Radiology

The role of digital tools and artificial intelligence in supporting antimicrobial stewardship: Study protocol for a systematic review.

INTRODUCTION: Since the post-antibiotic era, there has been significant difficulty in treating infectious diseases due to the increase in antimicrobial resistance, the scarcity of new antimicrobials, and the complexity of the healthcare system. The World Health Organization (WHO) recognized it as one of the main public health problems. To mitigate this issue, Antimicrobial Stewardship Programs (AS...

Advanced Fetal Cardiac Disease Detection Using Optimized Gegenbauer Graph Neural Networks on Ultrasound Images to Facilitate Early Diagnosis and Clinical Assessment.

Early detection of fetal cardiac diseases can dramatically improve neonatal outcomes by enabling timely intervention and informed clinical management. However, accurate diagnosis remains challenging due to the complexity of fetal heart structures in ultrasound images and the subtlety of congenital anomalies. To address these challenges, this work introduces Fetal Cardiac Disease Detection Using Ul...

Fully automated quantification of net water uptake in acute ischemic stroke using only non-contrast CT imaging.

OBJECTIVE: Estimating early lesion progression in ischemic stroke is essential for assessing thrombolytic treatment efficacy. While computed tomograph...

AI-driven paradigm shift in follicle ultrasound monitoring: from automated segmentation to clinical decision support.

This commentary delineates the developmental pathway of artificial intelligence (AI) in ultrasound follicular monitoring, highlighting a paradigm shif...

DAUS-Net: Toward Ultrasound Scanner-Agnostic Domain Generalized Robust and Accurate Segmentation.

In medical imaging, segmentation is a critical task for analysis and diagnosis. Deep learning-based segmentation has been actively studied and has sho...

MGML: A plug-and-play meta-guided multi-modal learning framework for incomplete multimodal brain tumor segmentation.

Leveraging multimodal information from Magnetic Resonance Imaging (MRI) plays a vital role in lesion segmentation, especially for brain tumors. Howeve...

Combining the prognostic values of entropy-based heterogeneity features from 18 F-fluorodeoxyglucose PET and transmission computed tomography using machine learning in patients with lung adenocarcinoma undergoing curative surgery.

OBJECTIVE: The objective of this study is to evaluate the combined prognostic values of 18 F-fluorodeoxyglucose ( 18 F-FDG) PET and computed tomograph...

Recent Advances in Musculoskeletal Radiology: Bridging Innovation and Clinical Application.

Recent advances in musculoskeletal (MSK) radiology have markedly improved diagnostic accuracy through innovations in MRI, CT, and artificial intellige...

Development and external validation of an MRI radiomics-based machine learning model to predict tumour recurrence in brain metastases treated with Gamma Knife radiosurgery.

BACKGROUND: Predicting recurrence after gamma knife radiosurgery (GKRS) is clinically important, as it informs salvage treatment and patient managemen...

AI-CAD for diagnostic mammography: comparison to radiologists according to different indications.

OBJECTIVE: Although artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly applied in screening mammography, its use in diagn...

Identification of high-priority radiology reports with unexpected findings using fine-tuned large language models.

OBJECTIVE: This study aims to evaluate whether large language models (LLMs) can accurately predict the urgency and severity of radiology reports. MATE...

Cost-effectiveness of artificial intelligence tools in radiology: a systematic review.

OBJECTIVES: To systematically review the evidence on the cost-effectiveness of artificial intelligence (AI) interventions for diagnostic imaging in ra...

Conjugate gradient and deep learning reconstructions: reduced time without affecting image quality and nodule detection.

OBJECTIVES: The purpose of this study was to determine the utility of conjugate gradient reconstruction (CG Recon) and deep learning reconstruction (D...

Deep Learning-Based Brainstem Segmentation and Multi-Class Classification for Parkinsonian Syndrome.

BACKGROUND: Brain segmentation using structural MRI is effective for identifying regional atrophy in Parkinsonian syndromes. However, clinical validat...

Artificial Intelligence in Fetal MRI: Principles, Applications, Limitations, and Future Directions.

Artificial intelligence (AI) offers solutions to overcome limitations of fetal MRI, including motion, low signal-to-noise ratio, and slice misregistra...

Effect of deep learning reconstruction on arm-induced artifacts compared with hybrid iterative reconstruction and filtered-backprojection in abdominal CT.

Abdominal computed tomography (CT) is normally performed with patients raising their arms over abdominal region to prevent arm-induced artifacts that ...

Elevated Retinal Neovascularization on Widefield Optical Coherence Tomography Angiography Predicts Complications in High-Risk Proliferative Diabetic Retinopathy.

PURPOSE: To determine whether retinal neovascularization (RNV) metrics derived from single-shot widefield swept-source OCT angiography (SS-OCTA) predi...

Understanding the challenges to implementing AI solutions in radiology departments and how to overcome them: A comprehensive review endorsed by the French College of Radiologists (CERF) and the French Society of Radiology (SFR).

Despite the exponential growth of artificial intelligence (AI) solutions designed to assist radiologists in clinical practice, their actual impact on ...

Microrobots/micromotors in cancer therapeutics, advances and applications.

Cancer remains one of the most challenging diseases to conquer due to its high mortality rate and the lack of effective diagnostic and therapeutic too...

Unsupervised anomaly detection in brain MRI via disentangled anatomy learning.

Detection of various lesions in brain MRI is clinically critical, but challenging due to the diversity of lesions and variability in imaging condition...