Radiology

Leveraging the non-contrast CT component of PET/CT: an AI-driven delta-radiomics approach to monitor treatment response in metastatic breast cancer.

PURPOSE: 18 F-FDG PET/CT is the standard modality for monitoring treatment response in metastatic breast cancer. This study aims to evaluate the predictive value of delta-radiomics derived solely from the low-dose, non-contrast CT component acquired during routine PET/CT imaging-without requiring an additional dedicated CT examination or extra contrast administration-for monitoring response to CDK...

A narrative review of the role of AI and machine learning in enhancing cauda equina syndrome diagnosis.

INTRODUCTION: Cauda Equina Syndrome (CES) is a neurological emergency requiring rapid diagnosis. Traditional diagnostic methods face challenges due to variable presentations. This review evaluates the clinical performance, limitations, and validation needs of artificial intelligence and machine learning (AI/ML) compared to traditional CES diagnostic approaches. METHODS: A systematic PRISMA 2020 se...

Unsupervised anomaly detection for longitudinal comparison in whole-body PET/CT images.

PURPOSE: This study investigates the utility of unsupervised anomaly detection for longitudinal comparison of whole-body 18F-fluorodeoxyglucose (FDG)-...

Evaluating artificial intelligence-generated synthesized mammography as a standalone alternative to digital mammography with or without tomosynthesis.

PURPOSE: To evaluate whether standalone synthesized mammography (SM) can maintain or improve diagnostic accuracy while reducing reading time and radia...

Advances in quantum dot-based fluorescent biosensors for enzyme detection: mechanistic insights and applications in disease.

It is imperative to develop precise detection tools for enzymes, as key biomarkers in disease pathogenesis, to fuel progress in clinical diagnostics a...

Optimizing Renal Fibrosis Assessment in Patients with Chronic Kidney Disease: Feature and Decision-Level Fusion in Machine Learning Using Clinical and Ultrasound Information.

PURPOSE: Machine learning has been extensively applied in nephrology. This study aims to evaluate and compare the effectiveness of various information...

Volumetric Analysis of Deep Cerebellar and Thalamic Nuclei in Spastic Cerebral Palsy.

OBJECTIVE: Cerebral palsy (CP) encompasses various movement disorders, most commonly spasticity, although other motor phenotypes such as dystonia may ...

DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT.

Following successful large-vessel recanalization via endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), some patients experience a compl...

CTransFuse: A Hybrid Transformer-CNN Framework for Precise Meniscus Segmentation and Lesion Identification.

Meniscal tears and degenerative changes are the most common pathologies affecting the knee joint. In magnetic resonance imaging (MRI), these lesions o...

Patients' Perspectives on the Implementation of AI in Radiological Diagnostics: Focus Group Study.

BACKGROUND: Rapid developments in artificial intelligence (AI) will enable its widespread use in radiological diagnostics in the near future. Patients...

Whole-body reduced-dose dual-energy CT with deep learning image reconstruction for detection of osteolytic lesions in multiple myeloma.

INTRODUCTION: Deep learning image reconstruction (DLIR) has been incorporated into dual-energy CT (DECT) to improve image quality. However, its applic...

Prediction and Clinical Application of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma Based on Multi-modal Ultrasound Feature Fusion: A Multi-center Study.

OBJECTIVE: To develop and validate a deep learning model integrating multi-modal ultrasound information from B-mode ultrasound (BMUS) and strain elast...

External validity of deep learning solution for spontaneous intracranial hemorrhage detection on head CT scans.

We externally validated the performance of deep learning (DL) solution for detection of spontaneous intracerebral (ICH), intraventricular (IVH) and su...

A clinical-radiomics model based on multiparametric MRI for discriminating solitary primary spinal tumors from solitary spinal metastases.

OBJECTIVE: To develop and validate a clinical-radiomics model based on multiparametric MRI for differentiating solitary primary spinal tumors from sol...

Artificial intelligence for oral cancer diagnosis: a systematic review and meta-analysis of image-based and non-imaging models.

BACKGROUND: Artificial intelligence (AI) is increasingly recognized as a valuable tool for the early detection and prognosis of oral cancer, addressin...

Integration of ANN-GA optimization and multi-scale mechanistic analysis for ultrasound-assisted enzymatic extraction of flavonoids from Cortex Mori.

Cortex Mori (CM), a valuable medicinal plant with abundant flavonoids, exhibits diverse biological and pharmacological activities. This work aims to d...

Advances in Nano-Enabled Biosensing Technology for Gastric Cancer Diagnosis: Mechanistic Insights and Translational Perspectives.

Gastric cancer (GC) remains a critical burden on healthcare, affecting millions of people annually. Helicobacter pylori infection is a critical contri...

Nuclear DNA-Gated Electrochemiluminescence Microscopy with Deep Learning for Single-Cell Apoptosis Profiling.

Accurately assessing how individual cells respond to anticancer agents remains challenging because most assays provide bulk or binary readouts and can...

From conventional imaging software to artificial intelligence: tools for stone volume assessment in urolithiasis. Α review by the EAU and YAU sections of endourology.

INTRODUCTION: Accurate assessment of stone burden is fundamental in urolithiasis, as it directly influences treatment selection and prognostic evaluat...

Advances in machine learning for cardiac event prediction: where can we still improve?

INTRODUCTION: Artificial intelligence (AI) is playing a transformative role in cardiovascular care by enabling more precise prediction of adverse clin...