Radiology

Implementing a deep learning model for automatic tongue tumour segmentation in ex-vivo 3-dimensional ultrasound volumes.

Three-dimensional (3D) ultrasound can assess the margins of resected tongue carcinoma during surgery. Manual segmentation (MS) is time-consuming, labour-intensive, and subject to operator variability. This study aims to investigate use of a 3D deep learning model for fast intraoperative segmentation of tongue carcinoma in 3D ultrasound volumes. Additionally, it investigates the clinical effect of ...

TSCA-Net: Transformer based spatial-channel attention segmentation network for medical images.

Deep learning architectures based on convolutional neural network (CNN) and Transformer have achieved great success in medical image segmentation. Models based on the encoder-decoder framework like U-Net have been successfully employed in many realistic scenarios. However, due to the low contrast between object and background, various shapes and scales of objects, and complex background in medical...

MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study.

To develop and validate an MRI radiomics-based decision support tool for the automated grading of cervical disc degeneration. The retrospective stud...

Cystic renal mass screening: machine-learning-based radiomics on unenhanced computed tomography.

PURPOSE: The present study compares the diagnostic performance of unenhanced computed tomography (CT) radiomics-based machine learning (ML) classifier...

Artificial Intelligence for PET and SPECT Image Enhancement.

Nuclear medicine imaging modalities such as PET and SPECT are confounded by high noise levels and low spatial resolution, necessitating postreconstruc...

A validation of an entropy-based artificial intelligence for ultrasound data in breast tumors.

BACKGROUND: The application of artificial intelligence (AI) in the ultrasound (US) diagnosis of breast cancer (BCa) is increasingly prevalent. However...

Application of an artificial intelligence-based system in the diagnosis of breast ultrasound images obtained using a smartphone.

BACKGROUND: Breast ultrasound (US) is useful for dense breasts, and the introduction of artificial intelligence (AI)-assisted diagnoses of breast US i...

Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis.

Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in importance since the advent of magnetic resonance im...

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer.

BACKGROUND: To establish a predictive model based on multisequence magnetic resonance imaging (MRI) using deep learning to identify wild-type (WT) epi...

Deep Generalized Learning Model for PET Image Reconstruction.

Low-count positron emission tomography (PET) imaging is challenging because of the ill-posedness of this inverse problem. Previous studies have demons...

High Throughput Tomography (HiTT) on EMBL beamline P14 on PETRA III.

Here, high-throughput tomography (HiTT), a fast and versatile phase-contrast imaging platform for life-science samples on the EMBL beamline P14 at DES...

Deep learning to overcome Zernike phase-contrast nanoCT artifacts for automated micro-nano porosity segmentation in bone.

Bone material contains a hierarchical network of micro- and nano-cavities and channels, known as the lacuna-canalicular network (LCN), that is thought...

Deep learning for automatic organ and tumor segmentation in nanomedicine pharmacokinetics.

: Multimodal imaging provides important pharmacokinetic and dosimetry information during nanomedicine development and optimization. However, accurate ...

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures.

Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing ...

Assessing the Capability of ChatGPT, Google Bard, and Microsoft Bing in Solving Radiology Case Vignettes.

 The field of radiology relies on accurate interpretation of medical images for effective diagnosis and patient care. Recent advancements in artifici...

Contrast-Induced Nephropathy in Interventional Cardiology: Incidence, Risk Factors, and Identification of High-Risk Patients.

AIM: This study aimed to study contrast-induced nephropathy (CIN) or more recent nomenclature contrast-associated acute kidney injury (CI-AKI) in pati...

Comparative Evaluation of Caudal Epidural Method to Ultrasound-Guided S1 Transforamen Block in Patient's Lumbar Discectomy with Failed Back Syndrome Symptoms: A Double-Blind Clinical Trial.

BACKGROUND: Post-lumbar surgery syndrome (PLSS) refers to persistent or recurrent pain following spinal surgery with an unknown cause. It is commonly ...

The Role of Pelvic Ultrasound in Evaluating the Success of Tension-free Vaginal Tape (TVT).

OBJECTIVES: This study aims to assess the lack of response to treatment in individuals undergoing mid-urethral sling surgery for stress urinary incont...

Sharing Patient Praises With Radiology Staff: Workflow Automation and Impact on Staff.

OBJECTIVE: This study aims to develop and evaluate a semi-automated workflow using natural language processing (NLP) for sharing positive patient feed...

Deep learning approach using SPECT-to-PET translation for attenuation correction in CT-less myocardial perfusion SPECT imaging.

OBJECTIVE: Deep learning approaches have attracted attention for improving the scoring accuracy in computed tomography-less single photon emission com...