Radiology

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 nature of the lesion. This study was performed to assess the ability of a machine learning (ML) model using computed tomography (CT) and magnetic resonance imaging (MRI) radiomic features to classify odontogenic cysts and tumours. CT and MRI data from patients with odontogenic lesions including dentig...

Knowledge, attitudes, and practices of cardiovascular health care personnel regarding coronary CTA and AI-assisted diagnosis: a cross-sectional study.

BACKGROUND: Artificial intelligence (AI) holds significant promise for medical applications, particularly in coronary computed tomography angiography (CTA). We assessed the knowledge, attitudes, and practices (KAP) of cardiovascular health care personnel regarding coronary CTA and AI-assisted diagnosis.

Enhancing Prostate Cancer Classification: A Comprehensive Review of Multiparametric MRI and Deep Learning Integration.

Multiparametric MRI (mpMRI) has become an essential tool in the detection of prostate cancer (PCa) and can help many men avoid unnecessary biopsies. H...

Ultrafast MRI and diffusion-weighted imaging: a review of morphological evaluation and image quality in breast MRI.

Breast magnetic resonance imaging (MRI) is an essential tool for evaluating breast lesions, with dynamic contrast-enhanced (DCE) MRI being considered ...

Deep learning-driven abbreviated knee MRI protocols: diagnostic accuracy in clinical practice.

BACKGROUND: Deep learning (DL) reconstruction shows potential in reducing MRI acquisition times while preserving image quality, but the impact of vary...

Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain Prevention.

Targeted muscle reinnervation (TMR) was initially developed as a technique for bionic prosthetic control but has since become a widely adopted strateg...

Rapid Salient Object Detection With Difference Convolutional Neural Networks.

This paper addresses the challenge of deploying salient object detection (SOD) on resource-constrained devices with real-time performance. While recen...

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 year worldwide. The mortality rate is over 251,000 de...

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 imaging techniques for diagnosing complex conditi...

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 Resonance Imaging (MRI), Ultrasound (US), and Mammogr...

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 using T2-weighted, diffusion-weighted, and contras...

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 ultrasound is challenged by lack of trained providers, wo...

Integrating MobileNetV3 and SqueezeNet for Multi-class Brain Tumor Classification.

Brain tumors pose a critical health threat requiring timely and accurate classification for effective treatment. Traditional MRI analysis is labor-int...

How well does GPT-4 perform on an emergency medicine board exam? A comparative assessment.

BACKGROUND: Recent advancements in artificial intelligence have shown promise in enhancing diagnostic precision within healthcare sectors. In emergenc...

BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease.

INTRODUCTION: Early-onset Alzheimer's disease (EOAD) population is a clinically, genetically and pathologically heterogeneous condition. Identifying b...

Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer, Dictionary Version PM1.0.

Artificial intelligence (AI) can advance medical diagnostics, but interpretability limits its clinical use. This work links standardized quantitative ...

Development of a deep learning-based automated diagnostic system (DLADS) for classifying mammographic lesions - a first large-scale multi-institutional clinical trial in Japan.

BACKGROUND: Recently, western countries have built evidence on mammographic artificial Intelligence-computer-aided diagnosis (AI-CADx) systems; howeve...

Multi-modal Classification of Retinal Disease Based On Convolutional Neural Network.

Retinal diseases such as age-related macular degeneration and diabetic retinopathy will lead to irreversible blindness without timely diagnosis and tr...

De-speckling of medical ultrasound image using metric-optimized knowledge distillation.

Ultrasound imaging provides real-time views of internal organs, which are essential for accurate diagnosis and treatment. However, speckle noise, caus...

Development of a prediction model by combining tumor diameter and clinical parameters of adrenal incidentaloma.

When adrenal incidentalomas are detected, diagnostic procedures are complicated by the need for endocrine-stimulating tests and imaging using various ...