Radiology

Leveraging fuzzy embedded wavelet neural network with multi-criteria decision-making approach for coronary artery disease prediction using biomedical data.

Coronary artery disease (CAD) is the main cause of death. It is a complex heart disease that is linked with many risk factors and a variety of symptoms. In the past few years, CAD has experienced a remarkable growth. Prompt risk prediction of CAD would be capable of decreasing the death rate by permitting timely and targeted treatments. Angiography is the most precise CAD diagnosis technique; howe...

Deep learning radiomics on grayscale ultrasound images assists in diagnosing benign and malignant of BI-RADS 4 lesions.

This study aimed to explore a deep learning radiomics (DLR) model based on grayscale ultrasound images to assist radiologists in distinguishing between benign breast lesions (BBL) and malignant breast lesions (MBL). A total of 382 patients with breast lesions were included, comprising 183 benign lesions and 199 malignant lesions that were collected and confirmed through clinical pathology or biops...

Overfit detection method for deep neural networks trained to beamform ultrasound images.

Deep neural networks (DNNs) have remarkable potential to reconstruct ultrasound images. However, this promise can suffer from overfitting to training ...

Role of Artificial Intelligence for Endoscopic Ultrasound.

Endoscopic ultrasound (EUS) is widely used for the diagnosis of biliopancreatic and gastrointestinal tract diseases, but it is one of the most operato...

Descriptive overview of AI applications in x-ray imaging and radiotherapy.

Artificial intelligence (AI) is transforming medical radiation applications by handling complex data, learning patterns, and making accurate predictio...

Recent Advances and Future Directions in Sonodynamic Therapy for Cancer Treatment.

Deep-tissue solid cancer treatment has a poor prognosis, resulting in a very low 5-year patient survival rate. The primary challenges facing solid tum...

Role of artificial intelligence in magnetic resonance imaging-based detection of temporomandibular joint disorder: a systematic review.

This systematic review aimed to evaluate the application of artificial intelligence (AI) in the identification of temporomandibular joint (TMJ) disc p...

MRI-derived radiomics and end-to-end deep learning models for predicting glioma ATRX status: a systematic review and meta-analysis of diagnostic test accuracy studies.

We aimed to systematically review and meta-analyze the predictive value of magnetic resonance imaging (MRI)-derived radiomics/end-to-end deep learning...

Latent representation learning for classification of the Doppler ultrasound images.

The classification of Doppler ultrasound images plays an important role in the diagnosis of pregnancy. However, it is a challenging problem that suffe...

Diagnosis of intracranial aneurysms by computed tomography angiography using deep learning-based detection and segmentation.

BACKGROUND: Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a laborious task.

Accuracy of machine learning models for pre-diagnosis and diagnosis of pancreatic ductal adenocarcinoma in contrast-CT images: a systematic review and meta-analysis.

PURPOSE: To evaluate the diagnostic ability and methodological quality of ML models in detecting Pancreatic Ductal Adenocarcinoma (PDAC) in Contrast C...

Diagnosis of major depressive disorder using a novel interpretable GCN model based on resting state fMRI.

The diagnosis and analysis of major depressive disorder (MDD) faces some intractable challenges such as dataset limitations and clinical variability. ...

A wrapper method for finding optimal subset of multimodal Magnetic Resonance Imaging sequences for ischemic stroke lesion segmentation.

Multimodal data, while being information-rich, contains complementary as well as redundant information. Depending on the target problem some modalitie...

Prior-FOVNet: A Multimodal Deep Learning Framework for Megavoltage Computed Tomography Truncation Artifact Correction and Field-of-View Extension.

Megavoltage computed tomography (MVCT) plays a crucial role in patient positioning and dose reconstruction during tomotherapy. However, due to the lim...

Multi-modal cross-domain self-supervised pre-training for fMRI and EEG fusion.

Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have shown promise in detecting function...

Artificial intelligence and MRI in sinonasal tumors discrimination: where do we stand?

BACKGROUND: Artificial intelligence (AI) demonstrates high potential when applied to radiomic analysis of magnetic resonance imaging (MRI) to discrimi...

Predicting lymph node metastasis in thyroid cancer: systematic review and meta-analysis on the CT/MRI-based radiomics and deep learning models.

BACKGROUND: Thyroid cancer, a common endocrine malignancy, has seen increasing incidence, making lymph node metastasis (LNM) a critical factor for rec...

A feature fusion method based on radiomic features and revised deep features for improving tumor prediction in ultrasound images.

BACKGROUND: Radiomic features and deep features are both vitally helpful for the accurate prediction of tumor information in breast ultrasound. Howeve...

Ensemble learning-based radiomics model for discriminating brain metastasis from glioblastoma.

OBJECTIVE: Differentiating between brain metastasis (BM) and glioblastoma (GBM) preoperatively is challenging due to their similar imaging features on...

Longitudinal interpretability of deep learning based breast cancer risk prediction.

Deep-learning-based models have achieved state-of-the-art breast cancer risk (BCR) prediction performance. However, these models are highly complex, a...