Radiology

Utility of augmented reality in endoscopic surgery for stones: European Association of Urology endourology up-to-date review.

PURPOSE OF REVIEW: The development of digital solutions has a direct impact on the modern understanding of the future of urology. Augmented reality is no exception. Specialists use it for intraoperative navigation, which positively impacts procedure metrics, especially in endoscopic surgery for stones. This review aims to determine the chronology of this technology's development and its current tr...

Smartphone Detection of Fetal Movements Using Artificial Intelligence.

OBJECTIVE: To evaluate whether machine learning could be used with audio recordings from a smartphone to detect fetal movements that create disruptions of the amniotic fluid environment. METHODS: We conducted a prospective study to simultaneously record fetal movements seen on ultrasound and audio recordings using a smartphone placed on the maternal abdomen and to compare these with maternal perce...

Derivation of machine learning brain aging biomarkers for a set of forty thousand functional connectomes.

Various Magnetic Resonance Imaging modalities were developed to explore the brain. Among them, functional MRI is of key importance for studying brain ...

Improved image quality and reduced acquisition time in brain MRI using deep learning-based reconstruction: A quantitative and subjective assessment compared to standard MPRAGE in 0.55 T MRI.

PURPOSE: To assess the impact of deep learning (DL)-based image reconstruction on quantitative and subjective image quality in brain MRI at 0.55 T by ...

Artificial Intelligence for T classification of TNM breast cancer in MRI imaging: Enabling Precision in Treatment Decisions.

The integration of artificial intelligence (AI) into breast cancer management presents transformative potential for both diagnosis and treatment plann...

Validation of an AI Method for Automated Lymphoma Metabolic Tumor Volume Segmentation Using a Public Benchmark PET/CT Dataset.

The aim of this study was to evaluate the performance of an artificial intelligence (AI)-based method for automated segmentation of total metabolic tu...

Prediction of Ki-67 expression in invasive breast cancer with dual-modality radiomics.

Ki-67 expression, a critical biomarker for tumor aggressiveness and proliferation in invasive breast cancer, is traditionally assessed via invasive bi...

Automated detection of new cerebral infarctions and prognostic implications using deep learning on serial MRI.

We developed and externally validated a deep learning model to automatically detect new ischemic lesions on serial FLAIR MRI scans in patients with st...

Application of treatment response assessment maps (TRAMs), based on delayed-contrast MRI for radiomic characterization of breast lesions.

Dynamic contrast-enhanced (DCE) breast MRI is a highly sensitive modality for detecting breast cancer, but its limited specificity often leads to fals...

Automatic Hepatic Steatosis Quantification using Low-Dose CT with deep learning-based noise reduction and CT Fat Fraction Analysis Software.

OBJECTIVES: To evaluate the accuracy of CT-derived fat fraction (CDFF) software for quantifying hepatic steatosis at various radiation doses, using MR...

AI task-shifting for echocardiographic LVEF assessment in Singapore: an economic evaluation.

BACKGROUND: Accurate assessment of left ventricular ejection fraction (LVEF) is crucial for heart failure (HF) diagnosis but requires skilled sonograp...

Differentiation of Benign and Malignant Cervical Lymph Nodes Using a Multi-Modal Ultrasound-Based Machine Learning Model with SHAP Interpretability.

OBJECTIVES: To evaluate the diagnostic value of a machine learning (ML) model based on multi-modal ultrasound features in differentiating benign from ...

Highlights from the Italian National Congress of Imaging in Pulmonology 2025: fostering implementation of advanced technologies for precision patient-centered care.

The Italian National Congress of Imaging in Pulmonology, held in Milan on November 21st, provided a unique educational platform exploring the evolving...

Longitudinal evaluation of intra-patient changes in computed tomography-based body composition measures.

OBJECTIVE: To report longitudinal intra-patient changes in CT-based body composition using fully automated AI tools in an adult patient sample. METHOD...

In vivo quantification of arterial active mechanics using deep learning-assisted pressure-area analysis.

Active arterial mechanics, governed by vascular smooth muscle contraction, are critical to physiological regulation, cardiovascular disease progressio...

Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.

Glioblastoma (GB), the most aggressive primary brain tumor, is characterized by profound inter- and intratumoral heterogeneity and a highly immunosupp...

Unsupervised segmentation of dynamic pulmonary MRI using cross-modality adaptation with annotated CT images.

PURPOSE: Accurate segmentation of lung parenchyma in dynamic pulmonary magnetic resonance imaging (MRI) is required for clinical diagnosis and treatme...

Artificial intelligence-enabled ultrasound diagnosis and stratification of follicular thyroid neoplasms: a multi-center study.

Preoperatively distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA) remains a significant clinical challenge. Curre...

Artificial intelligence-based coronary computed tomography angiography quantification of atherosclerosis burden: comparison with intravascular ultrasound in the INVICTUS Registry.

OBJECTIVES: Automated artificial intelligence (AI)-based assessment of atherosclerosis burden applied to coronary computed tomography angiography (CCT...

Cost-effectiveness analysis of artificial intelligence-assisted risk stratification of indeterminate pulmonary nodules.

BACKGROUND: Artificial intelligence-based radiomic approaches have been shown to accurately evaluate indeterminate pulmonary nodules. With the expansi...