A new task force dedicated to artificial intelligence (AI) with respect to paediatric radiology was created in 2021 at the International Paediatric Radiology (IPR) meeting in Rome, Italy (a joint society meeting by the European Society of Pediatric R...
PURPOSE: To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation.
AJR. American journal of roentgenology
Jun 15, 2022
Deep learning-based convolutional neural networks have enabled major advances in development of artificial intelligence (AI) software applications. Modern AI applications offer comprehensive multiorgan evaluation. The purpose of this article was to...
The value of artificial intelligence (AI) in healthcare has become evident, especially in the field of medical imaging. The accelerated pace and acuity of care in the Emergency Department (ED) has made it a popular target for artificial intelligence-...
OBJECTIVES: Accurate evaluation of bowel fibrosis in patients with Crohn's disease (CD) remains challenging. Computed tomography enterography (CTE)-based radiomics enables the assessment of bowel fibrosis; however, it has some deficiencies. We aimed ...
AJR. American journal of roentgenology
May 11, 2022
Interest in artificial intelligence (AI) applications for lung nodules continues to grow among radiologists, particularly with the expanding eligibility criteria and clinical utilization of lung cancer screening CT. AI has been heavily investigated f...
Despite the wide availability of ultrasound machines for hepatocellular carcinoma surveillance, an inadequate number of expert radiologists performing ultrasounds in remote areas remains a primary barrier for surveillance. We demonstrated feasibility...
PURPOSE: To compare the diagnostic performance of a deep learning (DL) model with that of musculoskeletal physicians and radiologists for detecting bone marrow edema on dual-energy CT (DECT).
PURPOSE: To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions.