Radiology

PatchGAN-guided semi-supervised U-Net for carotid plaque segmentation in ultrasound under limited annotation conditions.

OBJECTIVE: Artificial intelligence models are increasingly used for stroke risk evaluation and clinical decision-making. However, the scarcity of expert masks, low contrast, and noise in ultrasound images affect segmentation performance. METHODS: Our study innovatively integrated an adversarial PatchGAN discriminator into a batch-normalized semi-supervised U-Net generator to enhance carotid plaque...

Incremental prognostic value of AI-based coronary CT angiography combined with PET/CT dynamic myocardial perfusion imaging in patients with suspected or confirmed coronary artery disease.

BACKGROUND: Coronary computed tomography angiography (CCTA) and positron emission tomography/computed tomography (PET/CT) myocardial perfusion imaging (MPI) provide complementary anatomical and functional information for coronary artery disease (CAD). However, the prognostic value of integrating CCTA-derived coronary imaging characteristics with PET-MPI parameters remains to be established. Theref...

Establishment of a Multimodal Prognostic Prediction Model for Multiple Myeloma Patients Based on Radiomics and Clinical Features: A Retrospective Cohort Study.

OBJECTIVE: We aimed to develop a survival prediction system integrating radiomics and clinical features for newly diagnosed multiple myeloma (MM), and...

α-Synuclein Seeds Amplification Assays for Parkinson's Disease and Related Synucleinopathies.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by the pathological misfolding and aggregation of α-synuclein (α-sy...

A Generative Multi-modal Deep Learning Framework for Non-invasive Prediction of Ki-67 Expression in Papillary Thyroid Microcarcinoma Using Preoperative Ultrasound.

Accurate assessment of Ki-67 expression plays a critical role in the risk stratification and prognosis of papillary thyroid microcarcinoma (PTMC). How...

A Hybrid CNN-Transformer Model with Crossover Boosted Cheetah Optimization for Prenatal Spina Bifida Identification from Ultrasound Images.

OBJECTIVES: Spina bifida is a birth defect caused by the incomplete closure of the neural tube around the spinal cord. Earlier, various deep learning-...

Automated ventricular and midline segmentation in cranial ultrasound with metrology.

Ultrasound imaging through sonolucent cranial implants is an emerging modality for post-neurosurgical monitoring of the adult brain, but quantitative ...

A Noise-Aware Robustness Evaluation Framework for Breast Cancer Classification in Ultrasound Imaging.

Accurate detection of breast cancer is essential, as it remains a leading cause of cancer-related mortality worldwide. Ultrasound is adopted due to it...

Multiparametric PET/CT Tensor Radiomics for Stability-Aware Machine Learning in Lung Cancer Survival Prediction.

To address the challenges of model instability and limited generalizability in radiomics-based lung cancer prognosis, we developed a robust multiparam...

Multi-component exercise intervention methods for intelligent assisted diagnosis of sarcopenia in the elderly based on deep learning.

Population aging has made sarcopenia a major public health challenge in geriatric medicine. Current clinical diagnosis relies on large specialized equ...

RCAF for patient-level thyroid ultrasound malignancy prediction under leakage-free evaluation and calibration.

Accurate differentiation between benign and malignant thyroid nodules on ultrasound remains clinically important, yet interpretation is operator-depen...

MINeR: Direction-modulated implicit neural representation enables ultrafast multi-shell diffusion MRI.

Diffusion magnetic resonance imaging (dMRI) enables noninvasive mapping of tissue microstructure by probing water molecule diffusivity. While advanced...

MR habitat radiomics for differentiating indeterminate (Grade 3) nasopharyngeal lesions: lymphoid hyperplasia versus early-stage nasopharyngeal carcinoma.

BACKGROUND: To investigate the feasibility of MRI-based habitat radiomics to differentiate indeterminate Grade 3 nasopharyngeal lesions into nasophary...

Predicting preoperative lymph node metastasis of hilar cholangiocarcinoma based on deep learning radiomics.

BACKGROUND: The objective of this study was to evaluate whether deep learning radiomic (DLR) models utilizing B-mode ultrasound (BUS) and contrast-enh...

Comparison of Super-Resolution Deep-Learning Reconstruction and Hybrid Iterative Reconstruction for Coronary Stent Assessment on CTA: A Prospective Multicenter Study.

Background: Conventional deep-learning reconstruction (DLR) CT methods provide noise reduction but limited improvements in spatial resolution. Objecti...

Arthroscopy-Validated Diagnostic Performance of a Deep Learning Reconstruction Pipeline for Rapid 7-Minute Five-Sequence 3-T Knee MRI.

Background. Deep learning reconstruction (DLR) methods can enhance image quality and reduce scan time of knee MRI compared with conventional approache...

Accelerating prostate MRI: a practical review of modern acquisition strategies.

Prostate MRI has become a key tool in the detection, localization, and staging of prostate cancer, as well as in active surveillance. Despite its diag...

Automated generation of impressions in abdominal radiology reports using an artificial intelligence-based tool: performance compared to manual impressions.

OBJECTIVES: Recent years have seen a rapid development of artificial intelligence (AI) tools to enhance radiologists' workflow, but most have focused ...

Image optimization for low-dose 18F-FDG breast PET/MRI using deep learning: a pilot study.

BACKGROUND: Positron emission tomography with magnetic resonance imaging (PET/MRI) provides noninvasive molecular characterization of breast cancer an...

A comparative evaluation of brain MRI modalities, modality-specific measures, brain regions, and cognitive tests for brain age prediction.

Brain age estimation using machine learning has gained significant attention as a promising approach to assess cognitive health and aging. By analyzin...