Radiology

Automatic pipeline for segmentation of LV myocardium on quantitative MR T1 maps using deep learning model and computation of radial T1 and ECV values.

Native T1 mapping is a non-invasive technique used for early detection of diffused myocardial abnormalities, and it provides baseline tissue characterization. Post-contrast T1 mapping enhances tissue differentiation, enables extracellular volume (ECV) calculation, and improves myocardial viability assessment. Accurate and precise segmenting of the left ventricular (LV) myocardium on T1 maps is cru...

Diagnostic performances of Claude 3 Opus and Claude 3.5 Sonnet from patient history and key images in Radiology's "Diagnosis Please" cases.

PURPOSE: The diagnostic performance of large language artificial intelligence (AI) models when utilizing radiological images has yet to be investigated. We employed Claude 3 Opus (released on March 4, 2024) and Claude 3.5 Sonnet (released on June 21, 2024) to investigate their diagnostic performances in response to the Radiology's Diagnosis Please quiz questions.

Fusing multi-scale functional connectivity patterns via Multi-Branch Vision Transformer (MB-ViT) for macaque brain age prediction.

Brain age (BA) is defined as a measure of brain maturity and could help characterize both the typical brain development and neuropsychiatric disorders...

Recent trends in AI applications for pelvic MRI: a comprehensive review.

Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum....

Two-step optimization for accelerating deep image prior-based PET image reconstruction.

Deep learning, particularly convolutional neural networks (CNNs), has advanced positron emission tomography (PET) image reconstruction. However, it re...

Gated SPECT-Derived Myocardial Strain Estimated From Deep-Learning Image Translation Validated From N-13 Ammonia PET.

RATIONALE AND OBJECTIVES: This study investigated the use of deep learning-generated virtual positron emission tomography (PET)-like gated single-phot...

Intelligent skin-removal photoacoustic computed tomography for human based on deep learning.

Photoacoustic computed tomography (PACT) has centimeter-level imaging ability and can be used to detect the human body. However, strong photoacoustic ...

Transforming Health Care Landscapes: The Lever of Radiology Research and Innovation on Emerging Markets Poised for Aggressive Growth.

Advances in radiology are crucial not only to the future of the field but to medicine as a whole. Here, we present three emerging areas of medicine th...

Aberrant migration features in primary skin fibroblasts of Huntington's disease patients hold potential for unraveling disease progression using an image based machine learning tool.

Huntington's disease (HD) is a complex neurodegenerative disorder with considerable heterogeneity in clinical manifestations. While CAG repeat length ...

The stroke outcome optimization project: Acute ischemic strokes from a comprehensive stroke center.

Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. Publicly sharing thes...

Soul: An OCTA dataset based on Human Machine Collaborative Annotation Framework.

Branch retinal vein occlusion (BRVO) is the most prevalent retinal vascular disease that constitutes a threat to vision due to increased venous pressu...

Evaluation of multiple deep neural networks for detection of intracranial dural arteriovenous fistula on susceptibility weighted angiography imaging.

BACKGROUND: The natural history of intracranial dural arteriovenous fistula (DAVF) is variable and early diagnosis is crucial in order to positively i...

Development of machine learning models for fractional flow reserve prediction in angiographically intermediate coronary lesions.

BACKGROUND: Fractional flow reserve (FFR) represents the gold standard in guiding the decision to proceed or not with coronary revascularization of an...

Decoding pulsatile patterns of cerebrospinal fluid dynamics through enhancing interpretability in machine learning.

Analyses of complex behaviors of Cerebrospinal Fluid (CSF) have become increasingly important in diseases diagnosis. The changes of the phase-contrast...

Improved microvascular imaging with optical coherence tomography using 3D neural networks and a channel attention mechanism.

Skin microvasculature is vital for human cardiovascular health and thermoregulation, but its imaging and analysis presents significant challenges. Sta...

Integrating MRI-based radiomics and clinicopathological features for preoperative prognostication of early-stage cervical adenocarcinoma patients: in comparison to deep learning approach.

OBJECTIVES: The roles of magnetic resonance imaging (MRI) -based radiomics approach and deep learning approach in cervical adenocarcinoma (AC) have no...

Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding.

Brain tumor detection in clinical applications is a complex and challenging task due to the intricate structures of the human brain. Magnetic Resonanc...

A novel assessment of whole-mount Gleason grading in prostate cancer to identify candidates for radical prostatectomy: a machine learning-based multiomics study.

: This study aims to assess whole-mount Gleason grading (GG) in prostate cancer (PCa) accurately using a multiomics machine learning (ML) model and to...

Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model.

OBJECTIVES: Radiomics, a recently developed image-processing technology, holds potential in medical diagnostics. This study aimed to propose a machine...

Automatic segmentation of the maxillary sinus on cone beam computed tomographic images with U-Net deep learning model.

BACKGROUND: Medical imaging segmentation is the use of image processing techniques to expand specific structures or areas in medical images. This tech...