Radiology

Deep learning-based breast MRI for predicting axillary lymph node metastasis: a systematic review and meta-analysis.

BACKGROUND: To perform a systematic review and meta-analysis that assesses the diagnostic performance of deep learning algorithms applied to breast MRI for predicting axillary lymph nodes metastases in patients of breast cancer.

AI-ECG Supported Decision-Making for Coronary Angiography in Acute Chest Pain: The QCG-AID Study.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD...

Automated detection of metastatic lymph nodes in head and neck malignant tumors on high - resolution MRI images using an improved convolutional neural network.

PURPOSE: To develop an AI-based diagnostic model for assessing cervical lymph nodes in head and neck malignant tumors using MRI, enabling non-invasive...

Deep Learning and Radiomics Discrimination of Coronary Chronic Total Occlusion and Subtotal Occlusion using CTA.

RATIONALE AND OBJECTIVES: Coronary chronic total occlusion (CTO) and subtotal occlusion (STO) pose diagnostic challenges, differing in treatment strat...

Improving realism in abdominal ultrasound simulation combining a segmentation-guided loss and polar coordinates training.

BACKGROUND: Ultrasound (US) simulation helps train physicians and medical students in image acquisition and interpretation, enabling safe practice of ...

Prediction of BRAF and TERT status in PTCs by machine learning-based ultrasound radiomics methods: A multicenter study.

BACKGROUND: Preoperative identification of genetic mutations is conducive to individualized treatment and management of papillary thyroid carcinoma (P...

Multimodal contrastive learning for enhanced explainability in pediatric brain tumor molecular diagnosis.

Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integr...

Deep Learning Based on Ultrasound Images Differentiates Parotid Gland Pleomorphic Adenomas and Warthin Tumors.

Exploring the clinical significance of employing deep learning methodologies on ultrasound images for the development of an automated model to accurat...

Development and validation of a nomogram for predicting bone marrow involvement in lymphoma patients based on F-FDG PET radiomics and clinical factors.

OBJECTIVE: This study aimed to develop and validate a nomogram combining F-FDG PET radiomics and clinical factors to non-invasively predict bone marro...

Comment On: "Role of artificial intelligence in magnetic resonance imaging-based detection of temporomandibular joint disorder: a systematic review".

In reply to the Letter to Editor regarding Comment on: Role of artificial intelligence in magnetic resonance imaging-based detection of temporomandibular joint disorder: a systematic review.

Internal Target Volume Estimation for Liver Cancer Radiation Therapy Using an Ultra Quality 4-Dimensional Magnetic Resonance Imaging.

PURPOSE: Accurate internal target volume (ITV) estimation is essential for effective and safe radiation therapy in liver cancer. This study evaluates ...

Advanced convolutional neural network with attention mechanism for Alzheimer's disease classification using MRI.

This paper introduces a novel convolutional neural network model with an attention mechanism to advance Alzheimer disease (AD) classification using Ma...

Artificial intelligence for tumor [F]FDG-PET imaging: Advancement and future trends-part I.

The advent of sophisticated image analysis techniques has facilitated the extraction of increasingly complex data, such as radiomic features, from var...

Guided ultrasound acquisition for nonrigid image registration using reinforcement learning.

We propose a guided registration method for spatially aligning a fixed preoperative image and untracked ultrasound image slices. We exploit the unique...

Association of the characteristics of brain magnetic resonance imaging with genes related to disease onset in schizophrenia patients.

BACKGROUND: Schizophrenia (SCH) is a complex neurodevelopmental disorder, whose pathogenesis is not fully elucidated. This article aims to reveal dise...

F-FDG PET radiomics score construction by automatic machine learning for treatment response prediction in elderly patients with diffuse large B-cell lymphoma: a multicenter study.

PURPOSE: To explore the development and validation of automated machine learning (AutoML) models for F-FDG PET imaging-based radiomics signatures to p...

Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging.

The kidney plays a vital role in maintaining homeostasis, but lifestyle factors and diseases can lead to kidney failures. Early detection of kidney di...

Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning.

BACKGROUND: Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatme...

Comparative Assessment of Manual Segmentation of Cerebral Infarction Lesions in Experimental Animals Based on Magnetic Resonance Imaging Using Artificial Intelligence.

The aim of this study was to evaluate the quality of manual segmentation of cerebral infarction lesions in experimental animals with modeled brain inf...