Radiology

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

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A Case for Automated Segmentation of MRI Data in Milder Neurodegenerative Diseases

Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool f...

The Clinical Value of ChatGPT for Epilepsy Presurgical Decision Making: Systematic Evaluation on Seizure Semiology Interpretation

For patients with drug-resistant focal epilepsy (DRE), surgical resection of the epileptogenic zone ...

DUNE: a versatile neuroimaging encoder captures brain complexity across three major diseases: cancer, dementia and schizophrenia

Magnetic resonance images (MRI) of the brain exhibit high dimensionality that pose significant chall...

Segmentation-Free Pretherapeutic Assessment of BRAF-Status in Pediatric Low-Grade Gliomas

BRAF status is crucial for treating pediatric low-grade gliomas (pLGG) and can be assessed non-invas...

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...

Automating Imaging Biomarker Analysis for Knee Osteoarthritis Using an Open-Source MRI-Based Deep Learning Pipeline

Osteoarthritis (OA) is a leading cause of chronic disability worldwide, with knee OA being the most ...

Predicting Prostate Cancer Without a Prostate: A Potential Problem with AI

Machine learning (ML) algorithms have demonstrated great potential for the identification and classi...

Deep generative models for vessel segmentation in CT angiography of the brain

Automated vessel segmentation in brain CT angiography (CTA) remains challenging despite the potentia...

A universal translator for AI scores: Providing context using error

Artificial intelligence (AI) programs in radiology typically provide a numeric score for each case t...

Predicting Antiseizure Medication Outcomes in Early Diagnosed Epilepsy: A Multimodal Framework Using EEG, MRI, and Clinical Data

Accurate prediction of antiseizure medication (ASM) outcomes is crucial for optimising epilepsy trea...

Extracting Pulmonary Embolism Diagnoses from Radiology Impressions Using GPT-4o: A Large Language Model Evaluation Study

Pulmonary embolism (PE) is a critical condition requiring rapid diagnosis to reduce mortality. Extra...

OphthUS-GPT: Multimodal AI for Automated Reporting in Ophthalmic B-Scan Ultrasound

The rapid advancement of AI in ophthalmology is transforming diagnostics, especially in resource-lim...

Synthesizing evidence regarding artificial intelligence generated radiological reports based on medical images: a scoping review protocol

Considering numerous radiological images and the heavy workload of writing corresponding reports in ...

mBrainGT: Modular Graph Transformer for Brain Disorder Diagnosis

Functional brain networks play an essential role in the diagnosis of brain disorders by enabling the...

MRI-Derived Variables Combined with Machine Learning for Pulmonary Hypertension Risk Prediction: A Retrospective Analysis

Pulmonary hypertension (PH) is a severe and progressive vascular disease for which early diagnosis a...

Precise perivascular space segmentation on magnetic resonance imaging from Human Connectome Project-Aging

Perivascular spaces (PVS) are cerebrospinal fluid-filled tunnels around brain blood vessels, crucial...

Artificial intelligence automation of echocardiographic measurements

Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular ...

Transcribing multilingual radiologist-patient dialogue into mammography reports using AI: A step towards patient-centric radiology

Radiology reports are primarily designed for healthcare professionals, often containing complex medi...

Large Language Models in Radiology Reporting—A Systematic Review of Performance, Limitations, and Clinical Implications

Large language models (LLMs) have emerged as potential tools for automated radiology reporting. Howe...

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