Aron F. H. A. Bakker

Objectives: To develop a deep learning method to quantify ureter perfusion during indocyanine green (ICG) fluoroscopy in robot-assisted radical cystectomy (RARC). Introduction: Ureteroenteric stricture (UES) is a common and clinically significant complication of RARC. Ureter ICG fluoroscopy likely reduces UES rates. However, its current reliance on subjective visual interpretation warrants a quantitative assessment method. Materials and Methods: A single-center retrospective pilot study was performed using 96 videos of RARC with ICG fluoroscopy, recorded between November 2019 and June 2024. Afterwards, 251 full-color and 358 ICG frames of suspended ureters were randomly split into 80/20% training and test sets and were annotated. A surgical foundation model, SurgeNetXL, which was readily pre-trained, was trained on the training sets. Performance was assessed using the Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). Each ureter segmentation was divided into 50 horizontal planes with green intensity sampling points to calculate five perfusion parameters, including time to peak and normalized peak slope. These parameters were combined into a single composite “perfusion score” per plane. A stable and manually annotated video was used for a proof of concept. Results: The model performed well in segmenting full-color ureters (DSC = 0.800, HD = 24 pixels). Regarding ureters in ICG fluoroscopy, the model also performed well (DSC = 0.767, HD = 61 pixels), despite worse visibility. There was a good subjective visual correspondence between the perfusion score graphical overlay and the green intensity in the video. Conclusions: This pre-clinical study shows promising steps toward automated assistance of ureter dissection plane determination during RARC, aimed at reducing UES in a data-driven manner. Additional steps are required to improve the technique and to enable fully automated intensity measurements, which are needed to study correlations with UES outcomes.

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.