Radiology

Keypoint localization and parameter measurement in ultrasound biomicroscopy anterior segment images based on deep learning.

BACKGROUND: Accurate measurement of anterior segment parameters is crucial for diagnosing and managing ophthalmic conditions, such as glaucoma, cataracts, and refractive errors. However, traditional clinical measurement methods are often time-consuming, labor-intensive, and susceptible to inaccuracies. With the growing potential of artificial intelligence in ophthalmic diagnostics, this study aims...

New Targets for Imaging in Nuclear Medicine.

Nuclear medicine is rapidly evolving with new molecular imaging targets and advanced computational tools that promise to enhance diagnostic precision and personalized therapy. Recent years have seen a surge in novel PET and SPECT tracers, such as those targeting prostate-specific membrane antigen (PSMA) in prostate cancer, fibroblast activation protein (FAP) in tumor stroma, and tau protein in neu...

PEARL: Cascaded Self-Supervised Cross-Fusion Learning for Parallel MRI Acceleration.

Supervised deep learning (SDL) methodology holds promise for accelerated magnetic resonance imaging (AMRI) but is hampered by the reliance on extensiv...

Towards High-Quality MRI Reconstruction With Anisotropic Diffusion-Assisted Generative Adversarial Networks and Its Multi-Modal Images Extension.

Recently, fast Magnetic Resonance Imaging reconstruction technology has emerged as a promising way to improve the clinical diagnostic experience by si...

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma From Multi-Sequence Magnetic Resonance Imaging Based on Deep Fusion Representation Learning.

Recent studies have identified microvascular invasion (MVI) as the most vital independent biomarker associated with early tumor recurrence. With advan...

Frozen Large-Scale Pretrained Vision-Language Models are the Effective Foundational Backbone for Multimodal Breast Cancer Prediction.

Breast cancer is a pervasive global health concern among women. Leveraging multimodal data from enterprise patient databases-including Picture Archivi...

Progressive Distillation With Optimal Transport for Federated Incomplete Multi-Modal Learning of Brain Tumor Segmentation.

Multi-modal Magnetic Resonance Imaging (MRI) provide sufficient complementary information for brain tumor segmentation, however, most current approach...

AI-driven early diagnosis of specific mental disorders: a comprehensive study.

One of the areas where artificial intelligence (AI) technologies are used is the detection and diagnosis of mental disorders. AI approaches, including...

Advanced holographic convolutional dense networks and Tangent runner optimization for enhanced polycystic ovarian disease classification.

Polycystic Ovarian Disease (PCOD) is among the most prevalent endocrine disorders complicating the health of innumerable women worldwide due to lack o...

Pix2Pix generative-adversarial network in improving the quality of T2-weighted prostate magnetic resonance imaging: a multi-reader study.

PURPOSE: To assess the performance and feasibility of generative deep learning in enhancing the image quality of T2-weighted (T2W) prostate magnetic r...

Artificial intelligence guided imaging as a tool to fill gaps in health care delivery.

Deep vein thrombosis (DVT) causes significant morbidity/mortality and timely diagnosis often via ultrasound is critical. However, the shortage of trai...

From Image to Diagnosis: Convolutional Neural Networks in Tongue Lesions.

Clinical examination of the tongue is essential for diagnosing systemic and local diseases. However, traditional diagnostic methods rely on subjective...

Semisupervised adaptive learning models for IDH1 mutation status prediction.

The mutation status of isocitrate dehydrogenase1 (IDH1) in glioma is critical information for the diagnosis, treatment, and prognosis. Accurately dete...

The Role of Large Language Models (LLMs) in Breast Imaging Today and in the Near Future.

This narrative review focuses on the integration of large language models (LLMs), such as GPT-4 and Gemini, into breast imaging. LLMs excel in underst...

PSMA PET/CT for prostate cancer diagnosis: current applications and future directions.

Prostate cancer (PCa) requires improved diagnostic strategies beyond conventional imaging. This review aimed to evaluate the role of prostate-specific...

An optimized deep neural network with explainable artificial intelligence framework for brain tumour classification.

Brain tumour classification plays a significant role in improving patient care, treatment planning, and enhancing the overall healthcare system's effe...

The Role of Artificial Intelligence in Providing Real-Time Guidance During Interventional Cardiology Procedures: A Narrative Review.

Integrating artificial intelligence (AI) in interventional cardiology revolutionizes procedural guidance, particularly in high-stakes environments suc...

Self-supervised learning for MRI reconstruction through mapping resampled k-space data to resampled k-space data.

In recent years, significant advancements have been achieved in applying deep learning (DL) to magnetic resonance imaging (MRI) reconstruction, which ...

Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.

OBJECTIVE: Pituitary adenomas (PAs), craniopharyngiomas (CRs), Rathke's cleft cysts (RCCs), and tuberculum sellar meningiomas (TSMs) are common sellar...

Surgical and radiological outcomes of giant cell tumor of the bone: prognostic value of Campanacci grading and selective use of denosumab.

BACKGROUND: Advancements in diagnostic and therapeutic modalities for giant cell tumors of bone (GCTB) have introduced molecular and radiological tool...