Willem M. Brinkman
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Willem M. Brinkman has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Alexander. J. W. Beulens
Department of Urology, Catharina Hospital, Eindhoven
1 shared publication - Aron F. H. A. Bakker
University Medical Center Utrecht
1 shared publication - Fons van der Sommen
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
1 shared publication - Henk G. van der Poel
Tijdschrift voor Urologie, Bohn Stafleu van Loghum, Houten, Nederland
1 shared publication - Joris V. de Nijs
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
1 shared publication Michel Wyndaele
Afdeling Urologie Medische Volwassenen, Divisie Heelkundige Specialismen, UMC Utrecht, Utrecht, Nederland
1 shared publication
Latest Publications
- Open Access
- 1 September 2026
- Published by Springer Nature in Tijdschrift voor Urologie
- Vol. 16 (6-7) , 119-120
De uroloog mét AI vervangt de uroloog zonder AI
- 1 September 2026
- Published by Springer Nature in Tijdschrift voor Urologie
- Vol. 16 (6-7) , 119-120
Open AccessDe uroloog mét AI vervangt de uroloog zonder AI
- 1 July 2024
- Published by SAGE Publications in Journal of Endourology
- Vol. 38 (7) , 690-696
Estimating Surgical Urethral Length on Intraoperative Robot-Assisted Prostatectomy Images Using Artificial Intelligence Anatomy Recognition
Objective To construct a Convolutional Neural Network (CNN) model that can recognize and delineate anatomic structures on intraoperative video frames of robot-assisted radical prostatectomy (RARP) and to use these annotations to predict the surgical urethral length (SUL). Background Urethral dissection during RARP impacts patient urinary incontinence (UI) outcomes, and requires extensive training. Large differences exist between incontinence outcomes of different urologists and hospitals. Also, surgeon experience and education are critical towards optimal outcomes. Therefore new approaches are warranted. SUL is associated with UI. Artificial intelligence (AI) surgical image segmentation using a CNN could automate SUL estimation and contribute towards future AI-assisted RARP and surgeon guidance. Methods Eighty-eight intraoperative RARP videos between June 2009 and September 2014 were collected from a single center. 264 frames were annotated according to: prostate, urethra, ligated plexus and catheter. Thirty annotated images from different RARP videos were used as a test dataset. The Dice coefficient (DSC) and 95th percentile Hausdorff distance (Hd95) were used to determine model performance. SUL was calculated using the catheter as a reference. Results The DSC of the best performing model were 0.735 and 0.755 for the catheter and urethra classes respectively, with a Hd95 of 29.27 and 72.62 respectively. The model performed moderately on the ligated plexus and prostate. The predicted SUL showed a mean difference of 0.64 - 1.86mm difference versus human annotators, but with significant deviation (SD 3.28 - 3.56). Conclusion This study shows that an AI image segmentation model can predict vital structures during RARP urethral dissection with moderate to fair accuracy. SUL estimation derived from it showed large deviations and outliers when compared to human annotators, but with a very small mean difference (<2mm). This is a promising development for further research on AI-assisted RARP. Keywords Prostate cancer, Anatomy recognition, Artificial intelligence, Continence, Urethral length.
- 1 July 2024
- Published by SAGE Publications in Journal of Endourology
- Vol. 38 (7) , 690-696
Estimating Surgical Urethral Length on Intraoperative Robot-Assisted Prostatectomy Images Using Artificial Intelligence Anatomy Recognition
Objective To construct a Convolutional Neural Network (CNN) model that can recognize and delineate anatomic structures on intraoperative video frames of robot-assisted radical prostatectomy (RARP) and to use these annotations to predict the surgical urethral length (SUL). Background Urethral dissection during RARP impacts patient urinary incontinence (UI) outcomes, and requires extensive training. Large differences exist between incontinence outcomes of different urologists and hospitals. Also, surgeon experience and education are critical towards optimal outcomes. Therefore new approaches are warranted. SUL is associated with UI. Artificial intelligence (AI) surgical image segmentation using a CNN could automate SUL estimation and contribute towards future AI-assisted RARP and surgeon guidance. Methods Eighty-eight intraoperative RARP videos between June 2009 and September 2014 were collected from a single center. 264 frames were annotated according to: prostate, urethra, ligated plexus and catheter. Thirty annotated images from different RARP videos were used as a test dataset. The Dice coefficient (DSC) and 95th percentile Hausdorff distance (Hd95) were used to determine model performance. SUL was calculated using the catheter as a reference. Results The DSC of the best performing model were 0.735 and 0.755 for the catheter and urethra classes respectively, with a Hd95 of 29.27 and 72.62 respectively. The model performed moderately on the ligated plexus and prostate. The predicted SUL showed a mean difference of 0.64 - 1.86mm difference versus human annotators, but with significant deviation (SD 3.28 - 3.56). Conclusion This study shows that an AI image segmentation model can predict vital structures during RARP urethral dissection with moderate to fair accuracy. SUL estimation derived from it showed large deviations and outliers when compared to human annotators, but with a very small mean difference (<2mm). This is a promising development for further research on AI-assisted RARP. Keywords Prostate cancer, Anatomy recognition, Artificial intelligence, Continence, Urethral length.