Abstract Purpose: We sought to assess the performance of the Recurrence of Kidney Stones nomogram for risk stratification of recurrence in a retrospective study. Materials and Methods: We performed a case-control study of 200 patients (100 with and 100 without subsequent recurrence) who underwent kidney stone surgery between 2013-2015, with at least 5 years of follow-up. We analyzed the performance of the 2018 ROKS nomogram via area under the receiver operating curve (ROC-AUC) for predicting 2- and 5-year stone recurrence. We evaluated the nomogram’s ability to stratify patients based on low or high risk of recurrence at: a) an optimized cutoff threshold (i.e. optimized for both sensitivity and specificity), and b) a sensitive cutoff threshold (i.e. high sensitivity (0.80) and low specificity). Time-to-recurrence data were estimated using the Kaplan Meier method. Results: The ROKS nomogram demonstrated fair ability to predict recurrence at 2 and 5 years (ROC-AUC of 0.67 and 0.63, respectively). At the optimized cutoff threshold, recurrence rates for the low and high-risk groups were 20 % and 45% at two years, and 50% and 70% at five years, respectively. At the sensitive cutoff threshold, the corresponding recurrence rates for the low and high-risk groups was of 16% and 38% at two years, and 42% and 66% at five years, respectively. Kaplan-Meier analysis revealed a significant recurrence-free advantage between the groups for both cutoff thresholds (p<0.01, Fig. 2). Conclusions: The ROKS nomogram could serve as a tool for recurrence risk stratification into lower and higher risk groups and facilitate adherence to risk-based follow-up protocols.
Objective: To evaluate the performance of computer vision models for automated kidney stone segmentation during flexible ureteroscopy and laser lithotripsy. Materials and Methods: We collected 20 ureteroscopy videos of intrarenal kidney stone treatment and extracted frames (N = 578) from these videos. We manually annotated kidney stones on each frame. Eighty percent of the data were used to train three standard computer vision models (U-Net, U-Net++, and DenseNet) for automatic stone segmentation during flexible ureteroscopy. The remaining data (20%) were used to compare performance of the three models after optimization through Dice coefficients and binary cross entropy. We identified the highest performing model and evaluated automatic segmentation performance during ureteroscopy for both stone localization and treatment using a separate set of endoscopic videos. We evaluated performance of the pixel-based analysis using area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and positive predictive value both in previously recorded videos and in real time. Results: A computer vision model (U-Net++) was evaluated, trained, and optimized for kidney stone segmentation during ureteroscopy using 20 surgical videos (mean video duration of 22 seconds, standard deviation ±13 seconds). The model showed good performance for stone localization with both digital ureteroscopes (AUC-ROC: 0.98) and fiberoptic ureteroscopes (AUC-ROC: 0.93). Furthermore, the model was able to accurately segment stones and stone fragments <270 μm in diameter during laser fragmentation (AUC-ROC: 0.87) and dusting (AUC-ROC: 0.77). The model automatically annotated videos intraoperatively in three cases and could do so in real time at 30 frames per second (FPS). Conclusion: Computer vision models demonstrate strong performance for automatic stone segmentation during ureteroscopy. Automatically annotating new videos at 30 FPS demonstrate the feasibility of real-time application during surgery, which could facilitate tracking tools for stone treatment.
