Radiology

Comparison of deep learning-based image segmentation methods for intravascular ultrasound on retrospective and large image cohort study.

OBJECTIVES: The aim of this study was to investigate the generalization performance of deep learning segmentation models on a large cohort intravascular ultrasound (IVUS) image dataset over the lumen and external elastic membrane (EEM), and to assess the consistency and accuracy of automated IVUS quantitative measurement parameters.

Legal and ethical principles governing the use of artificial intelligence in radiology services in South Africa.

Artificial intelligence (AI) will drastically change the healthcare system. Radiology is one speciality that is most affected as AI algorithms are increasingly used in diagnostic imaging. AI-enhanced health technologies will, inter alia, increase workflow efficiency, improve diagnostic accuracy, reduce healthcare-related costs, and help alleviate medical personnel shortages in under-resourced sett...

CEST and nuclear Overhauser enhancement imaging with deep learning-extrapolated semisolid magnetization transfer reference: Scan-rescan reproducibility and reliability studies.

PURPOSE: To develop a novel MR physics-driven, deep-learning, extrapolated semisolid magnetization transfer reference (DeepEMR) framework to provide f...

Recent Advances and Challenges in Enzymatic Depolymerization and Recycling of PET Wastes.

Poly (ethylene terephthalate) (PET) is one of the most commonly used plastics in daily life and various industries. Enzymatic depolymerization and rec...

Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions.

AIMS: Impaired standardization of echocardiograms may increase inter-operator variability. This study aimed to determine whether the real-time guidanc...

Trapdoor endarterectomy for coral reef plaque of the paravisceral aorta in the modern era.

Coral reef atherosclerosis of the paravisceral aorta is a rare disease whose description is confined to before contemporary vascular surgical techniqu...

Toward a deep learning-based magnetic resonance imaging only workflow for postimplant dosimetry in I-125 seed brachytherapy for prostate cancer.

BACKGROUND AND PURPOSE: The current standard imaging-technique for creating postplans in seed prostate brachytherapy is computed tomography (CT), that...

Implications of ultrasound-based deep learning model for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

OBJECTIVES: The current study developed an ultrasound-based deep learning model to make preoperative differentiation among hepatocellular carcinoma (H...

A Lightweight Deep Learning Framework for Automatic MRI Data Sorting and Artifacts Detection.

The purpose of this study is to develop a lightweight and easily deployable deep learning system for fully automated content-based brain MRI sorting a...

Robotic tomographic ultrasound and artificial intelligence for management of haemodialysis arteriovenous fistulae.

BACKGROUND: Arteriovenous fistulae (AVF) and Arteriovenous Grafts (AVG) may present a problematic vascular access for renal replacement therapy (RRT),...

Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions.

Positron emission tomography (PET) is vital for diagnosing diseases and monitoring treatments. Conventional image reconstruction (IR) techniques like ...

Artificial Intelligence-powered automatic volume calculation in medical images - available tools, performance and challenges for nuclear medicine.

Volumetry is crucial in oncology and endocrinology, for diagnosis, treatment planning, and evaluating response to therapy for several diseases. The in...

Use of deep learning to segment bolus during videofluoroscopic swallow studies.

Anatomical segmentations generated using artificial intelligence (AI) have the potential to significantly improve video fluoroscopic swallow study (VF...

Saliency of breast lesions in breast cancer detection using artificial intelligence.

The analysis of mammograms using artificial intelligence (AI) has shown great potential for assisting breast cancer screening. We use saliency maps to...

Coronary CT Angiography in the Cath Lab: Leveraging Artificial Intelligence to Plan and Guide Percutaneous Coronary Intervention.

The role of coronary CT angiography for the diagnosis and risk stratification of coronary artery disease is well established. However, its potential b...

The Performance of Machine Learning for Prediction of H3K27 M Mutation in Midline Gliomas: A Systematic Review and Meta-Analysis.

BACKGROUND: Diffuse midline gliomas (DMGs) encompass a set of tumors, and those tumors with H3K27 M mutation carry a poor prognosis. In recent years, ...

Artificial intelligence in medical imaging is a tool for clinical routine and scientific discovery.

The emergence of powerful machine learning methodology together with an increasing amount of data collected during clinical routine have fostered a gr...

Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005-2023).

BACKGROUND AND OBJECTIVES: Non-alcoholic fatty liver disease (NAFLD) is a common liver disease with a rapidly growing incidence worldwide. For prognos...

Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer.

BACKGROUND AND OBJECTIVE: Recently, deep learning (DL) algorithms showed to be promising in predicting outcomes such as distant metastasis-free surviv...

Densely connected convolutional networks for ultrasound image based lesion segmentation.

Delineating lesion boundaries play a central role in diagnosing thyroid and breast cancers, making related therapy plans and evaluating therapeutic ef...