Radiology

Integrating handcrafted and deep learning MRI signatures: an interpretable framework for predicting chemotherapy benefit in glioma.

The molecular and spatial heterogeneity of gliomas severely limits accurate prediction of postoperative adjuvant chemotherapy efficacy, representing a critical bottleneck in achieving personalized treatment decisions. Conventional imaging assessments and single-modality AI models struggle to comprehensively characterize the complex tumor phenotype. Based on preoperative multimodal MRI data from th...

MRI-based deep learning radiomics model for automated classification of disc degeneration in the lumbar spine.

Disc degeneration in the lumbar spine is a major cause of low back pain (LBP). The accurate grading of disc degeneration on magnetic resonance imaging (MRI) is critical for clinical management and patient selection for spine surgery. This study aims to develop and evaluate machine learning (ML) models that combine features from deep learning (DL) and radiomics for the automated prediction of Pfirr...

Deep learning-based cervical cancer T-staging using MRI: multi-structure segmentation and classification.

AIMS: Cervical cancer has high incidence and mortality, seriously threatening women's survival and quality of life. Radiologists currently rely mainly...

What's next for VI-RADS? Updates and future perspectives from the ACR VI-RADS steering committee.

Since the introduction of the Vesical Imaging-Reporting and Data System (VI-RADS), MRI has become an important imaging modality in the management of p...

A bibliometric analysis of the association between ultrasound and sarcopenia: Trends, networks, and future directions.

OBJECTIVE: With the escalating global prevalence of sarcopenia, the demand for effective diagnostic tools is critical. While ultrasound shows promise ...

Ultrasound-Mediated Thrombolysis: From Mechanistic Insights to Advanced Nanoplatforms and Clinical Translation.

Although pharmacological thrombolysis and mechanical thrombectomy are standard treatments for thromboembolic diseases, they are limited by hemorrhagic...

Deep Learning-Based Diagnosis of Parotid Gland Tumors on CT Images: A Multi-view Approach for Preoperative Differentiation of Benign and Malignant Lesions.

BACKGROUND: Accurate preoperative differentiation between benign and malignant parotid gland tumors is essential for guiding surgical planning and tre...

Synchrotron-Based Deep Learning Network of the Inner Ear: Development and Expert Validation.

OBJECTIVE: To develop and externally validate a deep learning segmentation network capable of automatically segmenting the inner ear in preoperative c...

Evaluating cognitive biases in AI-assisted mammography interpretation: a simulation reader study of explainable AI across radiologist experience levels.

OBJECTIVES: To evaluate the impact of automation and anchoring bias in artificial intelligence (AI)-assisted mammography interpretation and to assess ...

Validation of an automated AI-based micro-CT organ segmentation workflow against expert annotations and its impact on fluorescence quantification.

OBJECTIVE: Automated, artificial intelligence (AI)-based, organ segmentation has the potential to streamline preclinical imaging workflows, but its su...

Beyond Correlation: Causal Intervention for Multi-Label Medical Image Diagnosis.

This paper addresses the challenge of multi-disease diagnosis by integrating causal reasoning into the diagnostic framework. In clinical practice, mul...

I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling.

Multi-modal medical image synthesis involves nonlinear transformation of tissue signals between source and target modalities, where tissues exhibit co...

Geometry aware neural radiance fields for freehand ultrasound reconstruction.

Reconstructing 3D volumes from 2D freehand ultrasound (US) is a challenging task. During reconstruction, the ensuing overlap between sweeps can cause ...

Enhancing brain tumor classification with a simplified CNN through hyperparameter optimization.

Brain tumors remain a major public health challenge because of their high mortality rate and the need for timely and accurate diagnosis. Magnetic Reso...

AI-MIRACLE: Artificial Intelligence and MultIpaRAmetric MRI Predict CLinical OutcomEs to Neoadjuvant Immunotherapy in Patients with Muscle-invasive Bladder Cancer Undergoing Radical Cystectomy.

BACKGROUND AND OBJECTIVE: Neoadjuvant immune-checkpoint inhibitors (ICIs) in muscle-invasive bladder cancer (MIBC) were tested in patient's ineligible...

MSML-DenseXmer: harnessing vision transformers through integration with novel dense networks for medical image fusion.

The integration of multiple modalities in medical imaging allows a thorough representation of structural and functional details, resulting in improved...

Leveraging large language models for gastrointestinal injury detection in athletes: a medical image analysis approach.

Injury detection and rehabilitation monitoring are critical components of sports medicine, particularly for gastrointestinal injuries that can impact ...

VeNet: a lightweight neural network for efficient brain vessel segmentation in endovascular robotic surgery.

Telesurgery integrates artificial intelligence (AI), robotic systems, sensing technologies, and wireless communication to enable remote and computer-a...

Autism spectrum disorder identification using machine learning models on MRI data.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects sensory processing, speech, behavior and identifying the condition at an...

MSTM-Net: a two-stage prostate cancer segmentation network based on swin-transformer-mamba architecture.

BACKGROUND: Magnetic resonance imaging (MRI) has become a core imaging modality for prostate cancer screening and diagnosis. Accurate and automatic se...