Radiology

Machine learning-based prediction of large-for-gestational-age neonates in diabetic and non-diabetic pregnancies.

OBJECTIVE: This study determines whether a machine-learning model integrating sonographic biometry with maternal clinical parameters improves prediction of large-for-gestational-age (LGA) compared with Hadlock's EFW formula. METHODS: We conducted a retrospective cohort study including all singleton live births at ≥32 gestational weeks at a tertiary medical center. Predictors comprised biparietal d...

Predicting molecular types of adult-type diffuse gliomas based on MRI reports with large language models.

OBJECTIVES: To evaluate the performance of large language models (LLMs) in predicting molecular types of adult-type diffuse gliomas according to the 2021 WHO classification using MRI radiology reports. MATERIALS AND METHODS: This retrospective study included 2169 patients diagnosed with adult-type diffuse gliomas (294 oligodendrogliomas, 295 IDH-mutant astrocytomas, and 1580 IDH-wildtype glioblast...

Dynamic-Guided Diffusion Probability Model for Cranial Nerves Segmentation.

PURPOSE: Segmentation of cranial nerves (CNs) bundles using magnetic resonance imaging (MRI) provides a valuable quantitative approach for analyzing t...

Support vector machine classification of 18F-FDG PET scans across subtypes of amyotrophic lateral sclerosis.

PURPOSE: While 18F-FDG PET imaging has demonstrated diagnostic value in people with Amyotrophic Lateral Sclerosis (PwALS) and group-level differences ...

Innovation, Technology, and Telemedicine in Critical Care.

Critical care medicine is undergoing a major transformation driven by rapid technological innovation, digital integration, and telemedicine. This arti...

Artificial intelligence-driven prostate cancer diagnosis: Enhancing accuracy and personalizing patient care.

Prostate cancer remains a major global burden; diagnostic pathways rely on prostate-specific antigen (PSA), multiparametric magnetic resonance imaging...

Computational studies of target-specific radiopharmaceuticals for theranostics.

Radiopharmaceuticals are key tools in nuclear medicine, enabling both diagnostic imaging and targeted therapy for conditions such as cancer and neurol...

Complexity of olfactory-evoked EEG as an evidence-based marker of Alzheimer's disease.

Olfactory impairment is an early symptom of Alzheimer's disease (AD). However, currently used olfactory task-based functional magnetic resonance imagi...

LI-RADS-aligned artificial intelligence for liver cancer diagnosis: methods, evidence, and clinical readiness.

Liver tumor diagnosis relies heavily on imaging, and the liver imaging reporting and data system (LI-RADS) provides a structured framework for evaluat...

A bibliometric analysis of the 100 most cited radiology papers on pancreatic diseases (1990-Present).

PURPOSE: To map the intellectual evolution of pancreatic radiology through a comprehensive bibliometric analysis of the 100 most-cited articles, ident...

A nomogram model integrating ultrasound-based multimodal radiomics features and clinical indexes for diagnosing significant hepatic fibrosis in AILD patients.

OBJECTIVE: To develop a prediction model combining radiomics features from 2D ultrasound (2D-US) and shear wave elastography (SWE) with clinical indic...

Deep learning-based simulated contrast-enhanced MRI for rectal cancer evaluation: a multicenter study.

OBJECTIVES: To assess the feasibility and accuracy of using deep learning to generate simulated contrast-enhanced T1-weighted rectal MRI scans from pr...

Identification of TFCC substructure injury in wrist MRI using computer vision: a diagnostic aid for radiologists.

OBJECTIVE: The purpose of this study is to develop an automated model to assist in the detection of substructural injuries of the triangular fibrocart...

Deep Learning-Based Denoising for High-Resolution Carotid Vessel Wall MRI Using Standard Neurovascular Coils.

PURPOSE: To develop a deep learning (DL) denoising method to enhance high-resolution carotid vessel wall MRI quality acquired using a standard head-an...

Developmental Brain Age Estimation From MRI Data: A Systematic Review of Deep Learning Approaches and Open Datasets.

Brain age is an emerging concept that reflects complex, time-dependent changes in brain structure, identifying departures from expected neurodevelopme...

Evaluating the efficacy of the ResNet50 deep learning model utilizing thyroid scintigraphy images for predicting the outcomes of initial iodine-131 therapy in patients with Graves' disease.

BACKGROUND: Graves' disease (GD), a leading cause of hyperthyroidism, exhibits heterogeneous responses to iodine-131 therapy, underscoring the need fo...

Breast Cancer Detection in Mammography Images Using Transfer Learning Model.

Breast cancer remains a significant global health concern, emphasizing the need for advanced and accurate diagnostic tools. This research paper focuse...

Granular Machine Learning-Based Computed Tomography Contrast Phase Prediction.

OBJECTIVE: To develop and evaluate a machine learning framework that detects intravenous contrast and distinguishes eight granular renal contrast phas...

Recent advances in the early detection of ovarian cancer.

Ovarian cancer (OC), predominantly epithelial OC, remains the most lethal gynecological malignancy. Owing to its often asymptomatic or non-specific cl...

Dataset of artificial breast cancer MRIs produced from unpaired mammograms.

The development of machine learning models for medical imaging is often constrained by the scarcity of large, paired datasets, particularly in breast ...