Radiology

RetinalFRNet: retinal vessel segmentation in OCTA images using the feature reconstruction network.

OBJECTIVES: Automated segmentation of retinal blood vessels in optical coherence tomography angiography (OCTA) images is essential for early diagnosis of ocular diseases such as diabetic retinopathy, myopia, and macular degeneration. This study evaluates the performance of the Retinal Feature Reconstruction Network (RetinalFRNet), a full-resolution deep learning architecture designed for OCTA reti...

Support vector machine-driven Parkinson's disease identification: a 7-Tesla multidimensional structural MRI approach.

Parkinson's disease (PD) is a progressive neurodegenerative disorder diagnosed clinically by cardinal motor symptoms, with structural brain changes associated with its diverse motor and non-motor manifestations. This study integrated multidimensional 7-Tesla structural Magnetic Resonance Imaging (MRI) features (gray matter volume, cortical thickness, etc.) using Support Vector Machine (SVM) to dis...

Fast 3D dynamic visualization of porcine spermatozoa by using high-resolution wavefront coding light sheet microscopy and machine learning.

We present a wavefront coding (WFC) based decoupled illumination-detection light sheet fluorescence microscopy (DID-LSFM) system that integrates machi...

Deep learning-based breast cancer detection with customized ensemble attention.

Breast cancer remains one of the leading malignancies globally, and accurate diagnostic decisions at the early stages of the disease can significantly...

An advanced hybrid deep learning framework for high-precision brain tumor detection and classification in MRI scans.

Early and accurate identification of brain tumors from magnetic resonance imaging (MRI) is essential for timely clinical intervention; however, manual...

Diagnosis of Acromegaly.

The clinical presentation of acromegaly reflects systemic effects of chronic GH and IGF-I excess. Diagnostic delay frequently ranges from 6 to 10 year...

A lightweight GhostYOLO framework with GhostDynamic Module and multi-scale attention for thyroid nodule diagnosis.

Ultrasound is the primary tool for thyroid nodule assessment, but diagnostic accuracy and efficiency require improvement. This study aims to develop a...

Time-Conditioned Zero-Shot Self-Supervised Reconstruction for Accelerated 3D Ultra-Low-Field MRI.

PURPOSE: Ultra-low-field (ULF) MRI provides a cost-effective, portable imaging option but has relatively low SNR and long acquisition times compared t...

Quantifying the Heterogeneity of TRUS-Visible Lesions for Predicting Prostate Cancer: A Multicenter Preliminary Study.

RATIONALE AND OBJECTIVES: This study aimed to quantitatively characterize the heterogeneity of Transrectal ultrasound (TRUS)-visible lesions using sub...

Predicting intracranial aneurysm rupture risk and intervention outcomes using denoising-enhanced CT Angiography.

BACKGROUND AND PURPOSE: To develop a comprehensive multi-modal framework for assessing the rupture risk of intracranial aneurysms and predicting inter...

Quantitative whole-body measurement of inflammothrombotic immunologic response disease using Fleming method for tissue and vascular differentiation and metabolism.

BACKGROUND: Precision theranostics in nuclear medicine requires reproducible, calibrated quantification of tissue perfusion, and metabolism. This stud...

Sex-Specific Prognostic Value of Left Atrioventricular Coupling Index in Acute Coronary Syndrome: Insights From Artificial Intelligence-Enhanced 3-Dimensional Echocardiography.

BACKGROUND: Left atrioventricular coupling index (LACI) has emerged as a prognostic biomarker, but its sex-specific relevance in acute coronary syndro...

Volumetric phase-specific three-dimensional radiomics and machine learning for differentiation of non-mass enhancement in breast magnetic resonance imaging.

PURPOSE: Non-mass enhancement (NME) in breast magnetic resonance imaging (MRI) is a diagnostically challenging entity due to overlapping benign and ma...

Beyond binary classification: a pilot study of imaging-derived glioma severity modeling using T1-weighted and diffusion MRI radiomics.

OBJECTIVE: Gliomas are heterogeneous brain tumors with variable biology and treatment response. Accurate, non-invasive assessment of tumor aggressiven...

Application of artificial intelligence in paediatric oncology imaging.

Paediatric oncology relies heavily on medical imaging for diagnosis, treatment planning, and longitudinal disease monitoring. Yet the field faces uniq...

Data-Efficient Language Model for Assessing Pulmonary Embolism Diagnostic Certainty From Radiology Reports: Model Development and Validation Study.

BACKGROUND: Computed tomography pulmonary angiography (CTPA) is the standard imaging modality for diagnosing pulmonary embolism (PE), but diagnostic u...

Next-generation AI for visually occult pancreatic cancer detection in a low-prevalence setting with longitudinal stability and multi-institutional generalisability.

BACKGROUND: Failure of conventional imaging to detect pancreatic ductal adenocarcinoma (PDA) at its visually occult pre-diagnostic stage is a primary ...

Structural determinants of pulmonary diffusing capacity identified by network analysis and machine learning on quantitative CT.

Diffusing capacity for carbon monoxide (DLCO) reflects pulmonary gas exchange efficiency, but its measurement and interpretation remain challenging du...

Deep learning models to predict mammographic density jointly on standard dose and low dose images.

OBJECTIVES: Mammographic density is associated with increased risk of developing breast cancer. Automated estimation of density in women below normal ...

Counterfactual Reasoning for Mammogram Classification via Semantic Texture Masking.

Artificial intelligence-based computer-aided diagnosis (CADx) systems have seen growing adoption in mammography, yet the limited interpretability of t...