Radiology

Oculomics: advances and perspectives from traditional Chinese medicine to modern multimodal biomarkers.

Oculomics, the study of the relationship between ophthalmic biomarkers (changes or abnormalities in the eye) and systemic health or disease states, posits that the eye can serve as a window into the overall health of the body. This concept aligns closely with the ideas of traditional Chinese medicine (TCM) ocular diagnosis, which similarly emphasizes the eye as a reflective indicator of systemic c...

Enhancing ultrasound training for breast cancer diagnosis: a controlled study of AI-assisted learning.

OBJECTIVES: This study aimed to develop and evaluate an AI-assisted teaching platform to enhance diagnostic competency in breast ultrasound. The goal was to assess whether AI integration improves diagnostic accuracy, learning efficiency, and participant satisfaction within a residency training program. METHODS: We conducted a cohort-based study at our hospital. Twelve junior residents (experimenta...

Real-world multimetric comparison of four commercial artificial intelligence solutions for intracranial hemorrhage detection.

PURPOSE: To evaluate the real-world multimetric performance of four commercially available computed tomography (CT)-based artificial intelligence (AI)...

Rapid and Nondestructive Techniques for Quality Assessment of Tofu: A Comprehensive Review.

Tofu is a traditional soy-based food of global importance, requiring stringent quality control to ensure safety and support industrial modernization. ...

Live-Cell Imaging Enables Reporter-Free Monitoring Of The Circadian Rhythm In Individual Synechocystis Cells.

In vivo monitoring of circadian rhythms depends on reliable and non-invasive detection methods. This is often achieved by expressing reporter genes he...

Prediction of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Multi-Ultrasound Radiomics of Intratumoral and Peritumoral Regions.

OBJECTIVE: The aim of this study was to integrate multimodal intratumoral and peritumoral ultrasound radiomic features with clinical data to construct...

An interpretable machine learning model integrating ultrasound and clinical variables for predicting osteoporosis in patients with rheumatoid arthritis.

OBJECTIVES: Rheumatoid arthritis (RA) significantly increases the risk of osteoporosis (OP) and fractures, yet dual-energy X-ray absorptiometry (DXA) ...

Machine learning for predicting full-count FDG PET brain images from low-count acquisitions in suspected dementia: a clinical and quantitative evaluation.

Objective.Artificial intelligence methods for denoising low-count FDG PET brain images are usually evaluated using image quality metrics alone, with l...

Current Perspectives on Thyroid Nodule Evaluation: Integrating Cytomorphology, Ultrasound Risk Stratification, and Molecular Testing.

Fine-needle aspiration biopsy (FNAB) remains the cornerstone of preoperative evaluation of thyroid nodules and represents one of the most enduring and...

Integrating CT Radiomics and Machine Learning for Preoperative Assessment of STAS Grading and Prognostic Stratification in Lung Adenocarcinoma.

RATIONALE AND OBJECTIVES: The distance of spread through air spaces (STAS) dissemination is associated with prognosis in lung adenocarcinoma (LUAD). T...

A Machine-Learning Model Using Pre-treatment Multimodal Data to Predict Sentinel Lymph Node Status After Neoadjuvant Chemotherapy in Operable Early-stage Breast Cancer.

RATIONALE AND OBJECTIVES: Although accurate preoperative prediction of sentinel lymph node (SLN) status in operable early-stage breast cancer (EBC) af...

Multimodal Deep Fusion of Ultrasound Images and Clinical Factors for Pre-operative Prediction of Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma.

OBJECTIVE: Lateral cervical lymph node metastasis (LLNM) is missed in up to 30% of papillary thyroid carcinoma (PTC) patients pre-operatively, leading...

Breast cancer detection and classification via a robust deep learning approach.

This work presents a leakage-controlled deep-learning framework for breast cancer classification using the CBIS-DDSM mammography archive. The proposed...

Neoadjuvant Chemoradiotherapy on Postoperative Complications of Rectal Cancer: A Retrospective Study Integrating MRI Radiomics and Deep Learning.

Neoadjuvant chemoradiotherapy (nCRT) is standard for locally advanced rectal cancer but increases postoperative complication (POC) risks due to tissue...

Artificial Intelligence in Ischemic Stroke Lesion Segmentation: A Narrative Review of Deep Learning Methods, Clinical Utility, and Future Directions.

Ischemic stroke management is time-sensitive, and lesion segmentation supports treatment selection, prognostication, and reproducible quantification. ...

Integrating multiple hematologic inflammatory indices into a principal component analysis-derived inflammatory burden score for coronary artery disease.

BACKGROUND: Complete blood count-derived inflammatory indices have been associated with coronary artery disease (CAD), but their substantial overlap m...

Explainable machine learning for early prediction and anatomical classification of pulmonary embolism in the emergency department.

BACKGROUND: Pulmonary thromboembolism (PTE) is a life‑threatening condition that requires prompt and accurate evaluation in the emergency department (...

A CT-based deep learning model for the automated risk stratification of refractory Mycoplasma pneumoniae pneumonia in children.

BACKGROUND: The accurate identification of children with refractory Mycoplasma pneumoniae pneumonia (RMPP) remains challenging. This study aimed to de...

Optimizing coronary artery disease management: the role of CT-derived fractional flow reserve in predicting revascularization and guiding clinical decisions.

OBJECTIVE: Coronary artery disease (CAD) is the most common heart disease, and invasive coronary angiography (ICA) has been recognized as the gold sta...

Predicting placenta accreta spectrum antenatally: a deep learning-radiomics dual-model based on intraoperative clinical and histopathologic criteria.

PURPOSE: Definitive diagnosis of placenta accreta spectrum (PAS) disorders occurs at delivery by using combined intraoperative clinical and histopatho...