Radiology

Deep learning-based binary classification of beta-amyloid plaques using 18 F florapronol PET.

PURPOSE: This study aimed to investigate a deep learning model to classify amyloid plaque deposition...

AI in the clinical management of GA: A novel therapeutic universe requires novel tools.

Regulatory approval of the first two therapeutic substances for the management of geographic atrophy...

The application of machine learning for identifying frailty in older patients during hospital admission.

BACKGROUND: Early identification of frail patients and early interventional treatment can minimize t...

An open-source fine-tuned large language model for radiological impression generation: a multi-reader performance study.

BACKGROUND: The impression section integrates key findings of a radiology report but can be subjecti...

Deep-learning model accurately classifies multi-label lung ultrasound findings, enhancing diagnostic accuracy and inter-reader agreement.

Despite the increasing use of lung ultrasound (LUS) in the evaluation of respiratory disease, operat...

Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks.

The standard method for identifying active Brown Adipose Tissue (BAT) is [F]-Fluorodeoxyglucose ([F]...

Detection of breast cancer in digital breast tomosynthesis with vision transformers.

Digital Breast Tomosynthesis (DBT) has revolutionized more traditional breast imaging through its th...

Deep learning-based segmentation for high-dose-rate brachytherapy in cervical cancer using 3D Prompt-ResUNet.

To develop and evaluate a 3D Prompt-ResUNet module that utilized the prompt-based model combined wit...

An Artificial Intelligence Algorithm Integrated into the Clinical Workflow Can Ensure High Quality Acute Intracranial Hemorrhage CT Diagnostic.

PURPOSE: Intracranial hemorrhage (ICH) is a life-threatening condition requiring rapid diagnostic an...

AngioPy Segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation.

BACKGROUND: Quantitative coronary angiography (QCA) typically employs traditional edge detection alg...

Explainable biology for improved therapies in precision medicine: AI is not enough.

Technological advances and high-throughput bio-chemical assays are rapidly changing ways how we form...

Using Natural Language Processing (GPT-4) for Computed Tomography Image Analysis of Cerebral Hemorrhages in Radiology: Retrospective Analysis.

BACKGROUND: Cerebral hemorrhage is a critical medical condition that necessitates a rapid and precis...

A Large Language Model to Detect Negated Expressions in Radiology Reports.

Natural language processing (NLP) is crucial to extract information accurately from unstructured tex...

Automatic Segmentation of Ultrasound-Guided Quadratus Lumborum Blocks Based on Artificial Intelligence.

Ultrasound-guided quadratus lumborum block (QLB) technology has become a widely used perioperative a...

Cross-site Validation of AI Segmentation and Harmonization in Breast MRI.

This work aims to perform a cross-site validation of automated segmentation for breast cancers in MR...

MRI-based deep learning for differentiating between bipolar and major depressive disorders.

Mood disorders, particularly bipolar disorder (BD) and major depressive disorder (MDD), manifest cha...

Raman fiber-optic probe for rapid diagnosis of gastric and esophageal tumors with machine learning analysis or similarity assessments: a comparative study.

Gastric and esophageal cancers, the predominant forms of upper gastrointestinal malignancies, contri...

Generative AI and large language models in nuclear medicine: current status and future prospects.

This review explores the potential applications of Large Language Models (LLMs) in nuclear medicine,...

Radiomics prediction models of left atrial appendage hypercoagulability based on machine learning algorithms: an exploration about cardiac computed tomography angiography imaging.

Transesophageal echocardiography (TEE) is the standard method for diagnosing left atrial appendage (...

Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients' electrocardiograms.

We develop a machine learning (ML) model using electrocardiography (ECG) to predict myocardial blood...

Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy.