Radiology

Fully automated three-dimensional deep learning-based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery.

OBJECTIVE: There are several clinical and research applications for determining the amount of brain tissue resected after epilepsy surgery; however, manual segmentation of postoperative magnetic resonance imaging (MRI) is imprecise and time-consuming. In this study, we developed and benchmarked ResectVol DL, a freely available deep learning-based tool that performs this task automatically. METHODS...

Deep learning-enabled versatile shape perception for soft robots via single-ended multimode fiber.

The evolution of soft robots into embodied intelligent systems relies fundamentally on precise proprioception. However, a universal solution for capturing continuous deformations during diverse interactions, particularly in spatially confined interventional scenarios, remains lacking. Here, we introduce a deep learning-enabled versatile shape perception method based on a single-ended multimode fib...

Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: Clinical insights, pitfalls, and observer agreement analyses.

PURPOSE: This study addresses critical gaps in automated lymphoma segmentation from PET/CT imaging, often overlooked in prior work. While deep learnin...

Application Value of Radiomics-Based Machine Learning for Preoperative Risk Stratification of Bladder Cancer: Systematic Review and Meta-Analysis.

BACKGROUND: Some researchers have explored the application of radiomics-based machine learning to detect preoperative muscle invasion, high-grade tumo...

Comparing and assessing the thermophysical and structural predictions of an Empirical and a Machine-learning interatomic potentials on liquid (U,Zr).

Liquid uranium-zirconium (U,Zr) mixtures play a crucial role in the context of nuclear accident scenarios, particularly in the early stages of Pressur...

Opportunistic Screening Based on Computed Tomography in Musculoskeletal Radiology: How and Why.

With the rapid growth of the use of computed tomography, advances in artificial intelligence enable opportunistic screening, the systematic extraction...

Comparison of ChatGPT and dietitians in formulating diet plans and recommendations for patients with cardiometabolic diseases.

BACKGROUND: Artificial Intelligence (AI) is transforming personalized medicine, yet its efficacy constitutes a dynamic factor in the field of health a...

Validation of Artificial Intelligence in Detecting Acute Cervical Spine Fractures on CT.

BACKGROUND: Cervical spine fractures require prompt and accurate diagnosis to minimize risk of long-term neurological impairment. Artificial intellige...

Enhancing Tenosynovitis Diagnosis With XGBoost: A Machine Learning Approach Using Ultrasound Data.

BACKGROUND: Rheumatoid arthritis (RA) is a systemic autoimmune disorder characterized by chronic inflammation and progressive joint destruction. Tenos...

Radiomic Carotid Plaque Features Integrated into Machine Learning Models for Cardiovascular Risk Prediction.

OBJECTIVE: Carotid plaque detected by ultrasound is associated with major adverse cardiovascular events (MACE) and can be characterized using manual o...

New Frontiers of Skin Ultrasound in Precision Medicine.

Ultrasound has emerged as a versatile, non-invasive imaging technique in dermatology, offering real-time, high-resolution visualization of cutaneous s...

Fast surface reconstruction of human brain MRI: benchmarking deep-learning based morphometry tools.

Time efficient and reliable pipelines for quantitative evaluation of structural brain MRI are essential to utilize the potential of morphometry tools ...

Automated error localisation and correction techniques for deep-learning-based segmentation of 3D MRI sequences based on feature-derived-region aggregation.

Automatic segmentation using convolutional neural networks (CNNs) has become a key tool in musculoskeletal imaging, offering substantial reductions in...

An artificial intelligence model to detect abnormal ejection fraction from non-contrast chest computed tomography: the CT-LVEF study.

AIMS: Heart failure (HF), a major global health challenge, affects millions worldwide and poses substantial healthcare and economic burdens. It is est...

Emerging technologies in interventional pain management: a scoping review on current innovations and future directions.

BACKGROUND: Emerging digital and bioelectronic technologies are rapidly transforming interventional pain management, but their clinical roles and evid...

Deep learning for the diagnosis of lumbar disc herniation: a systematic review and meta-analysis.

BACKGROUND: Lumbar disc herniation (LDH) is a major cause of low back pain and disability worldwide. Although magnetic resonance imaging (MRI) is the ...

Fusion of MRI-radiomics and clinical features to improve pathological T-stage prediction of rectal cancer: a multicenter study.

BACKGROUND: Developing a rational model for precise preoperative staging of rectal cancer using magnetic resonance imaging is crucial for improving th...

A methodological framework for integrating generalisable deep learning and radiomics fusion model for early lung cancer detection across multi-centre imaging datasets.

Early detection of lung cancer remains one of the most effective strategies for improving survival; however, diagnostic performance is limited by vari...

CT-based habitat analysis combined with multi-channel deep learning for predicting early recurrence after pancreatic cancer resection: a multicenter study.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with high early recurrence rates after curative resection. Current predictio...

Fourier Shell Analysis: k-Space-Based Metrics for Assessing Super-Resolution in 4D Flow MRI.

PURPOSE: To support the emerging field of super-resolution (SR) in 4D flow MRI by proposing Fourier shell analysis to disentangle resolution enhanceme...