Radiology

Use of Deep Learning Models in the Diagnosis of Proptosis Through Orbital Magnetic Resonance Imaging.

BACKGROUND Proptosis is a common manifestation of orbital disease; however, current diagnostic tools, such as the Hertel exophthalmometer and manual radiological measurements, have limited reproducibility and are observer-dependent. More objective, automated approaches are needed. In this single-center retrospective study, orbital magnetic resonance imaging (MRI) examinations from 521 participants...

Mammography-based artificial intelligence model for predicting axillary lymph node status after neoadjuvant therapy in breast cancer.

OBJECTIVES: Our objective is to develop a deep learning-based artificial intelligence (AI) model capable of analyzing digital mammography (DM) images to predict axillary lymph node (ALN) status subsequent to neoadjuvant therapy (NAT) in breast cancer patients. MATERIALS AND METHODS: We developed and validated an AI model for predicting post-NAT ALN status using images and clinical data of 956 inva...

A large-scale fMRI dataset for vision-language semantic association.

Understanding the neural coding and association of visual and language information benefits from the development of deep learning models and the colle...

Improved visualization of perivascular spaces on T2-weighted imaging with deep learning-based denoising and super-resolution reconstruction.

This prospective study evaluated the ability of a deep learning-based denoising followed by super-resolution function (SR-DL) to improve the visualiza...

3D Segmentation of Multi-contrast Cardiac Magnetic Resonances With Topological Correction and Synthetic Data Augmentation.

PURPOSE: Automatic segmentation of cardiac magnetic resonance (CMR) images improves the evaluation of heart structure and function, helping clinical d...

[Exploration and clinical application of the "digital and intelligent surgery" diagnosis and treatment workflow for oral and maxillofacial tumors].

Tumors in the oral and maxillofacial region present significant clinical challenges due to anatomical complexity and high individual variability, with...

[Research progress in clinical pathology and molecular mechanisms of pancreatic adenosquamous carcinoma].

Pancreatic adenosquamous carcinoma (PASC) is a rare exocrine malignancy of the pancreas with an increasing incidence, histologically defined by the co...

Proteomics for precision nutrition: current evidence and future directions.

PURPOSE OF REVIEW: This review critically evaluates proteomics research applied to clinical nutrition and metabolism published between mid-2024 and ea...

Multiparametric AI-based perfusion analysis outperforms Tmax thresholding for critically hypoperfused tissue estimation in acute ischemic stroke undergoing admission MRI.

OBJECTIVE: Perfusion imaging is used to estimate critically hypoperfused tissue in acute ischemic stroke (AIS), commonly using threshold-based methods...

Identifying neuroimaging-based biomarkers for obsessive-compulsive disorder: A resting-state fMRI and machine learning study in patients and unaffected first-degree relatives.

BACKGROUND: Identifying whether brain alterations in obsessive-compulsive disorder (OCD) represent the illness itself or an underlying genetic vulnera...

Use of Machine Learning to Identify Markers of Risk for Fragile X-Associated Tremor/Ataxia Syndrome: A Preliminary Analysis.

OBJECTIVE: The objective of this study was to examine whether machine learning has the capacity to prospectively identify and predict the emergence of...

Construction of Rheumatoid Arthritis-Associated Interstitial Lung Disease diagnostic model and identification of biomarkers based on a multi-omics integration strategy of machine learning.

OBJECTIVE: This study aimed to develop and validate a machine learning model integrating multi-omics and radiomics data to improve diagnostic accuracy...

Evaluation of Brain Age on Magnetic Resonance Imaging Using Machine Learning Techniques in Patients With Hearing Loss.

OBJECTIVES: Hearing impairment is strongly linked to cognitive decline, with individuals having mild-to-severe hearing loss estimated as having a twof...

Deciphering Short-Range Order in 2D Transition Metal Dichalcogenides: From Origin to Multi-Scale Property Modulation.

Chemical short-range order (SRO) is a critical structural characteristic in multi-principal element materials, governing the global electronic propert...

Promptable segmentation with region exploration enables minimal-effort expert-level prostate cancer delineation.

PURPOSE: Accurate segmentation of prostate cancer on magnetic resonance (MR) images is crucial for planning image-guided interventions such as targete...

Training in diagnostic and interventional endoscopic ultrasound: a state-of-the-art review.

INTRODUCTION: Endoscopic ultrasound (EUS) has progressed from a primarily diagnostic modality to an essential diagnostic and therapeutic platform. The...

The SPECT/CT radiomics-based classification of skeletal metastases and benign bone lesions.

Hybrid Single Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) improves lesion localization and diagnostic accuracy in detecting ske...

A Hybrid Approach for Differentiating Fibrotic Hypersensitivity Pneumonitis and Idiopathic Pulmonary Fibrosis: Deep Feature Extraction and Machine Learning.

In patients over 60 years of age who are male, non-smokers, and in whom no organic antigen is identified in the history, differentiating fibrotic hype...

Explainable transfer learning ensemble AI model for lung ultrasound pneumothorax detection with expert benchmark.

BACKGROUND: Lung ultrasound is essential for rapid, radiation-free bedside pneumothorax diagnosis but limited by variability in human interpretation. ...

Cogninet: an explainable deep learning model for multi-class MRI-based Alzheimer's disease staging.

PURPOSE: Alzheimer's Disease (AD) is a neurodegenerative condition which presents significant challenges in early diagnosis and clinical decision-maki...