Radiology

Artificial intelligence system for identification of overlooked lung metastasis in abdominopelvic computed tomography scans of patients with malignancy.

PURPOSE: This study aimed to evaluate whether an artificial intelligence (AI) system can identify basal lung metastatic nodules examined using abdominopelvic computed tomography (CT) that were initially overlooked by radiologists.

An efficient ranking-based ensembled multiclassifier for neurodegenerative diseases classification using deep learning.

Neurodegenerative diseases are group of debilitating and progressive disorders that primarily affect the structure and functions of nervous system, leading to gradual loss of neurons and subsequent decline in cognitive, and behavioral activities. The two frequent diseases affecting the world's significant population falling in the above category are Alzheimer's disease (AD) and Parkinson's disease...

Precise ablation zone segmentation on CT images after liver cancer ablation using semi-automatic CNN-based segmentation.

BACKGROUND: Ablation zone segmentation in contrast-enhanced computed tomography (CECT) images enables the quantitative assessment of treatment success...

Artificial intelligence and advanced MRI techniques: A comprehensive analysis of diffuse gliomas.

INTRODUCTION: The complexity of diffuse gliomas relies on advanced imaging techniques like MRI to understand their heterogeneity. Utilizing the UCSF-P...

Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies.

Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not prac...

Reducing energy consumption in musculoskeletal MRI using shorter scan protocols, optimized magnet cooling patterns, and deep learning sequences.

OBJECTIVES: The unprecedented surge in energy costs in Europe, coupled with the significant energy consumption of MRI scanners in radiology department...

Artificial Intelligence in the Training of Radiology Residents: a Multicenter Randomized Controlled Trial.

The aim of the present study was to compare the effectiveness of AI-assisted training and conventional human training in clinical practice. This was a...

The Potential of Gemini and GPTs for Structured Report Generation based on Free-Text F-FDG PET/CT Breast Cancer Reports.

RATIONALE AND OBJECTIVE: To compare the performance of large language model (LLM) based Gemini and Generative Pre-trained Transformers (GPTs) in data ...

Advancements in supervised deep learning for metal artifact reduction in computed tomography: A systematic review.

BACKGROUND: Metallic artefacts caused by metal implants, are a common problem in computed tomography (CT) imaging, degrading image quality and diagnos...

Clinical impact of AI in radiology department management: a systematic review.

PURPOSE: Artificial intelligence (AI) has revolutionized medical diagnosis and treatment. Breakthroughs in diagnostic applications make headlines, but...

Low-dose computed tomography perceptual image quality assessment.

In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects...

Topology-preserving segmentation of abdominal muscle layers from ultrasound images.

BACKGROUND: In clinical anesthesia, precise segmentation of muscle layers from abdominal ultrasound images is crucial for identifying nerve block loca...

Artificial intelligence-assisted measurements of coronary computed tomography angiography parameters such as stenosis, flow reserve, and fat attenuation for predicting major adverse cardiac events in patients with coronary arterial disease.

Advancements in artificial intelligence (AI) offer promising tools for improving diagnostic accuracy and patient outcomes in cardiovascular medicine. ...

Classification of Alzheimer disease using DenseNet-201 based on deep transfer learning technique.

Alzheimer's disease (AD) is a brain illness that causes gradual memory loss. AD has no treatment and cannot be cured, so early detection is critical. ...

Multifunctional aggregation network of cell nuclei segmentation aiming histopathological diagnosis assistance: A new MA-Net construction.

Automated diagnostic systems can enhance the accuracy and efficiency of pathological diagnoses, nuclear segmentation plays a crucial role in computer-...

Precision meets generalization: Enhancing brain tumor classification via pretrained DenseNet with global average pooling and hyperparameter tuning.

Brain tumors pose significant global health concerns due to their high mortality rates and limited treatment options. These tumors, arising from abnor...

Accelerated chemical shift encoded cardiovascular magnetic resonance imaging with use of a resolution enhancement network.

BACKGROUND: Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial fat imaging. We sought to develop a deep learning...

Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review.

In recent years, many neuroimaging studies have applied artificial intelligence (AI) to facilitate existing challenges in Parkinson's disease (PD) dia...

Prediction of anhedonia in patients with first-episode schizophrenia using a Wavelet-ALFF-based Support vector regression model.

Anhedonia is one of the core features of the negative symptoms of schizophrenia and can be extremely burdensome. Our study applied resting-state funct...

Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction Using the Local Lipschitz.

Accurate image reconstruction is at the heart of diagnostics in medical imaging. Supervised deep learning-based approaches have been investigated for ...