelligence and Traditional Learning Tools for Chest X-Ray Interpretation: A Descriptive Study.
The clinical teacher
Aug 1, 2025
BACKGROUND: Chest X-ray (CXR) interpretation is a fundamental yet challenging skill for medical students to master. Traditional resources like Radiopaedia offer extensive content, while newer artificial intelligence (AI) tools, such as Chester, provi...
Impact of Deep Learning-Based Image Conversion on Fully Automated Coronary Artery Calcium Scoring Using Thin-Slice, Sharp-Kernel, Non-Gated, Low-Dose Chest CT Scans: A Multi-Center Study.
Korean journal of radiology
Aug 1, 2025
OBJECTIVE: To evaluate the impact of deep learning-based image conversion on the accuracy of automated coronary artery calcium quantification using thin-slice, sharp-kernel, non-gated, low-dose chest computed tomography (LDCT) images collected from m...
Causal insights from clinical information in radiology: Enhancing future multimodal AI development.
Computer methods and programs in biomedicine
Aug 1, 2025
PURPOSE: This study investigates the causal mechanisms underlying radiology report generation by analyzing how clinical information and prior imaging examinations contribute to annotation shifts. We systematically estimate why and how biases manifest...
Prospective quality control in chest radiography based on the reconstructed 3D human body.
Physics in medicine and biology
Jul 8, 2025
Chest radiography requires effective quality control (QC) to reduce high retake rates. However, existing QC measures are all retrospective and implemented after exposure, often necessitating retakes when image quality fails to meet standards and ther...
Exploring best-performing radiomic features with combined multilevel discrete wavelet decompositions for multiclass COVID-19 classification using chest X-ray images.
Computers in biology and medicine
Jul 1, 2025
Discrete wavelet transforms have been applied in many machine learning models for the analysis of COVID-19; however, little is known about the impact of combined multilevel wavelet decompositions for the disease identification. This study proposes a ...
The Chest X- Ray: The Ship has Sailed, But Has It?
Journal of insurance medicine (New York, N.Y.)
Jul 1, 2025
In the past, the chest X-ray (CXR) was a traditional age and amount requirement used to assess potential mortality risk in life insurance applicants. It fell out of favor due to inconvenience to the applicant, cost, and lack of protective value. With...
Future Applications of Cardiothoracic CT.
Radiology
Jun 1, 2025
Radiologists are witnessing astonishing innovation and advancement of CT technologies and their clinical applications. This review highlights how photon-counting CT (PCCT), upright CT, and artificial intelligence (AI) may impact cardiothoracic CT app...
Assessing the diagnostic accuracy and prognostic utility of artificial intelligence detection and grading of coronary artery calcification on nongated computed tomography (CT) thorax.
Clinical radiology
Jun 1, 2025
AIMS: This study assessed the diagnostic accuracy and prognostic implications of an artificial intelligence (AI) tool for coronary artery calcification (CAC) assessment on nongated, noncontrast thoracic computed tomography (CT).
Pediatric chest X-ray diagnosis using neuromorphic models.
Computers in biology and medicine
Jun 1, 2025
This research presents an innovative neuromorphic method utilizing Spiking Neural Networks (SNNs) to analyze pediatric chest X-rays (PediCXR) to identify prevalent thoracic illnesses. We incorporate spiking-based machine learning models such as Spiki...
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