Radiology

[Population screening for acupuncture treatment of neck pain: a machine learning study].

OBJECTIVE: To screen the population for acupuncture treatment of neck pain, using functional magnetic resonance imaging (fMRI) technology and based on machine learning algorithms.

Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised learning.

Self-supervised learning (SSL) has gained attention in the medical field as a deep learning approach utilizing unlabeled data. The Jigsaw puzzle task in SSL enables models to learn both features of images and the positional relationships within images. In breast cancer diagnosis, radiologists evaluate not only lesion-specific features but also the surrounding breast structures. However, deep learn...

Clinicians' perspectives on the use of artificial intelligence to triage MRI brain scans.

Artificial intelligence (AI) tools can triage radiology scans to streamline the patient pathway and also relieve clinician workload. Validated AI tool...

Machine learning Nomogram for Predicting endometrial lesions after tamoxifen therapy in breast Cancer patients.

Objective Endometrial lesions are a frequent complication following breast cancer, and current diagnostic tools have limitations. This study aims to d...

MRI to digital medicine diagnosis: integrating deep learning into clinical decision-making for lumbar degenerative diseases.

INTRODUCTION: To develop an intelligent system based on artificial intelligence (AI) deep learning algorithms using deep learning tools, aiming to ass...

Utilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases.

OBJECTIVE: To develop a machine learning-based clinical and/or radiomics model for predicting the primary site of brain metastases using multiparametr...

Advances in physiological and clinical relevance of hiPSC-derived brain models for precision medicine pipelines.

Precision, or personalized, medicine aims to stratify patients based on variable pathogenic signatures to optimize the effectiveness of disease preven...

Explainable PET-Based Habitat and Peritumoral Machine Learning Model for Predicting Progression-free Survival in Clinical Stage IA Pure-Solid Non-small Cell Lung Cancer: A Two-center Study.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate machine learning (ML) models utilizing positron emission tomography (PET)-habitat o...

Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging.

Cardiovascular diseases (CVD) can be diagnosed using various diagnostic modalities. The electrocardiogram (ECG) is a cost-effective and widely availab...

Automated ultrasonography of hepatocellular carcinoma using discrete wavelet transform based deep-learning neural network.

This study introduces HCC-Net, a novel wavelet-based approach for the accurate diagnosis of hepatocellular carcinoma (HCC) from abdominal ultrasound (...

DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.

In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parc...

Integrating artificial intelligence with endoscopic ultrasound in the early detection of bilio-pancreatic lesions: Current advances and future prospects.

The integration of Artificial Intelligence (AI) in endoscopic ultrasound (EUS) represents a transformative advancement in the early detection and mana...

Machine learning-based interpretation of non-contrast feature tracking strain analysis and T1/T2 mapping for assessing myocardial viability.

Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium enhancement (LGE) cardiovascular magnetic resonan...

Deep learning-based pelvimetry in pelvic MRI volumes for pre-operative difficulty assessment of total mesorectal excision.

BACKGROUND: Specific pelvic bone dimensions have been identified as predictors of total mesorectal excision (TME) difficulty and outcomes. However, ma...

Multi-institutional development and testing of attention-enhanced deep learning segmentation of thyroid nodules on ultrasound.

PURPOSE: Thyroid nodules are common, and ultrasound-based risk stratification using ACR's TIRADS classification is a key step in predicting nodule pat...

Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images.

Detecting brain tumours (BT) early improves treatment possibilities and increases patient survival rates. Magnetic resonance imaging (MRI) scanning of...

Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images.

Hepatic cystic echinococcosis (HCE), a life-threatening liver disease, has 5 subtypes, i.e., single-cystic, polycystic, internal capsule collapse, sol...

Deep-Learning-Based Reconstruction of Single-Breath-Hold 3 mm HASTE Improves Abdominal Image Quality and Reduces Acquisition Time: A Quantitative Analysis.

Breath-hold T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) magnetic resonance imaging (MRI) of the upper abdomen with a sli...

Dual-Stage AI Model for Enhanced CT Imaging: Precision Segmentation of Kidney and Tumors.

OBJECTIVES: Accurate kidney and tumor segmentation of computed tomography (CT) scans is vital for diagnosis and treatment, but manual methods are time...

ReIU: an efficient preliminary framework for Alzheimer patients based on multi-model data.

The rising incidence of Alzheimer's disease (AD) poses significant challenges to traditional diagnostic methods, which primarily rely on neuropsycholo...