Radiology

Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review.

PURPOSE: To summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for future breast cancer risk prediction.

Molecular Graph-Based Deep Learning Algorithm Facilitates an Imaging-Based Strategy for Rapid Discovery of Small Molecules Modulating Biomolecular Condensates.

Biomolecular condensates are proposed to cause diseases, such as cancer and neurodegeneration, by concentrating proteins at abnormal subcellular loci. Imaging-based compound screens have been used to identify small molecules that reverse or promote biomolecular condensates. However, limitations of conventional imaging-based methods restrict the screening scale. Here, we used a graph convolutional ...

A Machine Learning-Based Unenhanced Radiomics Approach to Distinguishing Between Benign and Malignant Breast Lesions Using T2-Weighted and Diffusion-Weighted MRI.

BACKGROUND: Breast MRI has been recommended as supplemental screening tool to mammography and breast ultrasound of breast cancer by international guid...

Predictive value and potential association of PET/CT radiomics on lymph node metastasis of cervical cancer.

OBJECTIVE: Due to the information-rich nature of positron emission tomography/computed tomography (PET/CT) images, the authors hope to explore radiomi...

Automatic Detection and Classification of Modic Changes in MRI Images Using Deep Learning: Intelligent Assisted Diagnosis System.

OBJECTIVE: Modic changes (MCs) are the most prevalent classification system for describing intravertebral MRI signal intensity changes. However, inter...

Deep learning performance for detection and classification of microcalcifications on mammography.

BACKGROUND: Breast cancer screening through mammography is crucial for early detection, yet the demand for mammography services surpasses the capacity...

Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning.

PET-based Alzheimer's disease (AD) assessment has many limitations in large-scale screening. Non-invasive techniques such as resting-state functional ...

Cross-Domain Unpaired Learning for Low-Dose CT Imaging.

Supervised deep-learning techniques with paired training datasets have been widely studied for low-dose computed tomography (LDCT) imaging with excell...

Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects.

The integration of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging techniques with machine learning ...

Mapping adipose tissue in short-axis echocardiograms using spectral analysis.

The number one cause of death in the United States is consistently cardiovascular disease (CVD). Studies have proven that the buildup of cardiac adipo...

Deep learning-based age estimation from clinical Computed Tomography image data of the thorax and abdomen in the adult population.

Aging is an important risk factor for disease, leading to morphological change that can be assessed on Computed Tomography (CT) scans. We propose a de...

Task-based assessment of resolution properties of CT images with a new index using deep convolutional neural network.

In this study, we propose a method for obtaining a new index to evaluate the resolution properties of computed tomography (CT) images in a task-based ...

An investigation study on the interpretation of ultrasonic medical reports using OpenAI's GPT-3.5-turbo model.

OBJECTIVES: Ultrasound medical reports are an important means of diagnosing diseases and assessing treatment effectiveness. However, their professiona...

A deep learning-based automated algorithm for labeling coronary arteries in computed tomography angiography images.

OBJECTIVE: Using two three-dimensional U-Net architectures for myocardium structure extraction and a distance transformation algorithm specifically fo...

Photon Absorption Remote Sensing Imaging of Breast Needle Core Biopsies Is Diagnostically Equivalent to Gold Standard H&E Histologic Assessment.

Photon absorption remote sensing (PARS) is a new laser-based microscope technique that permits cellular-level resolution of unstained fresh, frozen, a...

CT scan pancreatic cancer segmentation and classification using deep learning and the tunicate swarm algorithm.

Pancreatic cancer (PC) is a very lethal disease with a low survival rate, making timely and accurate diagnoses critical for successful treatment. PC c...

Advances in Fetal Cardiac Imaging and Intervention.

The field of fetal cardiology has evolved significantly in recent years. This review focuses on specific advances in fetal cardiac imaging and interve...

Quantitative image signature and machine learning-based prediction of outcomes in cerebral cavernous malformations.

PURPOSE: There is increasing interest in novel prognostic tools and predictive biomarkers to help identify, with more certainty, cerebral cavernous ma...

Fetal biometry and amniotic fluid volume assessment end-to-end automation using Deep Learning.

Fetal biometry and amniotic fluid volume assessments are two essential yet repetitive tasks in fetal ultrasound screening scans, aiding in the detecti...

Deep learning system for automated detection of posterior ligamentous complex injury in patients with thoracolumbar fracture on MRI.

This study aimed to develop a deep learning (DL) algorithm for automated detection and localization of posterior ligamentous complex (PLC) injury in p...