Radiology

[Basal ganglia and movement disorders : Part 1: the parkinsonian spectrum-Neuroradiological patterns and diagnostic considerations].

Movement disorders are a frequent yet diagnostically unspecific reason for referral in routine neuro...

Predicting intrahepatic recurrence of colorectal cancer liver metastases after curative hepatectomy using a machine learning model with data integration of ultrasound radiomics and clinicopathological parameters.

OBJECTIVES: To develop and validate a machine learning model integrating ultrasound radiomics and cl...

Cerebrovascular CTA radiomics for objective collateral grading in acute ischemic stroke.

OBJECTIVE: Collateral circulation is a key determinant of functional outcome after large vessel occl...

Deep learning-based identification of chronic pulmonary embolism on CTPA: a regional lung analysis using multiplanar MIP images.

OBJECTIVE: Chronic pulmonary embolism (CPE) and chronic thromboembolic pulmonary hypertension (CTEPH...

Advances in imaging modalities and tumor classification for vestibular schwannomas.

PURPOSE: Vestibular schwannomas are benign tumors of the cerebellopontine angle that may causesignif...

Generating synthetic CEM from low-energy images using deep learning: A future without contrast media? A proof-of-concept study.

OBJECTIVE: We used deep learning to generate synthetic, resembling in appearance, iodine-enhanced, m...

Radiomics in fetal brain MRI: a narrative review.

Fetal MRI has emerged as a crucial supplement to prenatal ultrasonography in the evaluation of the d...

Zero Echo Time Magnetic Resonance Imaging of the Spine.

Zero echo time magnetic resonance imaging is an ultrashort echo time technique that enables computed...

Artificial Intelligence in Radiology: Unlocking New Dimensions of Value.

Artificial intelligence (AI) is emerging as a transformative force in radiology, offering the potent...

Advances in the laboratory diagnosis of non-viral microbial keratitis.

Infectious keratitis remains a leading cause of corneal blindness and visual impairment worldwide, w...

Deep learning synthesis of virtual T2-weighted fat-suppressed MR images: a multi-center study.

OBJECTIVES: To develop a Generative Adversarial Network (GAN) for generating virtual T2 fat-suppress...

Beyond whole-image learning: anatomically partitioned deep learning models for superior sinonasal disease classification.

OBJECTIVES: Whole-image deep learning models for CT diagnosis of nasal cavity and paranasal sinus di...

Should AI results be disclosed in mammography reports? A randomised survey study of patient responses to concordant and discordant interpretations.

OBJECTIVES: To assess how disclosing artificial intelligence (AI) results, particularly discordant f...

Ethmoid sinus CBCT imaging as a biometric instrument: dataset creation for deep learning identification.

BACKGROUND: Internal cranial structures such as the sphenoid and ethmoid bones, along with their ass...

Beyond benchmarks of IUGC: Rethinking requirements of deep learning method for intrapartum ultrasound biometry from fetal ultrasound videos.

A significant proportion (45%) of maternal deaths, neonatal deaths, and stillbirths occur during the...

HyCoSwin-PD: An explainable hybrid ConvNeXtV2-Swin transformer framework for Parkinson's disease detection from neuroimaging.

Accurate detection of Parkinson's disease (PD) from structural MRI remains a significant challenge d...

Comparative study of different artificial intelligence (AI)-assisted compressed sensing factors in inner ear heavily T2-weighted imaging.

AIM: Artificial intelligence (AI)-assisted compressed sensing (ACS) is a cutting-edge magnetic reson...

ROBUST-MIPS: A Combined Skeletal Pose and Instance Segmentation Dataset for Laparoscopic Surgical Instruments.

Localisation of surgical tools constitutes a foundational building block for computer-assisted inter...

Simulation of the Metabolic Response to an Interventional Study with New Healthy Beverages by Machine-Learning Regression.

The present study proposes a methodology to emulate an interventional trial by employing machine-lea...

Super-resolution deep learning reconstruction enhances visualization of cerebral aneurysms on magnetic resonance angiography.

A multimodal feature disentanglement model for lymphadenopathy diagnosis based on BUS and CDFI ultrasound videos: a retrospective, prospective, multicenter study.

OBJECTIVES: This study developed and validated a deep learning model for diagnosing lymphadenopathy ...