Radiology

Deep Learning based Collateral Scoring on Multi-Phase CTA in patients with acute ischemic stroke in MCA region.

BACKGROUND AND PURPOSE: Collateral circulation is a critical determinant of clinical outcomes in acute ischemic stroke (AIS) patients and plays a key role in patient selection for endovascular therapy. This study aimed to develop an automated method for assessing and quantifying collateral circulation on multi-phase CT angiography, aiming to reduce observer variability and improve diagnostic effic...

Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer's disease diagnosis.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder marked by neuronal loss, leading to cognitive and behavioral decline. With the aging global population, AD incidence and its socioeconomic burden are increasing. Developing effective early diagnostic methods is thus critical for improving patient outcomes and slowing disease progression. In this paper, an enhanced Particle Swarm ...

An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging.

Lung and colon cancers (LCC) are among the foremost reasons for human death and disease. Early analysis of this disorder contains various tests, namel...

AG-MS3D-CNN multiscale attention guided 3D convolutional neural network for robust brain tumor segmentation across MRI protocols.

Accurate segmentation of brain tumors from multimodal Magnetic Resonance Imaging (MRI) plays a critical role in diagnosis, treatment planning, and dis...

Prediction of Motor Symptom Progression of Parkinson's Disease Through Multimodal Imaging-Based Machine Learning.

The unrelenting progression of Parkinson's disease (PD) leads to severely impaired quality of life, with considerable variability in progression rates...

Multilayer perceptron deep learning radiomics model based on Gd-BOPTA MRI to identify vessels encapsulating tumor clusters in hepatocellular carcinoma: a multi-center study.

OBJECTIVES: The purpose of this study is to mainly develop a predictive model based on clinicoradiological and radiomics features from preoperative ga...

Performance of GPT-4 for automated prostate biopsy decision-making based on mpMRI: a multi-center evidence study.

BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) has significantly advanced prostate cancer (PCa) detection, yet decisions on invasive b...

Corona Dynamics of Nanoparticles and Their Functional Design Space in Molecular Sensing.

As nanomaterials increasingly interact with complex biological environments, understanding and designing their interfacial layers is critical for enab...

Imaging and pulmonary function techniques in ARDS diagnosis and management: current insights and challenges.

Acute Respiratory Distress Syndrome (ARDS) is a life-threatening condition characterized by acute onset of respiratory failure, which presents signifi...

Optical coherence tomography for early detection of crop infection.

BACKGROUND: Fungal diseases are among the most significant threats to global crop production, often leading to substantial yield losses. Early detecti...

Artificial Intelligence-Assisted Standard Plane Detection in Hip Ultrasound for Developmental Dysplasia of the Hip: A Novel Real-Time Deep Learning Approach.

Developmental dysplasia of the hip (DDH) includes a range of conditions caused by inadequate hip joint development. Early diagnosis is essential to pr...

Artifact-robust Deep Learning-based Segmentation of 3D Phase-contrast MR Angiography: A Novel Data Augmentation Approach.

This study presents a novel data augmentation approach to improve deep learning (DL)-based segmentation for 3D phase-contrast magnetic resonance angio...

Multimodal Optical Imaging Combined with Radiomic Analysis for Fibrotic Cardiac Tissue Investigation.

Understanding the process of fibrotic scarring of the myocardium is critical for the diagnosis and risk stratification of life-threatening cardiac dys...

MRI-based detection of multiple sclerosis using an optimized attention-based deep learning framework.

BACKGROUND: Multiple Sclerosis (MS) is a chronic neurological disorder affecting millions worldwide. Early detection is vital to prevent long-term dis...

Harnessing protein language model for structure-based discovery of highly efficient and robust PET hydrolases.

Plastic waste, particularly polyethylene terephthalate (PET), presents significant environmental challenges, driving extensive research into enzymatic...

Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture.

Small intracranial aneurysms (SIAs) (< 5 mm) are increasingly detected due to advanced imaging, but predicting rupture risk remains challenging. Ruptu...

Integrating artificial intelligence in healthcare: applications, challenges, and future directions.

Artificial intelligence (AI) has demonstrated remarkable potential in transforming medical diagnostics across various healthcare domains. This paper e...

A preliminary attempt to harmonize using physics-constrained deep neural networks for multisite and multiscanner MRI datasets (PhyCHarm).

In magnetic resonance imaging (MRI), variations in scan parameters and scanner specifications can result in differences in image appearance. To minimi...

Multi-modal convolutional neural network-based thyroid cytology classification and diagnosis.

BACKGROUND: The cytologic diagnosis of thyroid nodules' benign and malignant nature based on cytological smears obtained through ultrasound-guided fin...

Disease Classification of Pulmonary Xenon Ventilation MRI Using Artificial Intelligence.

RATIONALE AND OBJECTIVES: Hyperpolarized Xenon magnetic resonance imaging (MRI) measures the extent of lung ventilation by ventilation defect percent ...