OBJECTIVE: The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands.
Studies in health technology and informatics
32570541
This paper discusses a study that aimed to elicit promising application areas and potential business models for social robotics in healthcare. For this goal, we conducted focus groups with care professionals and the management of elderly care organiz...
At the so-called in-bore, MRI-guided prostate biopsy, the radiologist in the MRI suite manually directs a rectal biopsy needle guide at an abnormality confirmed by a previous prostate MRI. This manual technique of taking a biopsy is time-consuming an...
BACKGROUND: Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging in clinical practice. Algorithms for decision support in the recognition of neonatal seizures could ...
This paper describes a novel dataset of iconic gestures, together with a publicly available robot-based elicitation method to record these gestures, which consists of playing a game of charades with a humanoid robot. The game was deployed at a scienc...
BACKGROUND & OBJECTIVE: Mathematical modeling is the most scientific technique to understand the evolution of natural phenomena, including the spread of infectious diseases. Therefore, these modeling tools have been widely used in epidemiology for pr...
INTRODUCTION: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an in...
Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical met...
Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage of convolutional neural networks (CNNs) to automatically extract relevant features from the patient's...