Radiology

Latest AI and machine learning research in radiology for healthcare professionals.

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Impact of a computed tomography-based artificial intelligence software on radiologists' workflow for detecting acute intracranial hemorrhage.

PURPOSE: To assess the impact of a commercially available computed tomography (CT)-based artificial ...

Optical coherence tomography for early detection of crop infection.

BACKGROUND: Fungal diseases are among the most significant threats to global crop production, often ...

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 on...

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 r...

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 segmentat...

PGMI assessment in mammography: AI software versus human readers.

INTRODUCTION: The aim of this study was to evaluate human inter-reader agreement of parameters inclu...

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

Plastic waste, particularly polyethylene terephthalate (PET), presents significant environmental cha...

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....

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

Artificial intelligence (AI) has demonstrated remarkable potential in transforming medical diagnosti...

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 re...

Machine learning approach using radiomics features to distinguish odontogenic cysts and tumours.

Although most odontogenic lesions in the jaw are benign, treatment varies widely depending on the na...

Disease Classification of Pulmonary Xenon Ventilation MRI Using Artificial Intelligence.

RATIONALE AND OBJECTIVES: Hyperpolarized Xenon magnetic resonance imaging (MRI) measures the extent ...

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

BACKGROUND: The cytologic diagnosis of thyroid nodules' benign and malignant nature based on cytolog...

AI-enabled obstetric point-of-care ultrasound as an emerging technology in low- and middle-income countries: provider and health system perspectives.

BACKGROUND: In many low- and middle-income countries (LMICs), widespread access to obstetric ultraso...

Deep learning-based classification of parotid gland tumors: integrating dynamic contrast-enhanced MRI for enhanced diagnostic accuracy.

BACKGROUND: To evaluate the performance of deep learning models in classifying parotid gland tumors ...

Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography.

OBJECTIVE: To develop a multi-modality machine learning-based radiomics model utilizing Magnetic Res...

Medical slice transformer for improved diagnosis and explainability on 3D medical images with DINOv2.

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are essential clinical cross-sectional...

Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms.

Brain tumours (BTs) are severe neurological disorders. They affect more than 308,000 people each yea...

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