Radiology

[Current status and progress of prognostic evaluation in marginal zone lymphoma].

Marginal zone lymphoma (MZL) is an indolent B-cell non-Hodgkin lymphoma with marked heterogeneity. Currently, histological subtype, clinical stage, biochemical parameters, and clinical scoring systems represented by the MZL-international prognostic index (MZL-IPI) remain the principal basis for risk stratification; however, their differentiation power is limited, particularly for intermediate-risk...

Automatic measurement of vertebral compression ratio on lumbar MR images fracture assessment based on MS-Res-AttU-Net model framework.

OBJECTIVES: To develop an MS-Res-AttU-Net-based deep learning framework for automatic measurement of vertebral compression ratio (VCR) on lumbar magnetic resonance images and to evaluate its value for image-based assessment of lumbar vertebral fractures. METHODS: This retrospective study included 92 patients with lumbar vertebral fractures who underwent sagittal T2-weighted MRI. An MS-Res-AttU-Net...

Comparative 10-year performance of mammography artificial intelligence, polygenic, and clinical breast cancer risk models in the Kaiser Permanente Research Bank.

BACKGROUND: We compared performance across 3 breast cancer risk domains-clinical, polygenic, and mammography artificial intelligence-alone and in comb...

An agentic AI framework for integrated decision support and surgical planning in intracerebral hemorrhage.

BACKGROUND: Intracerebral hemorrhage (ICH) remains associated with high mortality and treatment variability. Current workflows rely on fragmented imag...

Construction of a small-sample brain imaging data augmentation and explainable diagnostic model for autism based on generative adversarial networks.

BACKGROUND: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized primarily by social communication deficits and repetitive st...

The Value of a Predictive Model Based on Multimodal Ultrasound Imaging Biomarkers Combined with Clinical Features in the Diagnosis of Thyroid Nodules.

INTRODUCTION: This study aimed to develop and validate diagnostic models for distinguishing benign and malignant thyroid nodules. METHODS: Between Jan...

Unveiling the sensing mechanism of a Nile Red-naphthoquinone H₂S fluorescent probe: theoretical calculation and deep learning prediction.

Hydrogen sulfide (H₂S) is a vital endogenous gasotransmitter implicated in numerous physiological and pathological processes; thus, its precise detect...

Performance of the Techcyte AI-based image analysis system for coproparasitological diagnosis in clinical stool specimens: a retrospective evaluation study.

UNLABELLED: Coproparasitological stool analysis based on microscopic examination is the reference diagnostic technique routinely performed in clinical...

Focused Ultrasound System Design Considerations Based on the Applications for the Peripheral Nervous System.

OBJECTIVE: This paper provides a review of design considerations crucial to Focused ultrasound (FUS) systems for achieving effective therapeutic outco...

Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study.

BACKGROUND: Functional impairments associated with mental health conditions are on the rise. Predicting functional outcomes may improve the targeting ...

An innovative deep learning paradigm for automated detection and accurate classification of lung nodules in magnetic resonance imaging.

Lung cancer, the predominant kind of cancer, needs considerable care, since inadequate treatment may lead to fatal outcomes. The integration of comput...

Deep Learning-based Quantification of Knee Effusion-Synovitis Volume on MRI - Technique Development and Validation.

OBJECTIVE: To develop and validate a deep learning (DL) model for automatic quantification of knee effusion-synovitis volume (ESV) on MRI, assess corr...

Low-intensity transcranial ultrasound stimulation improves motor behavior and modulates cortical functional network connectivity in mice with ischemic stroke.

The recovery of motor function in patients with ischemic stroke is closely related to the plastic remodeling of cortical functional networks. Low-inte...

An intelligent MRI-based all-in-one diagnostic strategy for axillary lymph node status in breast cancer.

An integrated diagnostic strategy of preoperative identification of sentinel lymph node (SLN) metastasis, SLN metastatic burden, and non-SLN (NSLN) me...

PD-YOLO: a high-precision detection network for fine-grained lung cancer and multi-scale pulmonary nodules in CT images.

Timely and accurate Computed Tomography (CT) screening is crucial for the early clinical treatment of lung cancer and preventing the progression of ma...

IUM-hybrid model for enhanced CAD diagnosis using deep learning and VS Grad-CAM visualization.

Coronary artery disease(CAD) is a serious health issue worldwide. Early identification of CAD is used to prevent several complications, such as myocar...

Handling missing modalities in multimodal survival prediction for non-small cell lung cancer.

Accurate survival prediction in non-small cell lung cancer (NSCLC) requires integrating clinical, radiological, and histopathological data. Multimodal...

MammoDenseSegNet: A Context-Aware Deep Learning Model for Dense Tissue Segmentation in Digital Mammograms.

Breast density is a breast cancer risk factor. The accurate quantification of breast density requires reliable segmentation of dense tissue in mammogr...

Dynamic Fuzzy-Gaussian Modeling (DynFGM): A Kurtosis-Adaptive Unsupervised Framework for Automated Adipose Tissue Segmentation in Abdominal MRI.

Accurate MRI-based quantification of abdominal adipose tissue is critical for metabolic risk assessment but is limited by labor-intensive manual segme...

A Computationally Efficient and Improved Brain Tumor Recognition System by MRI-Segmentation Integrated Classification Network.

Brain tumors present a major global health concern, and a precise diagnosis is essential for proper treatment. Many existing MRI-based machine learnin...