Radiology

A deep learning method for total-body dynamic PET imaging with dual-time-window protocols.

PURPOSE: Prolonged scanning durations are one of the primary barriers to the widespread clinical adoption of dynamic Positron Emission Tomography (PET). In this paper, we developed a deep learning algorithm that capable of predicting dynamic images from dual-time-window protocols, thereby shortening the scanning time.

Attention-based Fusion Network for Breast Cancer Segmentation and Classification Using Multi-modal Ultrasound Images.

OBJECTIVE: Breast cancer is one of the most commonly occurring cancers in women. Thus, early detection and treatment of cancer lead to a better outcome for the patient. Ultrasound (US) imaging plays a crucial role in the early detection of breast cancer, providing a cost-effective, convenient, and safe diagnostic approach. To date, much research has been conducted to facilitate reliable and effect...

Proximity adjusted centroid mapping for accurate detection of nuclei in dense 3D cell systems.

In the past decade, deep learning algorithms have surpassed the performance of many conventional image segmentation pipelines. Powerful models are now...

Deep Learning techniques to detect and analysis of multiple sclerosis through MRI: A systematic literature review.

Deep learning (DL) techniques represent a rapidly advancing field within artificial intelligence, gaining significant prominence in the detection and ...

Artificial intelligence-enhanced magnetic resonance imaging-based pre-operative staging in patients with endometrial cancer.

OBJECTIVE: Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. Th...

Effect of the Ultrasound-Guided Interscalene and Supraclavicular Blocks on the C4 Dermatome.

PURPOSE: The C4 dermatome anesthesia holds significance for arthroscopic shoulder surgery. However, the reliability of achieving C4 dermatome anesthes...

SMART: Development and Application of a Multimodal Multi-organ Trauma Screening Model for Abdominal Injuries in Emergency Settings.

RATIONALE AND OBJECTIVES: Effective trauma care in emergency departments necessitates rapid diagnosis by interdisciplinary teams using various medical...

Brain networks and intelligence: A graph neural network based approach to resting state fMRI data.

Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitiv...

Noncontrast MRI-based machine learning and radiomics signature can predict the severity of primary lower limb lymphedema.

OBJECTIVE: According to International Lymphology Society guidelines, the severity of lymphedema is determined by the difference in volume between the ...

Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis.

BACKGROUND AND AIM: The potential intricacy of skull fractures as well as the complexity of underlying anatomy poses diagnostic hurdles for radiologis...

Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.

Artificial Intelligence (AI) has been proposed to improve workflow for coronary artery calcium scoring (CACS), but simultaneous demonstration of impro...

Dynamic graph consistency and self-contrast learning for semi-supervised medical image segmentation.

Semi-supervised medical image segmentation endeavors to exploit a limited set of labeled data in conjunction with a substantial corpus of unlabeled da...

Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning Models.

OBJECTIVE: Segmentation of brain sulci in pre-term infants is crucial for monitoring their development. While magnetic resonance imaging has been used...

Predicting molecular subtypes of breast cancer based on multi-parametric MRI dataset using deep learning method.

PURPOSE: To develop a multi-parametric MRI model for the prediction of molecular subtypes of breast cancer using five types of breast cancer preoperat...

A quality assessment tool for focused abdominal sonography for trauma examinations using artificial intelligence.

BACKGROUND: Current tools to review focused abdominal sonography for trauma (FAST) images for quality have poorly defined grading criteria or are deve...

Deep learning-based prediction of tumor aggressiveness in RCC using multiparametric MRI: a pilot study.

OBJECTIVE: To investigate the value of multiparametric magnetic resonance imaging (MRI) as a non-invasive method to predict the aggressiveness of rena...

Radiomics and Artificial Intelligence Landscape for [F]FDG PET/CT in Multiple Myeloma.

[F]FDG PET/CT is a powerful imaging modality of high performance in multiple myeloma (MM) and is considered the appropriate method for assessing treat...

CMFNet: a cross-dimensional modal fusion network for accurate vessel segmentation based on OCTA data.

Optical coherence tomography angiography (OCTA) is a novel non-invasive retinal vessel imaging technique that can display high-resolution 3D vessel st...

Diagnosis of Fibrotic Interstitial Lung Diseases Based on the Combination of Label-Free Quantitative Multiphoton Fiber Histology and Machine Learning.

Interstitial lung disease (ILD), characterized by inflammation and fibrosis, often suffers from low diagnostic accuracy and consistency. Traditional h...

A Context-Dependent CNN-Based Framework for Multiple Sclerosis Segmentation in MRI.

Despite several automated strategies for identification/segmentation of Multiple Sclerosis (MS) lesions in Magnetic Resonance Imaging (MRI) being deve...