Publications by authors named "B van Ginneken"
Background: Acute abdominal pain (AAP) constitutes 5-10% of all emergency department (ED) visits, with appendicitis being a prevalent AAP etiology often necessitating surgical intervention. The variability in AAP symptoms and causes, combined with the challenge of identifying appendicitis, complicate timely intervention. To estimate the risk of appendicitis, scoring systems such as the Alvarado score have been developed.
View Article and Find Full Text PDFObjectives: The main objective was to develop and evaluate an artificial intelligence model for tooth segmentation in magnetic resonance (MR) scans.
Methods: MR scans of 20 patients performed with a commercial 64-channel head coil with a T1-weighted 3D-SPACE (Sampling Perfection with Application Optimized Contrasts using different flip angle Evolution) sequence were included. Sixteen datasets were used for model training and 4 for accuracy evaluation.
Article Synopsis
- A novel convolutional neural network (CNN) algorithm was developed for detecting and staging secondary caries in bitewings, as limited research exists in this area.
- The algorithm was trained using data from a Dutch dental practice, with a dataset of 2,612 restored teeth and various analytical methods to assess detection accuracy and lesion severity.
- Results showed high specificity for detecting lesions, with a correlation coefficient indicating a good agreement between the algorithm's severity scores and expert evaluations, suggesting potential for clinical use.
Article Synopsis
- Biparametric MRI (bpMRI) may serve as a valid alternative to multiparametric MRI (mpMRI) for diagnosing clinically significant prostate cancer (csPCa), as assessed in a large international observer study.
- The study involved 400 mpMRI examinations from four different European centers, where readers evaluated both bpMRI and mpMRI for their ability to accurately diagnose csPCa, finding them to be similarly effective.
- Key findings indicated that bpMRI and mpMRI had comparable diagnostic accuracy (AUROC values) and sensitivity, with bpMRI showing a noninferior performance, though both methods had similar specificity when distinguishing csPCa.
Objectives: The assessment of lumbar central canal stenosis (LCCS) is crucial for diagnosing and planning treatment for patients with low back pain and neurogenic pain. However, manual assessment methods are time-consuming, variable, and require axial MRIs. The aim of this study is to develop and validate an AI-based model that automatically classifies LCCS using sagittal T2-weighted MRIs.
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