Radiology

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 Mammography (MMG) for the differentiation of benign and malignant breast nodules.

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 contrast-enhanced T1-weighted MR images, along with DCE data derived from time-intensity curves.

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 ...

MRI-based habitat, intra-, and peritumoral machine learning model for perineural invasion prediction in rectal cancer.

OBJECTIVES: This study aimed to analyze preoperative multimodal magnetic resonance images of patients with rectal cancer using habitat-based, intratum...

Universal dimensions of visual representation.

Do visual neural networks learn brain-aligned representations because they share architectural constraints and task objectives with biological vision ...

Development and validation of a deep learning ultrasound radiomics model for predicting drug resistance in lymph node tuberculosis a multicenter study.

BACKGROUND: To develop and validate an ensemble machine learning ultrasound radiomics model for predicting drug resistance in lymph node tuberculosis ...

SPACE: Subregion Perfusion Analysis for Comprehensive Evaluation of Breast Tumor Using Contrast-Enhanced Ultrasound-A Retrospective and Prospective Multicenter Cohort Study.

OBJECTIVE: To develop a dynamic contrast-enhanced ultrasound (CEUS)-based method for segmenting tumor perfusion subregions, quantifying tumor heteroge...

Reconstruction of Heart-related Imaging from Lung Electrical Impedance Tomography Using Semi-Siamese U-Net.

INTRODUCTION: Electrical Impedance Tomography (EIT) is widely used for bedside ventilation monitoring but is limited in reconstructing cardiac-related...

Multi-modal models using fMRI, urine and serum biomarkers for classification and risk prognosis in diabetic kidney disease.

BACKGROUND: Functional magnetic resonance imaging (fMRI) is a powerful tool for non-invasive evaluation of micro-changes in the kidneys. This study ai...

[AI-based applications in medical image computing].

The processing of medical images plays a central role in modern diagnostics and therapy. Automated processing and analysis of medical images can effic...

Diagnostic performance of artificial intelligence based on contrast-enhanced computed tomography in pancreatic ductal adenocarcinoma: a systematic review and meta-analysis.

PURPOSE: This meta-analysis systematically evaluated the diagnostic performance of artificial intelligence (AI) based on contrast-enhanced computed to...

Habitat-Derived Radiomic Features of Planning Target Volume to Determine the Local Recurrence After Radiotherapy in Patients with Gliomas: A Feasibility Study.

To develop a machine learning-based predictive model for local recurrence after radiotherapy in patients with gliomas, with interpretability enhanced ...