Cardiovascular

Semi-supervised YOLO-DEP for high-resolution X-ray component localization and counting.

Accurate localization and counting of tiny electronic components in high-resolution X-ray images is a critical yet challenging task in nuclear science, radiation imaging, and industrial quality control. Traditional methods suffer from poor generalization in cluttered scenes, while deep learning approaches are limited by the lack of large-scale annotated datasets. This study aims to develop a semi-...

Preoperative CT-based radiomics of suprapancreatic adipose tissue for predicting high-difficulty lymph node dissection in gastric cancer.

PURPOSE: Preoperative assessment of lymph node dissection (LND) difficulty in gastric cancer remains challenging. Conventional clinical indicators are relatively coarse and may not adequately reflect tissue-related complexity within the surgical field. This study aimed to develop and validate a preoperative CT-based radiomics approach using suprapancreatic adipose tissue for predicting high-diffic...

Lung ultrasound in interstitial lung disease: from bedside screening to prognostic integration and AI-assisted standardization.

Interstitial lung diseases (ILDs) require early recognition and longitudinal assessment, yet repeated high-resolution computed tomography (HRCT) is of...

Integrating Machine Learning and Single-Cell Analysis to Reveal the Diagnostic and Therapeutic Value of Regulated Cell Death Mechanisms in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) treatment faces significant challenges, particularly in tumor growth, metastasis, and drug resistance. While several pr...

Comparative study of radiomics features between ASL and DSC perfusion-weighted imaging in distinguishing radiation-induced brain injury from glioma recurrence.

OBJECTIVE: To compare the radiomics features of pseudocontinuous arterial spin labeling (ASL) and dynamic susceptibility contrast (DSC) perfusion-weig...

Artificial Intelligence in Gynecologic Oncology: Current Applications, Clinical Challenges, and Future Perspectives.

Cervical, endometrial, ovarian, vulvar, vaginal, fallopian tube, and gestational trophoblastic neoplasia (GTN) are major gynecologic cancers that sign...

Neural Representation Learning for Compact and Efficient Modeling of Monte Carlo Phase Space Data.

PURPOSE: Monte Carlo (MC) simulations provide gold standard dose calculations in radiation therapy but generate large phase space (PHSP) files that li...

Comparative analysis of CNN-LSTM and CNN-BiLSTM hybrid deep learning models for solar radiation prediction.

Solar radiation forecasting is a complex task since the radiation signal is nonlinear, intermittent and is significantly influenced by meteorological ...

Radiation Resilient Synthetic Antiferromagnets-Based Neuromorphic Device for Sea Surface Temperature Reconstruction.

Reconstruction of sea surface temperature is critical for marine monitoring, yet conventional edge devices based on complementary metal-oxide-semicond...

Plasma metabolomics combined with machine learning for postoperative prognostic stratification in hormone receptor-positive/human epidermal growth factor receptor 2-negative early breast cancer.

BACKGROUND AND AIMS: Hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) breast cancer (BC) accounts for the major...

Phase-only synthesis of cosecant-squared patterns with reduced sidelobes in large planar arrays via physics-informed deep neural networks.

This paper introduces a deep learning-based framework for phase-only synthesis of cosecant-squared (csc²) radiation patterns in planar antenna arrays ...

MRI-based quantification of intratumoral heterogeneity for predicting recurrence risk in ER+/HER2- breast cancer.

OBJECTIVES: The need for a cost-effective, rapid, and increasingly accessible alternative to the 21-gene assay prompted this study, which developed a ...

Radiation scatter at fluoroscopy-guided endoscopic procedures: Building towards further optimization.

INTRODUCTION: Exposure to ionizing radiation by endoscopy personnel during fluoroscopy-guided procedures remains a health hazard. We aimed to evaluate...

AI-assisted digital breast tomosynthesis as a decision-support tool for post-neoadjuvant surgical planning in breast cancer.

PURPOSE: Accurate assessment of residual disease after neoadjuvant chemotherapy (NAC) is essential for surgical planning and prevention of incomplete ...

Tracer-agnostic diffusion model-based CT-free attenuation correction for brain PET: Comprehensive evaluation across 14 tracers.

OBJECTIVE: Accurate attenuation correction (AC) is critical in quantitative brain PET imaging. Conventional CT-based AC methods increase radiation exp...

Optimising chest computed tomography (CT) scan ranges in paediatric patients to reduce radiation exposure.

AIM: The aim of this study was to accurately position the scan range of unenhanced chest computed tomography (CT) scans for paediatric patients by cla...

Machine learning and plasma growth differentiation factor-15 (GDF15) for early risk stratification of heart failure progression in a long-latency doxorubicin cardiomyopathy model.

BACKGROUND: Anthracycline-induced cardiotoxicity is a major cause of late heart failure (HF) in cancer survivors. Yet early identification of individu...

Delta radiomics for predicting early radiation-induced lung injury after thoracic radiotherapy: a retrospective paired-CT study.

PURPOSE: Early radiation-induced lung injury remains a clinically relevant complication after thoracic radiotherapy. We compared pretreatment, posttre...

CT in adult patients with Polytrauma: current practices, protocol optimization, structured reporting and future directions.

Computed tomography [CT] is the frontline imaging modality for the assessment of polytrauma patients because of its speed, diagnostic accuracy and inf...

The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of (CADRA).

Artificial intelligence (AI) is poised to fundamentally transform radiation medicine, with growing influence across clinical decision-making, workflow...