Radiology

Phantom-Based Ultrasound-ECG Deep Learning Framework for Prospective Cardiac Computed Tomography.

OBJECTIVE: We present the first multimodal deep learning framework combining ultrasound (US) and electrocardiography (ECG) data to predict cardiac quiescent periods (QPs) for optimized computed tomography angiography gating (CTA).

Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies.

Multiplexed imaging techniques require identifying different cell types in the tissue. To utilize their potential for cellular and molecular analysis, high throughput and accurate analytical approaches are needed in parsing vast amounts of data, particularly in clinical settings. Nuclear segmentation errors propagate in all downstream steps of cell phenotyping and single-cell spatial analyses. Her...

Machine learning-based hemodynamics quantitative assessment of pulmonary circulation using computed tomographic pulmonary angiography.

BACKGROUND: Pulmonary hypertension (pH) is a malignant pulmonary circulation disease. Right heart catheterization (RHC) is the gold standard procedure...

Using Deep learning to Predict Cardiovascular Magnetic Resonance Findings from Echocardiography Videos.

BACKGROUND: Echocardiography is the most common modality for assessing cardiac structure and function. While cardiac magnetic resonance (CMR) imaging ...

Strategies for Treatment De-escalation in Metastatic Renal Cell Carcinoma.

Immune checkpoint inhibitors (ICIs) and targeted therapies have revolutionized the management of metastatic renal cell carcinoma (mRCC). Currently, th...

Developing an ML-Based Pretest Probability Model of Obstructive CAD in Patients With Stable Chest Pain.

BACKGROUND: Updated pretest probability models (ESC2019, the PTP model supported by the European Society of Cardiology after a pooled analysis; and RF...

Multi-center evaluation of radiomics and deep learning to stratify malignancy risk of IPMNs.

Distinguishing high-risk intraductal papillary mucinous neoplasms (IPMNs), pancreatic cysts requiring surgery, from low-risk lesions remains a clinica...

An Information-Extreme Algorithm for Universal Nuclear Feature-Driven Automated Classification of Breast Cancer Cells.

Breast cancer diagnosis heavily relies on histopathological assessment, which is prone to subjectivity and inefficiency, especially with whole-slide ...

A Hybrid Model of Feature Extraction and Dimensionality Reduction Using ViT, PCA, and Random Forest for Multi-Classification of Brain Cancer.

The brain serves as the central command center for the nervous system in the human body and is made up of nerve cells known as neurons. When these ne...

18F-FDG PET/CT Radiomics for Predicting Therapy Response in Primary Mediastinal B-Cell Lymphoma: A Bi-Centric Pilot Study.

PURPOSE: This bi-centric pilot study investigates the predictive value of pre-treatment [F]FDG PET/CT radiomics for assessing therapy response in prim...

Brain Tumour Detection Using VGG-Based Feature Extraction With Modified DarkNet-53 Model.

The objective of AI research and development is to create intelligent systems capable of performing tasks and reasoning like humans. Artificial intell...

Predicting abnormal fetal growth using deep learning.

Ultrasound assessment of fetal size and growth is the mainstay of monitoring fetal well-being during pregnancy, as being small for gestational age (SG...

The use of imaging in the diagnosis and treatment of thromboembolic pulmonary hypertension.

Chronic thromboembolic pulmonary hypertension (CTEPH) is a potentially life-threatening condition, classified as group 4 pulmonary hypertension (PH), ...

Deep learning reconstruction for improved image quality of ultra-high-resolution brain CT angiography: application in moyamoya disease.

PURPOSE: To investigate vessel delineation and image quality of ultra-high-resolution (UHR) CT angiography (CTA) reconstructed using deep learning rec...

Deep learning enables fast and accurate quantification of MRI-guided near-infrared spectral tomography for breast cancer diagnosis.

The utilization of magnetic resonance (MR) im-aging to guide near-infrared spectral tomography (NIRST) shows significant potential for improving the s...

Free-running isotropic three-dimensional cine magnetic resonance imaging with deep learning image reconstruction.

BACKGROUND: Cardiovascular magnetic resonance (CMR) cine imaging is the gold standard for assessing ventricular volumes and function. It typically req...

A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America.

This scoping review aims to identify regulator-approved ophthalmic image analysis artificial intelligence as a medical device (AIaMD) in three jurisdi...

Integrating multimodal data to predict the progression of hormone-sensitive prostate cancer.

Identifying the population at risk of rapid progression from hormone-sensitive prostate cancer (HSPC) to lethal castration-resistant prostate cancer (...

Deep Learning CAIPIRINHA-VIBE Improves and Accelerates Head and Neck MRI.

RATIONALE AND OBJECTIVES: The aim of this study was to evaluate image quality for contrast-enhanced (CE) neck MRI with a deep learning-reconstructed V...

Optimizing Artificial Intelligence Thresholds for Mammographic Lesion Detection: A Retrospective Study on Diagnostic Performance and Radiologist-Artificial Intelligence Discordance.

Artificial intelligence (AI)-based systems are increasingly being used to assist radiologists in detecting breast cancer on mammograms. However, appl...