AI Medical Compendium Journal:
Clinical imaging

Showing 11 to 20 of 64 articles

Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review.

Clinical imaging
PURPOSE: There are many radiological datasets for breast cancer, some which have supported the development of AI medical devices for breast cancer screening and image classification. This review aims to identify mammography datasets (including digiti...

Cultivating diagnostic clarity: The importance of reporting artificial intelligence confidence levels in radiologic diagnoses.

Clinical imaging
Accurate image interpretation is essential in the field of radiology to the healthcare team in order to provide optimal patient care. This article discusses the use of artificial intelligence (AI) confidence levels to enhance the accuracy and dependa...

Diagnostic performance of an artificial intelligence model for the detection of pneumothorax at chest X-ray.

Clinical imaging
PURPOSE: Pneumothorax (PTX) is a common clinical urgency, its diagnosis is usually performed on chest radiography (CXR), and it presents a setting where artificial intelligence (AI) methods could find terrain in aiding radiologists in facing increasi...

Minimizing prostate diffusion weighted MRI examination time through deep learning reconstruction.

Clinical imaging
PURPOSE: To study the diagnostic image quality of high b-value diffusion weighted images (DWI) derived from standard and variably reduced datasets reconstructed with a commercially available deep learning reconstruction (DLR) algorithm.

Automated classification of Alzheimer's disease, mild cognitive impairment, and cognitively normal patients using 3D convolutional neural network and radiomic features from T1-weighted brain MRI: A comparative study on detection accuracy.

Clinical imaging
OBJECTIVES: Alzheimer's disease (AD) is a common neurodegenerative disorder that primarily affects older individuals. Due to its high incidence, an accurate and efficient stratification system could greatly aid in the clinical diagnosis and prognosis...

AI implementation: Radiologists' perspectives on AI-enabled opportunistic CT screening.

Clinical imaging
OBJECTIVE: AI adoption requires perceived value by end-users. AI-enabled opportunistic CT screening (OS) detects incidental clinically meaningful imaging risk markers on CT for potential preventative health benefit. This investigation assesses radiol...

Can large language models be new supportive tools in coronary computed tomography angiography reporting?

Clinical imaging
The advent of large language models (LLMs) marks a transformative leap in natural language processing, offering unprecedented potential in radiology, particularly in enhancing the accuracy and efficiency of coronary artery disease (CAD) diagnosis. Wh...

The impact of high-order features on performance of radiomics studies in CT non-small cell lung cancer.

Clinical imaging
High-order radiomic features have been shown to produce high performance models in a variety of scenarios. However, models trained without high-order features have shown similar performance, raising the question of whether high-order features are wor...

Performance and clinical utility of an artificial intelligence-enabled tool for pulmonary embolism detection.

Clinical imaging
PURPOSE: Diagnosing pulmonary embolism (PE) is still challenging due to other conditions that can mimic its appearance, leading to incomplete or delayed management and several inter-observer variabilities. This study evaluated the performance and cli...