Radiology

Artificial intelligence-assisted left ventricular global longitudinal strain assessment in patients with acute myocardial infarction: a RESUS-AMI trial sub-analysis.

PURPOSE: The aim of this sub-analysis of the RESUS-AMI trial was to evaluate the correlation of artificial intelligence (AI)-assisted echocardiographic global longitudinal strain (GLS) assessments with infarct size, left ventricular ejection fraction (LVEF) and volumes from cardiac magnetic resonance (CMR) imaging, in patients undergoing primary percutaneous coronary intervention for ST-elevation ...

Deep learning based automated left atrial segmentation and flow quantification of real time phase contrast MRI in patients with atrial fibrillation.

Real time 2D phase contrast (RTPC) MRI is useful for flow quantification in atrial fibrillation (AF) patients, but data analysis requires time-consuming anatomical contouring for many cardiac time frames. Our goal was to develop a convolutional neural network (CNN) for fully automated left atrial (LA) flow quantification. Forty-four AF patients underwent cardiac MRI including LA RTPC, collecting a...

Deep learning for quality assessment of axial T2-weighted prostate MRI: a tool to reduce unnecessary rescanning.

BACKGROUND: T2-weighted images are a critical component of prostate magnetic resonance imaging (MRI), and it would be useful to automatically assess i...

Development of an artificial intelligence powered software for automated analysis of skeletal muscle ultrasonography.

Muscle ultrasound has high utility in clinical practice and research; however, the main challenges are the training and time required for manual analy...

Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images.

Brain tumor detection is essential for early diagnosis and successful treatment, both of which can significantly enhance patient outcomes. To evaluate...

Deep learning radiopathomics predicts targeted therapy sensitivity in EGFR-mutant lung adenocarcinoma.

BACKGROUND: Ttyrosine kinase inhibitors (TKIs) represent the standard first-line treatment for patients with epidermal growth factor receptor (EGFR)-m...

Deep learning-assisted detection of meniscus and anterior cruciate ligament combined tears in adult knee magnetic resonance imaging: a crossover study with arthroscopy correlation.

AIM: We aimed to compare the diagnostic performance of physicians in the detection of arthroscopically confirmed meniscus and anterior cruciate ligame...

A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance Neurography with Modified Hiking Optimization Algorithm.

RATIONALE AND OBJECTIVES: Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper...

Comparative examination of the chemistry and biology of AI-driven gold NPs in Theranostics: New insights into biosensing, bioimaging, genomics, diagnostics, and therapy.

Integrating artificial intelligence (AI) with nanomedicine is transforming Theranostics, driving advances in biosensing, bioimaging, genomics, diagnos...

MED-NCA: Bio-inspired medical image segmentation.

The reliance on computationally intensive U-Net and Transformer architectures significantly limits their accessibility in low-resource environments, c...

Retinal Ischemic Perivascular Lesions (RIPLs) as Potential Biomarkers for Systemic Vascular Diseases: A Narrative Review of the Literature.

Retinal ischemic perivascular lesions (RIPLs) are characteristic focal thinning of the inner nuclear layer, with an upward expansion of the outer nucl...

Mexican dataset of digital mammograms (MEXBreast) with suspicious clusters of microcalcifications.

Breast cancer is one of the most prevalent cancers affecting women worldwide. Early detection and treatment are crucial in significantly reducing mort...

Construction of a Multimodal Machine Learning Model for Papillary Thyroid Carcinoma Based on Pathomics and Ultrasound Radiomics Dataset.

The use of machine learning to integrate and analyse multimodal information has broad prospects for enhancing the precision of tumour diagnosis. Our s...

F-FDG PET/CT-based deep learning models and a clinical-metabolic nomogram for predicting high-grade patterns in lung adenocarcinoma.

BACKGROUND: To develop and validate deep learning (DL) and traditional clinical-metabolic (CM) models based on 18 F-FDG PET/CT images for noninvasivel...

[Artificial intelligence and ultrasound in fetal medicine].

Ultrasound is essential in fetal medicine for diagnosing and monitoring, but it requires extensive training. Artificial intelligence (AI) shows a grea...

Application and optimization of the U-Net++ model for cerebral artery segmentation based on computed tomographic angiography images.

Accurate segmentation of cerebral arteries on computed tomography angiography (CTA) images is essential for the diagnosis and management of cerebrovas...

Biological age prediction in schizophrenia using brain MRI, gut microbiome and blood data.

The study of biological age prediction using various biological data has been widely explored. However, single biological data may offer limited insig...

The use of a convolutional neural network to automate radiologic scoring of computed tomography of paranasal sinuses.

BACKGROUND: Chronic rhinosinusitis (CRS) is diagnosed with symptoms and objective endoscopy or computed tomography (CT). The Lund-Mackay score (LMS) i...

Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression.

Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (...

Fast and automatic coronary artery segmentation using nnU-Net for non-contrast enhanced magnetic resonance coronary angiography.

Non-contrast enhanced magnetic resonance coronary angiography (MRCA) is a promising coronary heart disease screening modality. However, its clinical a...