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Your search keyword '"de Vos, B. D."' showing total 8 results
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8 results on '"de Vos, B. D."'

2. Improved patient selection for primary prevention ICD implantation by predicting ICD non-benefit using artificial intelligence

Author

Kolk, M Z H, primary, Ruiperez-Campillo, S, additional, Deb, B, additional, Bekkers, E J, additional, De Vos, B D, additional, Van Der Lingen, A C J, additional, Allaart, C P, additional, Isgum, I, additional, Rogers, A J, additional, Clopton, P, additional, Wilde, A A M, additional, Knops, R E, additional, Narayan, S M, additional, and Tjong, F V Y, additional

Published
2023
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3. Deep learning body-composition analysis of clinically acquired CT-scans estimates creatinine excretion with high accuracy in patients and healthy individuals

Author

Nefro Vasculaire Geneeskunde, MS Radiologie, Circulatory Health, Cancer, Researchgr. Cardiovasculaire Radiologie, Regenerative Medicine and Stem Cells, CDL Cluster Speciële Diagnostiek, MS Nefrologie, Pieters, Tobias T., Veldhuis, W. B., Moeskops, P., de Vos, B. D., Verhaar, M. C., Haitjema, S., Huitema, A. D.R., Rookmaaker, M. B., Nefro Vasculaire Geneeskunde, MS Radiologie, Circulatory Health, Cancer, Researchgr. Cardiovasculaire Radiologie, Regenerative Medicine and Stem Cells, CDL Cluster Speciële Diagnostiek, MS Nefrologie, Pieters, Tobias T., Veldhuis, W. B., Moeskops, P., de Vos, B. D., Verhaar, M. C., Haitjema, S., Huitema, A. D.R., and Rookmaaker, M. B.

Published
2022

6. State-of-the-Art Deep Learning in Cardiovascular Image Analysis

Author

Beeldverwerking ISI, Circulatory Health, Researchgr. Cardiovasculaire Radiologie, Cancer, Brain, Litjens, G., Ciompi, Francesco, Wolterink, Jelmer M., de Vos, B. D., Leiner, Tim, Teuwen, J., Išgum, I., Beeldverwerking ISI, Circulatory Health, Researchgr. Cardiovasculaire Radiologie, Cancer, Brain, Litjens, G., Ciompi, Francesco, Wolterink, Jelmer M., de Vos, B. D., Leiner, Tim, Teuwen, J., and Išgum, I.

Published
2019

7. Automatic Coronary Artery Calcium Scoring on Radiotherapy Planning CT Scans of Breast Cancer Patients: Reproducibility and Association with Traditional Cardiovascular Risk Factors

Author

Gernaat, S A M, Isgum, I, de Vos, B D, Takx, RAP, Young-Afat, D A, Rijnberg, N, Grobbee, DE, van der Graaf, Y, Jong, PA, Leiner, T, van den Bongard, D H J, Pignol, Jean-Philippe, Verkooijen, HM, Gernaat, S A M, Isgum, I, de Vos, B D, Takx, RAP, Young-Afat, D A, Rijnberg, N, Grobbee, DE, van der Graaf, Y, Jong, PA, Leiner, T, van den Bongard, D H J, Pignol, Jean-Philippe, and Verkooijen, HM

Published
2016

8. Deep Learning-Based Data-Point Precise R-Peak Detection in Single-Lead Electrocardiograms.

Author

Oudkerk Pool MD, de Vos BD, Winter MM, and Isgum I

Subjects
Algorithms, Electrocardiography, Humans, Neural Networks, Computer, Atrial Fibrillation diagnosis, Deep Learning
Abstract

Low-cost wearables with capability to record electrocardiograms (ECG) are becoming increasingly available. These wearables typically acquire single-lead ECGs that are mainly used for screening of cardiac arrhythmias such as atrial fibrillation. Most arrhythmias are characteruzed by changes in the RR-interval, hence automatic methods to diagnose arrythmia may utilize R-peak detection. Existing R-peak detection methods are fairly accurate but have limited precision. To enable data-point precise detection of R-peaks, we propose a method that uses a fully convolutional dilated neural network. The network is trained and evaluated with manually annotated R-peaks in a heterogeneous set of ECGs that contain a wide range of cardiac rhythms and acquisition noise. 700 randomly chosen ECGs from the PhysioNet/CinC challenge 2017 were used for training (n=500), validation (n=100) and testing (n=100). The network achieves a precision of 0.910, recall of 0.926, and an F1-score of 0.918 on the test set. Our data-point precise R-peak detector may be important step towards fully automatic cardiac arrhythmia detection.Clinical relevance- This method enables data-point precise detection of R-peaks that provides a basis for detection and characterization of arrhythmias.

Published
2021
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