Radiology

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review.

OBJECTIVES: Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imag...

Robot-assisted ultrasound reconstruction for spine surgery: from bench-top to pre-clinical study.

PURPOSE: Robot-assisted ultrasound (rUS) systems have already been used to provide non-radiative three-dimensional (3D) reconstructions that form the basis for guiding spine surgical procedures. Despite promising studies on this technology, there are few studies that offer insight into the robustness and generality of the approach by verifying performance in various testing scenarios. Therefore, t...

Diagnosis of Developmental Dysplasia of the Hip by Ultrasound Imaging Using Deep Learning.

BACKGROUND: A timely diagnosis of developmental dysplasia of the hip (DDH) is important for satisfactory clinical outcomes. Ultrasonography is a usefu...

A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features.

One of the most severe types of cancer caused by the uncontrollable proliferation of brain cells inside the skull is brain tumors. Hence, a fast and a...

Precise Brain-shift Prediction by New Combination of W-Net Deep Learning for Neurosurgical Navigation.

Brain tissue deformation during surgery significantly reduces the accuracy of image-guided neurosurgeries. We generated updated magnetic resonance ima...

Automated identification of piglet brain tissue from MRI images using Region-based Convolutional Neural Networks.

Magnetic resonance imaging is an important tool for characterizing volumetric changes of the piglet brain during development. Typically, an early step...

Eliminating the need for manual segmentation to determine size and volume from MRI. A proof of concept on segmenting the lateral ventricles.

Manual segmentation, which is tedious, time-consuming, and operator-dependent, is currently used as the gold standard to validate automatic and semiau...

Benign vs malignant vertebral compression fractures with MRI: a comparison between automatic deep learning network and radiologist's assessment.

OBJECTIVE: To test the diagnostic performance of a deep-learning Two-Stream Compare and Contrast Network (TSCCN) model for differentiating benign and ...

Boosting multiple sclerosis lesion segmentation through attention mechanism.

Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis and monitoring its progression. Although several attempts ...

Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network.

Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality...

Artificial intelligence in radiology - beyond the black box.

BACKGROUND: Artificial intelligence is playing an increasingly important role in radiology. However, more and more often it is no longer possible to r...

Deep learning-based PET/MR radiomics for the classification of annualized relapse rate in multiple sclerosis.

Background Annualized Relapse Rate (ARR) is one of the most important indicators of disease progression in patients with Multiple Sclerosis (MS). Howe...

A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy.

Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal mod...

Is ultrasound-guided hip aspiration more successful than fluoroscopic-guided aspiration in diagnosing prosthetic joint infection?

: aspiration of total hip arthroplasty (THA) is commonly performed to assist in the diagnosis of prosthetic joint infection (PJI). This study aimed to...

Imaging of early-stage osteoarthritis: the needs and challenges for diagnosis and classification.

In an effort to boost the development of new management strategies for OA, there is currently a shift in focus towards the diagnosis and treatment of ...

Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm.

This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional ...

Explainable AI in radiology: a white paper of the Italian Society of Medical and Interventional Radiology.

The term Explainable Artificial Intelligence (xAI) groups together the scientific body of knowledge developed while searching for methods to explain t...

Automatic detection and recognition of nasopharynx gross tumour volume (GTVnx) by deep learning for nasopharyngeal cancer radiotherapy through magnetic resonance imaging.

BACKGROUND: In this study, we propose the deep learning model-based framework to automatically delineate nasopharynx gross tumor volume (GTVnx) in MRI...

Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

OBJECTIVES: Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to pr...

Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review.

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor s...