Radiology

Claustrum and Hippocampus Segmentation-Based Alzheimer's Disease Identification Model Using RoT-Kmeans and CoLU-CNN.

Alzheimer's disease (AD) is a dementia disease that causes loss of cognitive functions. Also, it is a noncurable disease. However, early diagnosis and proper medication reduce AD's progression time. Yet, the prevailing AD diagnosis models did not concentrate on the claustrum in the brain, reducing the efficiency of the AD diagnosis. Thus, this framework proposes an effective AD identification mode...

Combining computed tomography radiomics and clinical features to predict lymph node metastasis in patients with lung cancer.

BACKGROUND: Accurate preoperative assessment of lymph node metastasis (LNM) is crucial for treatment planning and prognostic stratification in patients with lung cancer. This study aimed to develop and validate a predictive model for LNM using radiomic features derived from non-contrast computed tomography (CT) combined with clinical characteristics. METHODS: A total of 403 patients with pathologi...

Development and validation of stability prediction models for intracranial aneurysms based on ensemble learning algorithms.

OBJECTIVE: This study aims to develop and validate ensemble learning models based on pre-rupture or pre-growth images andmultiple features to predict ...

Tissue morphology predicts telomere shortening in human tissues.

We present TLPath, a deep learning framework that predicts bulk-tissue telomere length from tissue morphology extracted from routine histopathology im...

Machine learning models based on MRI predict the expression levels of Galectin - 9 expression in rectal cancer.

OBJECTIVE: To investigate the utility of a machine learning model based on MRI radiomics in predicting the expression of Galectin-9 in rectal cancer. ...

[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 neuroradiological practice. Hypokinetic syndromes with ...

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 clinicopathological parameters to predict intrahepat...

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

OBJECTIVE: Collateral circulation is a key determinant of functional outcome after large vessel occlusion (LVO) and informs thrombectomy decisions. Ho...

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) are challenging to diagnose, with delayed detect...

Advances in imaging modalities and tumor classification for vestibular schwannomas.

PURPOSE: Vestibular schwannomas are benign tumors of the cerebellopontine angle that may causesignificant neurological morbidity. Magnetic resonance i...

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, mammograms from low-energy contrast-enhanced mammog...

Radiomics in fetal brain MRI: a narrative review.

Fetal MRI has emerged as a crucial supplement to prenatal ultrasonography in the evaluation of the developing brain and in identifying congenital defe...

Zero Echo Time Magnetic Resonance Imaging of the Spine.

Zero echo time magnetic resonance imaging is an ultrashort echo time technique that enables computed tomography-like visualization of cortical and tra...

Artificial Intelligence in Radiology: Unlocking New Dimensions of Value.

Artificial intelligence (AI) is emerging as a transformative force in radiology, offering the potential to revolutionize the field by enabling sophist...

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

Infectious keratitis remains a leading cause of corneal blindness and visual impairment worldwide, with bacterial, fungal, amoebic and viral pathogens...

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-suppressed (T2FS) sequences from standard T1- and T2-weigh...

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 diseases often underperform because they disregard a...

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 findings, affects patient trust, anxiety, follow-up...

Toward personalized iodine-131 therapy: A review of dosimetric strategies.

BACKGROUND: Iodine-131 (131I) therapy is a cornerstone of nuclear medicine for thyroid diseases and certain cancers. This review evaluates the transit...

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 associated sinuses, provide valuable biometric inform...