Rapid advances in automated methods for extracting large numbers of quantitative features
from medical images have led to tremendous growth of publications reporting on radiomic …

Background: The use of artificial intelligence (AI) in health care has grown exponentially with
the promise of facilitating biomedical research and enhancing diagnosis, treatment …

Even though radiomics can hold great potential for supporting clinical decision-making, its
current use is mostly limited to academic research, without applications in routine clinical …

Minimizing bias is critical to adoption and implementation of machine learning (ML) in
clinical practice. Systematic mathematical biases produce consistent and reproducible …

Convolutional neural networks (CNN) can accurately predict chronological age in healthy
individuals from structural MRI brain scans. Potentially, these models could be applied …

Simple Summary Radiomics could increase the value of medical images for oncologic
patients, allowing for the identification of novel imaging biomarkers and building prediction …

Artificial intelligence (AI) and machine learning (ML) are increasingly used in radiology
research to deal with large and complex imaging data sets. Nowadays, ML tools have …

Objective This study developed a diagnostic tool combining machine learning (ML)
segmentation and radiomic texture analysis (RTA) for bone density screening using chest …

Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically
requiring human intelligence. Currently, AI is recognized in the broader technology radar …

Purpose To investigate the predictive performance of machine learning-based CT radiomics
for differentiating between low-and high-nuclear grade of clear cell renal cell carcinomas …