Radiology

Dose Reduction in 4-Dimensional Computed Tomography Imaging: Breathing Signal-Guided Deep Learning-Driven Data Acquisition.

PURPOSE: Four-dimensional computed tomography (4D CT) imaging is essential for radiation therapy planning in thoracic tumors. However, current protocols tend to acquire more projection data than is strictly necessary for reconstructing the 4D CT, potentially leading to unnecessary radiation exposure and a misalignment with the ALARA (As Low As Reasonably Achievable) principle. We propose a deep le...

Decision Strategies in AI-Based Ensemble Models in Opportunistic Alzheimer's Detection from Structural MRI.

Alzheimer's disease (AD) is a neurodegenerative condition and the most common form of dementia. Recent developments in AD treatment call for robust diagnostic tools to facilitate medical decision-making. Despite progress for early diagnostic tests, there remains uncertainty about clinical use. Structural magnetic resonance imaging (MRI), as a readily available imaging tool in the current AD diagno...

Current State-of-the-Art 3D MRI Sequences for Assessing Bone Morphology with Emphasis on Cranial and Spinal Imaging: A Narrative Review.

Traditionally, CT has been the go-to method for visualizing bone structures, while MRI has been preferred for assessing soft tissues, because structur...

Artificial intelligence-assisted PET imaging for predicting neoadjuvant chemotherapy response in breast cancer: a systematic review and meta-analysis.

PURPOSE: This study aims to evaluate the performance of artificial intelligence (AI)-assisted PET imaging in predicting neoadjuvant chemotherapy (NAC)...

Habitat-aware radiomics and adaptive 2.5D deep learning predict treatment response and long-term survival in ESCC patients undergoing neoadjuvant chemoimmunotherapy.

PURPOSE: Current radiomic approaches inadequately resolve spatial intratumoral heterogeneity (ITH) in esophageal squamous cell carcinoma (ESCC), limit...

Concurrent AI assistance with LI-RADS classification for contrast enhanced MRI of focal hepatic nodules: a multi-reader, multi-case study.

PURPOSE: The Liver Imaging Reporting and Data System (LI-RADS) assessment is subject to inter-reader variability. The present study aimed to evaluate ...

Data fusion of medical imaging in neurological disorders.

Medical imaging plays a crucial role in the accurate diagnosis and prognosis of various medical conditions, with each modality offering unique and com...

Accuracy of AI-Based Algorithms in Pulmonary Embolism Detection on Computed Tomographic Pulmonary Angiography: An Updated Systematic Review and Meta-analysis.

Several artificial intelligence (AI) algorithms have been designed for detection of pulmonary embolism (PE) using computed tomographic pulmonary angio...

Cutting-edge nanobiosensors: Revolutionizing cancer diagnosis and enabling precision medicine.

Integrating nanotechnology and artificial intelligence (AI) revolutionizes cancer diagnostics, propelling precision medicine into a transformative era...

Harnessing Artificial Intelligence for Shoulder Ultrasonography: A Narrative Review.

Shoulder pain is a common musculoskeletal complaint requiring accurate imaging for diagnosis and management. Ultrasound is favored for its accessibili...

Interrelations between dopaminergic-, gabaergic- and glutamatergic neurotransmitters in antipsychotic-naïve psychosis patients and the association to initial treatment response.

Preclinical evidence points to disturbances in neural networks in psychosis involving interrelations between dopaminergic-, GABAergic- and glutamaterg...

Ex vivo human brain volumetry: Validation of MRI measurements.

PURPOSE: The volume of in vivo human brains is determined with various MRI measurement tools that have not been assessed against a gold standard. The ...

U-ConvNext: A Robust Approach to Glioma Segmentation in Intraoperative Ultrasound.

Intraoperative tumor imaging is critical to achieving maximal safe resection during neurosurgery, especially for low-grade glioma resection. Given the...

Training With Local Data Remains Important for Deep Learning MRI Prostate Cancer Detection.

OBJECTIVES: Domain shift has been shown to have a major detrimental effect on AI model performance however prior studies on domain shift for MRI prost...

Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications.

BACKGROUND: Gastric cancer (GC) remains a major global health concern, ranking as the fifth most prevalent malignancy and the fourth leading cause of ...

Enhancing interpretability of AI with radiomics-based deep neural network: proof of concept in the classification of Parkinsonian syndromes with 18F-FDG PET imaging.

OBJECTIVE: Interpretability and reproducibility remain major challenges in applying deep neural network (DNN) to neuroimaging-based diagnosis. This st...

Radiology report generation via visual-semantic ambivalence-aware network and focal self-critical sequence training.

Radiology report generation, which aims to provide accurate descriptions of both normal and abnormal regions, has been attracting growing research att...

Deep-Learning System for Automatic Measurement of the Femorotibial Rotational Angle on Lower-Extremity Computed Tomography.

To develop and validate a deep-learning-based algorithm for automatic identification of anatomical landmarks and calculating femoral and tibial versio...

Implementing a Resource-Light and Low-Code Large Language Model System for Information Extraction from Mammography Reports: A Pilot Study.

Large language models (LLMs) have been successfully used for data extraction from free-text radiology reports. Most current studies were conducted wit...

An economic scenario analysis of implementing artificial intelligence in BreastScreen Norway-Impact on radiologist person-years, costs and effects.

ObjectiveTo study the implications of implementing artificial intelligence (AI) as a decision support tool in the Norwegian breast cancer screening pr...