Radiology

ERANet: Edge replacement augmentation for semi-supervised meniscus segmentation with prototype consistency alignment and conditional self-training.

The meniscus, a fibrocartilaginous structure within the knee joint, plays an essential role in joint stability and the prevention of knee osteoarthritis (OA). Accurate segmentation of the meniscus from magnetic resonance imaging (MRI) is crucial for early diagnosis and monitoring of OA progression. However, manual segmentation is labor-intensive, while automatic approaches face challenges due to v...

Feasibility of deep learning-based cancer detection in ultrasound microvascular images.

Acoustic angiography is a superharmonic contrast-enhanced ultrasound modality that maps 3-D microvasculature with fine spatial resolutions and has demonstrated potential to improve disease detection. However, the application of acoustic angiography for cancer detection currently faces challenges. Quantitative analysis relies on time-consuming, manual segmentation of individual vessels, and inter-o...

Emerging trends in nuclear medicine: breakthrough in radiopharmaceuticals, Geant4 simulations, and AI integration.

Nuclear medicine has witnessed revolutionary progress, spurred by advances in radiopharmaceuticals, computational modeling, and artificial intelligenc...

Deep Learning Radiomics Model Based on Ultrasound Images Predicts Myometrial Infiltration of Endometrial Cancer.

OBJECTIVE: This study aims to develop and validate a deep learning radiomics (DLR) model based on ultrasound images for non-invasively distinguishing ...

A CT-based 2.5D deep learning model for preoperative T-staging in gastric cancer: a retrospective multicenter study.

PURPOSE: To develop and validate a 2.5D multi-angle deep learning (MADL) model for preoperative T-staging in patients with gastric cancer (GC) and to ...

Ultrasound to guide critical decisions: What you need to know.

BACKGROUND: Hypotension in the intensive care unit (ICU) demands rapid diagnosis and intervention, as delays in identifying the etiology of shock dire...

Automatic measurement of pharyngeal contraction ratio during deglutition using 2D cine MRI with deep learning: A pilot study.

This study aimed to develop a deep learning-based method for automatic segmentation of the pharyngeal area (PA) and measurement of the pharyngeal cont...

Large language models for structured cardiovascular data extraction: a foundation for scalable research and clinical applications.

AIMS: Automated extraction of information from cardiac reports would benefit both clinical reporting and research. Large language models (LLMs) hold p...

A deep learning-based radiomics model for noninvasive diagnosis of melanoma.

To develop a noninvasive diagnostic model integrating deep learning and radiomics for improving the accuracy and clinical utility of early melanoma di...

Iterative learning for joint image denoising and motion artifact correction of 3D brain MRI.

Image noise and motion artifacts greatly affect the quality of brain magnetic resonance imaging (MRI) and negatively influence downstream medical imag...

Resolution and quality enhancement of SPECT cerebral blood flow images using Pix2pix deep learning.

BACKGROUND: Cerebral blood flow (CBF) imaging can be performed using SPECT with 123I-IMP; however, its spatial resolution and image quality are inferi...

Transformer-based multi-scale feature fusion for real-time CT bone metastasis detection.

Bone metastasis, a frequent complication of advanced cancers, requires early, precise detection to enable timely interventions and improve patient out...

Retinal Age Gap as a Biomarker for Patient Satisfaction and Visual Quality After Cataract Surgery.

PURPOSE: To evaluate whether the difference between artificial intelligence (AI)-estimated retinal biological age and chronological age-the retinal ag...

AI-assisted computed tomography analysis for pre-procedural planning prior to TAVI.

BACKGROUND: Several software programs have specifically been developed to analyse cardiac computed tomography prior to transcatheter aortic valve impl...

Deep learning for automated alveolar cleft segmentation and bone graft volume estimation in cone-beam computed tomography imaging: a multicenter study.

OBJECTIVE: To train and validate a deep learning-based diagnostic tool capable of accurately segmenting the alveolar cleft region and automatically es...

AI landmark versus conventional ultrasound-guided injections for adhesive capsulitis: a prospective, propensity score-matched, superiority study.

AIM: To investigate the artificial Intelligence (AI) landmark for guiding rotator cuff interval injections for adhesive capsulitis (AC). MATERIAL AND ...

Development and validation of a multiphase computed tomography-based radiomics classifier for differentiating retroperitoneal non-fatty dedifferentiated liposarcoma from leiomyosarcoma.

OBJECTIVE: We aimed to develop and internally validate a radiomics classification model based on multiphase computed tomography (CT) scans for preoper...

Deep learning for automatic detection of hepatocellular carcinoma in dynamic contrast-enhanced MRI.

OBJECTIVE: Develop a deep learning model for automatic hepatocellular carcinoma (HCC) detection in T1 weighted imaging (WI) Dynamic Contrast-Enhanced ...

18F-FDG PET-based ensemble deep learning model for the prediction of lymphovascular invasion in colorectal cancer patients.

OBJECTIVE: The aim of this study was to investigate the diagnostic performance of the 2.5-dimensional (2.5D) ensemble deep learning (DL) model based o...

Performance of Large Language Models on Radiology Residency In-Training Examination Questions.

RATIONALE AND OBJECTIVES: Large language models (LLMs) are increasingly investigated in radiology education. This study evaluated the performance of s...