Radiology

Emergence of artificial intelligence for automating cone-beam computed tomography-derived maxillary sinus imaging tasks. A systematic review.

Cone-beam computed tomography (CBCT) imaging of the maxillary sinus is indispensable for implantolog...

A deep learning model for generating [F]FDG PET Images from early-phase [F]Florbetapir and [F]Flutemetamol PET images.

INTRODUCTION: Amyloid-β (Aβ) plaques is a significant hallmark of Alzheimer's disease (AD), detectab...

Deep learning-based correction for time truncation in cerebral computed tomography perfusion.

Cerebral computed tomography perfusion (CTP) imaging requires complete acquisition of contrast bolus...

Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art.

Colorectal cancer remains a major cause of cancer death and morbidity worldwide. Surgery is a major ...

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study.

BACKGROUND: Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prost...

Artificial intelligence solution to accelerate the acquisition of MRI images: Impact on the therapeutic care in oncology in radiology and radiotherapy departments.

PURPOSE: MRI is essential in the management of brain tumours. However, long waiting times reduce pat...

Report of the HIMSS-SIIM Enterprise Imaging Community Data Standards Evaluation Workgroup: Anatomic Ontology Assessment.

Previously, the lack of a standard body part ontology has been identified as a critical deficiency n...

Robotics and Radiology.

Semi-supervised learning framework with shape encoding for neonatal ventricular segmentation from 3D ultrasound.

BACKGROUND: Three-dimensional (3D) ultrasound (US) imaging has shown promise in non-invasive monitor...

Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method.

PURPOSE: This study aims to assess the diagnostic value of ultrasound habitat sub-region radiomics f...

Integrating intratumoral and peritumoral radiomics with deep transfer learning for DCE-MRI breast lesion differentiation: A multicenter study comparing performance with radiologists.

PURPOSE: To conduct the fusion of radiomics and deep transfer learning features from the intratumora...

Frequency and characteristics of errors by artificial intelligence (AI) in reading screening mammography: a systematic review.

PURPOSE: Artificial intelligence (AI) for reading breast screening mammograms could potentially repl...

Time-Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach.

BACKGROUND: Pathological complete response (pCR) is an essential criterion for adjusting follow-up t...

Verification of image quality improvement by deep learning reconstruction to 1.5 T MRI in T2-weighted images of the prostate gland.

This study aimed to evaluate whether the image quality of 1.5 T magnetic resonance imaging (MRI) of ...

Explainable deep-learning-based ischemia detection using hybrid O-15 HO perfusion positron emission tomography and computed tomography imaging with clinical data.

BACKGROUND: We developed an explainable deep-learning (DL)-based classifier to identify flow-limitin...

Validation of an Artificial Intelligence-Based Ultrasound Imaging System for Quantifying Muscle Architecture Parameters of the Rectus Femoris in Disease-Related Malnutrition (DRM).

(1) Background: The aim was to validate an AI-based system compared to the classic method of reading...

Patient-centered radiology reports with generative artificial intelligence: adding value to radiology reporting.

The purposes were to assess the efficacy of AI-generated radiology reports in terms of report summar...

Why your doctor is not an algorithm: Exploring logical principles of different clinical inference methods using liver transplantation as a model.

The development of machine learning (ML) tools in many different medical settings is largely increas...

Automatic text classification of prostate cancer malignancy scores in radiology reports using NLP models.

This paper presents the implementation of two automated text classification systems for prostate can...

[Large language models from OpenAI, Google, Meta, X and Co. : The role of "closed" and "open" models in radiology].

BACKGROUND: In 2023, the release of ChatGPT triggered an artificial intelligence (AI) boom. The unde...

The Future Role of Radiologists in the Artificial Intelligence-Driven Hospital.

Increasing population and healthcare costs make changes in the healthcare system necessary. This art...