Computer Vision Enabled Segmentation of Kidney Stones During Ureteroscopy and Laser Lithotripsy.

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.

Feasibility of stone recurrence risk stratification using the recurrence of kidney stone (ROKS) nomogram.

This study seeks to evaluate the recurrence of kidney stones (ROKS) nomogram for risk stratification of recurrence in a retrospective study. To do this, we analyzed the performance of the 2018 ROKS nomogram in a case-control study of 200 patients (100 with and 100 without subsequent recurrence). All patients underwent kidney stone surgery between 2013 and 2015 and had at least 5 years of follow-up. We evaluated ROKS performance for prediction of recurrence at 2- and 5-year via area under the receiver operating curve (ROC-AUC). Specifically, we assessed the nomogram's potential for stratifying 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). We found fair performance of the nomogram for recurrence prediction 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 2 years, and 50 and 70% at 5 years, respectively. At the sensitive cutoff threshold, the corresponding recurrence rates for the low and high-risk groups were of 16 and 38% at 2 years, and 42 and 66% at 5 years, respectively. Kaplan-Meier analysis revealed a recurrence-free advantage between the groups for both cutoff thresholds (p < 0.01, Fig. 2). Therefore, we believe that the ROKS nomogram could facilitate risk stratification for stone recurrence and adherence to risk-based surveillance protocols.

Machine Learning Models to Predict 24 Hour Urinary Abnormalities for Kidney Stone Disease.

OBJECTIVE: To help guide empiric therapy for kidney stone disease, we sought to demonstrate the feasibility of predicting 24-hour urine abnormalities using machine learning methods. METHODS: We trained a machine learning model (XGBoost [XG]) to predict 24-hour urine abnormalities from electronic health record-derived data (n = 1314). The machine learning model was compared to a logistic regression model [LR]. Additionally, an ensemble (EN) model combining both XG and LR models was evaluated as well. Models predicted binary 24-hour urine values for volume, sodium, oxalate, calcium, uric acid, and citrate; as well as a multiclass prediction of pH. We evaluated performance using area under the receiver operating curve (AUC-ROC) and identified predictors for each model. RESULTS: The XG model was able to discriminate 24-hour urine abnormalities with fair performance, comparable to LR. The XG model most accurately predicted abnormalities of urine volume (accuracy = 98%, AUC-ROC = 0.59), uric acid (69%, 0.73) and elevated urine sodium (71%, 0.79). The LR model outperformed the XG model alone in prediction of abnormalities of urinary pH (AUC-ROC of 0.66 vs 0.57) and citrate (0.69 vs 0.64). The EN model most accurately predicted abnormalities of oxalate (accuracy = 65%, ROC-AUC = 0.70) and citrate (65%, 0.69) with overall similar predictive performance to either XG or LR alone. Body mass index, age, and gender were the three most important features for training the models for all outcomes. CONCLUSION: Urine chemistry prediction for kidney stone disease appears to be feasible with machine learning methods. Further optimization of the performance could facilitate dietary or pharmacologic prevention.