Radiology

Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging.

Machine-learning models for medical tasks can match or surpass the performance of clinical experts. However, in settings differing from those of the training dataset, the performance of a model can deteriorate substantially. Here we report a representation-learning strategy for machine-learning models applied to medical-imaging tasks that mitigates such 'out of distribution' performance problem an...

Using Deep Learning to Detect the Presence and Location of Hemoperitoneum on the Focused Assessment with Sonography in Trauma (FAST) Examination in Adults.

Abdominal ultrasonography has become an integral component of the evaluation of trauma patients. Internal hemorrhage can be rapidly diagnosed by finding free fluid with point-of-care ultrasound (POCUS) and expedite decisions to perform lifesaving interventions. However, the widespread clinical application of ultrasound is limited by the expertise required for image interpretation. This study aimed...

Joint liver and hepatic lesion segmentation in MRI using a hybrid CNN with transformer layers.

UNLABELLED: Backgound and Objective: Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical pr...

In situ sensing physiological properties of biological tissues using wireless miniature soft robots.

Implanted electronic sensors, compared with conventional medical imaging, allow monitoring of advanced physiological properties of soft biological tis...

Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study.

BACKGROUND: International societies have issued guidelines for high-risk breast cancer (BC) screening, recommending contrast-enhanced magnetic resonan...

Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images.

Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a gre...

A deep-learning model using enhanced chest CT images to predict PD-L1 expression in non-small-cell lung cancer patients.

AIM: To develop a deep-learning model using contrast-enhanced chest computed tomography (CT) images to predict programmed death-ligand 1 (PD-L1) expre...

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review.

Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assess...

Surgical Sealant with Integrated Shape-Morphing Dual Modality Ultrasound and Computed Tomography Sensors for Gastric Leak Detection.

Postoperative anastomotic leaks are the most feared complications after gastric surgery. For diagnostics clinicians mostly rely on clinical symptoms s...

3D surface reconstruction of cellular cryo-soft X-ray microscopy tomograms using semisupervised deep learning.

Cryo-soft X-ray tomography (cryo-SXT) is a powerful method to investigate the ultrastructure of cells, offering resolution in the tens of nanometer ra...

Deep learning phase error correction for cerebrovascular 4D flow MRI.

Background phase errors in 4D Flow MRI may negatively impact blood flow quantification. In this study, we assessed their impact on cerebrovascular flo...

Deep learning model for automatic image quality assessment in PET.

BACKGROUND: A variety of external factors might seriously degrade PET image quality and lead to inconsistent results. The aim of this study is to expl...

(MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects.

Robotic handling of objects is not always a trivial assignment, even in teleoperation where, in most cases, this might lead to stressful labor for ope...

A Two-Branch Neural Network for Short-Axis PET Image Quality Enhancement.

The axial field of view (FOV) is a key factor that affects the quality of PET images. Due to hardware FOV restrictions, conventional short-axis PET sc...

Deep Learning Accelerated Image Reconstruction of Fluid-Attenuated Inversion Recovery Sequence in Brain Imaging: Reduction of Acquisition Time and Improvement of Image Quality.

RATIONALE AND OBJECTIVES: Fluid-attenuated inversion recovery (FLAIR) imaging is playing an increasingly significant role in the detection of brain me...

A single stage knowledge distillation network for brain tumor segmentation on limited MR image modalities.

BACKGROUND AND OBJECTIVE: Precisely segmenting brain tumors using multimodal Magnetic Resonance Imaging (MRI) is an essential task for early diagnosis...

Validation of a deep learning model for traumatic brain injury detection and NIRIS grading on non-contrast CT: a multi-reader study with promising results and opportunities for improvement.

PURPOSE: This study aimed to assess and externally validate the performance of a deep learning (DL) model for the interpretation of non-contrast compu...

Noise reduction performance of a deep learning-based reconstruction in brain computed tomography images acquired with organ-based tube current modulation.

We aimed to evaluate the image quality of brain computed tomography (CT) images reconstructed using deep learning-based reconstruction (DLR) in organ-...

Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study.

BACKGROUND: Interstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic ...

Predicting FDG-PET Images From Multi-Contrast MRI Using Deep Learning in Patients With Brain Neoplasms.

BACKGROUND: F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is valuable for determining presence of viable tumor, but is limited by geo...