Radiology

Predicting Cardiac Magnetic Resonance-Derived Ejection Fraction from Echocardiogram Via Deep Learning Approach in Tetralogy of Fallot.

Systolic function assessment is essential in children with congenital heart disease. Traditional methods of echocardiographic left ventricular ejection fraction (LVEF) estimation might overestimate systolic function compared to the gold standard of cardiac magnetic resonance imaging (CMR), especially in Tetralogy of Fallot (TOF). Deep learning technologies such as EchoNet-Dynamic offer more consis...

Sustainable gelatin extraction from poultry skin-head-feet blend: An ultrasound-assisted approach.

The study investigated gelatin extraction from chicken skin-head-feet (SHF) blend using conventional and ultrasound-assisted methods with food-grade acetic and citric acids. Ultrasound pretreatment was introduced as an intervention in the extraction process, eliminating the need for alkali hydrolysis and significantly reducing the processing time. The gelatin yield, gel clarity, textural parameter...

Enhancing Blood-Brain Barrier Penetration Prediction by Machine Learning-Based Integration of Novel and Existing, In Silico and Experimental Molecular Parameters from a Standardized Database.

Predicting blood-brain barrier (BBB) penetration is crucial for developing central nervous system (CNS) drugs, representing a significant hurdle in su...

Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images.

Large language models have shown efficacy across multiple medical tasks. However, their value in the assessment of longitudinal follow-up computed tom...

AI-Generated Synthetic STIR of the Lumbar Spine from T1 and T2 MRI Sequences Trained with Open-Source Algorithms.

BACKGROUND AND PURPOSE: Lumbar spine MRIs can be time consuming, stressful for patients, and costly to acquire. In this work, we train and evaluate op...

Detection of obstetric anal sphincter injuries using machine learning-assisted impedance spectroscopy: a prospective, comparative, multicentre clinical study.

To evaluate the clinical performance and safety of the ONIRY system for obstetric anal sphincter injuries (OASI) detection versus three-dimensional en...

Prediction of Lymph Node Metastasis in Lung Cancer Using Deep Learning of Endobronchial Ultrasound Images With Size on CT and PET-CT Findings.

BACKGROUND AND OBJECTIVE: Echo features of lymph nodes (LNs) influence target selection during endobronchial ultrasound-guided transbronchial needle a...

Deep learning based image enhancement for dynamic non-Cartesian MRI: Application to "silent" fMRI.

Radial based non-Cartesian sequences may be used for silent functional MRI examinations particularly in settings where scanner noise could pose issues...

Performance of a point-of-care ultrasound platform for artificial intelligence-enabled assessment of pulmonary B-lines.

BACKGROUND: The incorporation of artificial intelligence (AI) into point-of-care ultrasound (POCUS) platforms has rapidly increased. The number of B-l...

MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival.

We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics mode...

Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning.

Predicting low nuclear grade DCIS before surgery can improve treatment choices and patient care, thereby reducing unnecessary treatment. Due to the hi...

Development and validation of a deep learning algorithm for prediction of pediatric recurrent intussusception in ultrasound images and radiographs.

PURPOSES: To develop a predictive model for recurrent intussusception based on abdominal ultrasound (US) images and abdominal radiographs.

Texture-based probability mapping for automatic assessment of myocardial injury in late gadolinium enhancement images after revascularized STEMI.

BACKGROUND: Late Gadolinium-enhancement in cardiac magnetic resonance imaging (LGE-CMR) is the gold standard for assessing myocardial infarction (MI) ...

Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges.

This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, ...

Use of deep learning-based high-resolution magnetic resonance to identify intracranial and extracranial symptom-related plaques.

This study aims to develop a deep learning model using high-resolution vessel wall imaging (HR-VWI) to differentiate symptom-related intracranial and ...

Artificial intelligence-driven 3D MRI of lumbosacral nerve root anomalies: accuracy, incidence, and clinical utility.

PURPOSE: Lumbosacral nerve root anomalies are relatively rare but can be a risk factor for intraoperative nerve injury. However, it is often difficult...

Denoising Diffusion Probabilistic Model to Simulate Contrast-enhanced spinal MRI of Spinal Tumors: A Multi-Center Study.

RATIONALE AND OBJECTIVES: To generate virtual T1 contrast-enhanced (T1CE) sequences from plain spinal MRI sequences using the denoising diffusion prob...

Comprehensive evaluation of pipelines for classification of psychiatric disorders using multi-site resting-state fMRI datasets.

Objective classification biomarkers that are developed using resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contri...

Deep Learning-Assisted Diagnosis of Malignant Cerebral Edema Following Endovascular Thrombectomy.

BACKGROUND: Malignant cerebral edema (MCE) is a significant complication following endovascular thrombectomy (EVT) in the treatment of acute ischemic ...

Artificial intelligence quantification and experienced reader computed tomography analysis for differentiating normal from minimally and mildly diseased coronary arteries: an early real-world compatibility study.

Differentiating normal from minimally and mildly diseased coronary arteries on coronary computed tomographic angiography (CCTA) is crucial, impacting ...