Radiology

Automated Brain Tumor Classification and Grading Using Multi-scale Graph Neural Network with Spatio-Temporal Transformer Attention Through MRI Scans.

The medical field uses Magnetic Resonance Imaging (MRI) as an essential diagnostic tool which provides doctors non-invasive images of brain structures and pathological conditions. Brain tumor detection stands as a vital application that needs specific and effective approaches for both medical diagnosis and treatment procedures. The challenges from manual examination of MRI scans stem from inconsis...

The Current State of Artificial Intelligence on Detecting Pulmonary Embolism via Computerised Tomography Pulmonary Angiogram: A Systematic Review.

Pulmonary embolism (PE) is a life-threatening condition with significant diagnostic challenges due to high rates of missed or delayed detection. Computed tomography pulmonary angiography (CTPA) is the current standard for diagnosing PE, however, demand for imaging places strain on healthcare systems and increases error rates. This systematic review aims to assess the diagnostic accuracy and clini...

Ensemble of weak spectral total-variation learners: a PET-CT case study.

Solving computer vision problems through machine learning, one often encounters lack of sufficient training data. To mitigate this, we propose the use...

Prenatal detection of congenital heart defects using the deep learning-based image and video analysis: protocol for Clinical Artificial Intelligence in Fetal Echocardiography (CAIFE), an international multicentre multidisciplinary study.

INTRODUCTION: Congenital heart defect (CHD) is a significant, rapidly emerging global problem in child health and a leading cause of neonatal and chil...

A radiogenomics study on F-FDG PET/CT in endometrial cancer by a novel deep learning segmentation algorithm.

OBJECTIVE: To create an automated PET/CT segmentation method and radiomics model to forecast Mismatch repair (MMR) and TP53 gene expression in endomet...

Recognition of flight cadets brain functional magnetic resonance imaging data based on machine learning analysis.

The rapid advancement of the civil aviation industry has attracted significant attention to research on pilots. However, the brain changes experienced...

Preoperative Identification of Papillary Thyroid Carcinoma Subtypes and Lymph Node Metastasis via Deep Learning-Assisted Surface-Enhanced Raman Spectroscopy.

Accurate preoperative diagnosis of papillary thyroid carcinoma (PTC) histological subtypes and lymph node metastasis is essential for formulating pers...

Digital removal of dermal denticle layer using geometric AI from 3D CT scans of shark craniofacial structures enhances anatomical precision.

Craniofacial morphometrics in sharks provide crucial insights into evolutionary history, geographical variation, sexual dimorphism, and developmental ...

Advancing prenatal healthcare by explainable AI enhanced fetal ultrasound image segmentation using U-Net++ with attention mechanisms.

Prenatal healthcare development requires accurate automated techniques for fetal ultrasound image segmentation. This approach allows standardized eval...

Automated Classification of Cervical Spinal Stenosis Using Deep Learning on Computed Tomography Scans.

STUDY DESIGN: Retrospective study. OBJECTIVE: To develop and validate a computed tomography-based deep learning (DL) model for diagnosing cervical spi...

Enhancing Lesion Detection in Inflammatory Myelopathies: A Deep Learning-Reconstructed Double Inversion Recovery MRI Approach.

BACKGROUND AND PURPOSE: The imaging of inflammatory myelopathies has advanced significantly across time, with MRI techniques playing a pivotal role in...

An interpretable deep learning approach for autism spectrum disorder detection in children using NASNet-mobile.

Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder featuring impaired social interactions and communication abilities engagi...

FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images.

Cancer is among the most dangerous diseases contributing to rising global mortality rates. Lung cancer, particularly adenocarcinoma, is one of the dea...

Machine learning model for preoperative classification of stromal subtypes in salivary gland pleomorphic adenoma based on ultrasound histogram analysis.

OBJECTIVES: Accurate preoperative discrimination of salivary gland pleomorphic adenoma (SPA) stromal subtypes is essential for therapeutic plannings. ...

Artificial intelligence may enhance the role of magnetic resonance imaging in prostate cancer focal therapy.

Current trends in glioma tumor segmentation: A survey of deep learning modules.

BACKGROUND: Multiparametric Magnetic Resonance Imaging (mpMRI) is the gold standard for diagnosing brain tumors, especially gliomas, which are difficu...

Current AI technologies in cancer diagnostics and treatment.

Cancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, an...

Egocentric value maps of the near-body environment.

Body-part-centered response fields are pervasive in single neurons, functional magnetic resonance imaging, electroencephalography and behavior, but th...

Ground-truth-free deep learning approach for accelerated quantitative parameter mapping with memory efficient learning.

Quantitative MRI (qMRI) requires the acquisition of multiple images with parameter changes, resulting in longer measurement times than conventional im...

Application research of artificial intelligence software in the analysis of thyroid nodule ultrasound image characteristics.

Thyroid nodule, as a common clinical endocrine disease, has become increasingly prevalent worldwide. Ultrasound, as the premier method of thyroid imag...