Radiology

Exploration of the Application of Multimodal Feature Analysis Based on Random Forest Algorithm Combining Ultrasound Elastography and Contrast-Enhanced Ultrasound in the Diagnosis of Ovarian Tumors.

OBJECTIVE: This study aimed to build a multimodal ultrasound (color Doppler flow imaging/shear wave elastography/contrast-enhanced ultrasound) combined with machine learning (ML) model, evaluating random forest (RF) for early ovarian malignancy diagnosis. METHODS: A retrospective analysis included 130 patients (72 benign, 58 malignant) pathologically confirmed ovarian lesions. Patients were split ...

On Hallucinations in Artificial Intelligence-Generated Content for Nuclear Medicine Imaging (the DREAM Report).

Artificial intelligence-generated content (AIGC) has shown remarkable performance in nuclear medicine imaging (NMI), offering cost-effective software solutions for tasks such as image enhancement, motion correction, and attenuation correction. However, these advancements come with the risk of hallucinations, generating realistic yet factually incorrect content. Hallucinations can misrepresent anat...

Instance-level quantitative saliency in multiple sclerosis lesion segmentation.

In recent years, explainable methods for artificial intelligence (XAI) have tried to reveal and describe models' decision mechanisms in the case of cl...

MRI neuroimaging-based Alzheimer's disease stage classification using deep neural network with convolutional block attention module and GAN-style noise injection.

Millions of individuals worldwide suffer from Alzheimer's disease (AD), a chronic, incurable neurological disorder. For the longevity of people, a com...

Hematologic markers and machine learning in predicting placenta accreta: A case-control study.

OBJECTIVE: This study aims to enhance antenatal detection of placenta accreta spectrum (PAS) and predict severe hemorrhage at delivery using machine l...

State-of-the-art 32 cm field-of-view digital PET/CT system: preliminary study for protocols optimization and DRLs update.

The aim of this study was to assess the impact of a state-of-the-art 32 cm axial field-of-view GE Omni Legend PET/CT system on administered activity a...

Multiscale segmentation using hierarchical phase-contrast tomography and deep learning.

Biomedical systems span multiple spatial scales, encompassing tiny functional units to entire organs. Interpreting these systems through image segment...

Evaluation of Large Language Models for Radiologists' Support in Multidisciplinary Breast Cancer Teams: Comparative Study.

BACKGROUND: Artificial intelligence tools, particularly large language models (LLMs), have shown considerable potential across various domains. Howeve...

The Accuracy of Artificial Intelligence Models in Carpal Tunnel Diagnosis: A Systematic Review and Meta-analysis.

BACKGROUND: Artificial intelligence (AI) has been integrated into diagnostic modalities like nerve conduction studies (NCS) and ultrasound (US) to imp...

An interpretable AI system reduces false-positive MRI diagnoses by stratifying high-risk breast lesions.

Breast cancer diagnosis using magnetic resonance imaging remains limited by high false-positive rates and substantial inter-reader variability, especi...

Multimodal fusion and explainability of artificial intelligence models in Alzheimer's Disease detection.

The integration of multimodal data has emerged as a powerful strategy for enhancing the accuracy and interpretability of artificial intelligence (AI) ...

Cardiovascular measures from abdominal MRI provide insights into abdominal vessel genetic architecture.

BACKGROUND: Cardiovascular disease remains a major source of morbidity and mortality, and population imaging studies have yielded insights into diseas...

Machine learning for the diagnosis of fibromyalgia based on magnetic resonance imaging.

The clinical diagnosis of fibromyalgia (FM), a syndrome characterized by generalized pain, is challenging due to its unknown etiology and frequent com...

Artificial intelligence for early detection of pancreatic cancer in prediagnostic and diagnostic computed tomography examinations: A multicenter retrospective case-control study.

PURPOSE: The purpose of this study was to develop and validate a computer-aided detection (CAD) tool for the detection of pancreatic cancer (PC) on di...

3D CSFA-UNet: a unified attention-driven deep learning framework for accurate knee MRI segmentation and osteoarthritis severity classification.

Although several recent multi-task deep learning methods already perform segmentation and classification jointly, many still face limitations in clini...

Advances in radiomics for predicting and managing xerostomia following radiotherapy: A systematic review.

BACKGROUND: Radiomics has emerged as a promising approach for predicting radiotherapy (RT)- induced xerostomia in head and neck cancer (HNC) patients,...

Parametric imaging of dynamic long-axial-field-of-view PET scans: Technical challenges, statistical insights and clinical applications.

Dynamic long-axial-field-of-view (LAFOV) PET imaging offers unprecedented opportunities for quantitative assessment of tracer kinetics across the enti...

BDEC: Brain Deep Embedded Clustering Model for Resting State fMRI Group-Level Parcellation of the Human Cerebral Cortex.

OBJECTIVE: To develop a robust group-level brain parcellation method using deep learning based on resting-state functional magnetic resonance imaging ...

Assignment of the Biological Identity of Breast Masses in Contrast-enhanced Spectral Computed Tomography Examinations of Chest Carried Out in Clinical Routine Diagnostics.

BACKGROUND: Focal breast lesions are observed in up to 5.8% of CT examinations performed in female patients for a wide variety of indications not affe...

Echo Flow-Induced Temporal Correlation Learning for Ultrasound Video Object Segmentation.

OBJECTIVE: The segmentation of ultrasound video objects aims to delineate specific anatomical structures or areas of injury in sequential ultrasound i...