AI Medical Compendium Journal:
Journal of medical imaging and radiation oncology

Showing 1 to 10 of 28 articles

Comparative Performance of Anthropic Claude and OpenAI GPT Models in Basic Radiological Imaging Tasks.

Journal of medical imaging and radiation oncology
BACKGROUND: Publicly available artificial intelligence (AI) Vision Language Models (VLMs) are constantly improving. The advent of vision capabilities on these models could enhance radiology workflows. Evaluating their performance in radiological imag...

Clinical application of convolutional neural network lung nodule detection software: An Australian quaternary hospital experience.

Journal of medical imaging and radiation oncology
INTRODUCTION: Early-stage lung cancer diagnosis through detection of nodules on computed tomography (CT) remains integral to patient survivorship, promoting national screening programmes and diagnostic tools using artificial intelligence (AI) convolu...

Evaluation of deep-learning TSE images in clinical musculoskeletal imaging.

Journal of medical imaging and radiation oncology
In this study, we compared the fat-saturated (FS) and non-FS turbo spin echo (TSE) magnetic resonance imaging knee sequences reconstructed conventionally (conventional-TSE) against a deep learning-based reconstruction of accelerated TSE (DL-TSE) scan...

Deep learning denoising reconstruction enables faster T2-weighted FLAIR sequence acquisition with satisfactory image quality.

Journal of medical imaging and radiation oncology
INTRODUCTION: Deep learning reconstruction (DLR) technologies are the latest methods attempting to solve the enduring problem of reducing MRI acquisition times without compromising image quality. The clinical utility of this reconstruction technique ...

Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Journal of medical imaging and radiation oncology
Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing ...

Current applications of algorithmic artificial intelligence in interventional radiology: A review of the literature.

Journal of medical imaging and radiation oncology
Artificial intelligence is a rapidly evolving area of technology whose integration into healthcare delivery infrastructure is predicted to have profound implications for medicine delivery in the 21st century. Artificial intelligence as it relates to ...

Imaging in interventional oncology, the better you see, the better you treat.

Journal of medical imaging and radiation oncology
Imaging and image processing is the fundamental pillar of interventional oncology in which diagnostic, procedure planning, treatment and follow-up are sustained. Knowing all the possibilities that the different image modalities can offer is capital t...

Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer.

Journal of medical imaging and radiation oncology
INTRODUCTION: Lymph node (LN) metastases are an important determinant of survival in patients with colon cancer, but remain difficult to accurately diagnose on preoperative imaging. This study aimed to develop and evaluate a deep learning model to pr...

MRI-based deep learning techniques for the prediction of isocitrate dehydrogenase and 1p/19q status in grade 2-4 adult gliomas.

Journal of medical imaging and radiation oncology
Molecular biomarkers are becoming increasingly important in the classification of intracranial gliomas. While tissue sampling remains the gold standard, there is growing interest in the use of deep learning (DL) techniques to predict these markers. T...

Automated pneumothorax triaging in chest X-rays in the New Zealand population using deep-learning algorithms.

Journal of medical imaging and radiation oncology
INTRODUCTION: The primary aim was to develop convolutional neural network (CNN)-based artificial intelligence (AI) models for pneumothorax classification and segmentation for automated chest X-ray (CXR) triaging. A secondary aim was to perform interp...