A warning system for urolithiasis via retrograde intrarenal surgery using machine learning: an experimental study

Abstract

Background To develop a warning system that can prevent or minimize laser exposure resulting in kidney and ureter damage during retrograde intrarenal surgery (RIRS) for urolithiasis. Our study builds on the hypothesis that shock waves of different degrees are delivered to the hand of the surgeon depending on whether the laser hits the stone or tissue. Methods A surgical environment was simulated for RIRS by filling the body of a raw whole chicken with water and stones from the human body. We developed an acceleration measurement system that recorded the power signal data for a number of hours, yielding distinguishable characteristics among three different states (idle state, stones, and tissue–laser interface) by conducting fast Fourier transform (FFT) analysis. A discrete wavelet transform (DWT) was used for feature extraction, and a random forest classification algorithm was applied to classify the current state of the laser-tissue interface. Results The result of the FFT showed that the magnitude spectrum is different within the frequency range of < 2500 Hz, indicating that the different states are distinguishable. Each recorded signal was cut in only 0.5-s increments and transformed using the DWT. The transformed data were entered into a random forest classifier to train the model. The test result was only measured with the dataset that was isolated from the training dataset. The maximum average test accuracy was > 95%. The procedure was repeated with random signal dummy data, resulting in an average accuracy of 33.33% and proving that the proposed method caused no bias. Conclusions Our monitoring system receives the shockwave signals generated from the RIRS urolithiasis treatment procedure and generates the laser irradiance status by rapidly recognizing (in 0.5 s) the current laser exposure state with high accuracy (95%). We postulate that this can significantly minimize surgeon error during RIRS.

Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review

“…[ 56 ] Optimization of PCNL puncture Cross-sectional ANN model achieved a better localization and puncture method selection compared to the MVRA model and the surgeon's experience No comparator Jeong et al. [ 57 ] Optimization of RIRS safety profile Experimental Recognition of tissue exposure to laser energy with accuracy of 95% and latency time of 0.5 s No comparator AI, artificial intelligence; ANN, artificial neural network; ESWL, extracorporeal shockwave lithotripsy; MVRA, multiple variable regression analysis; PCNL, percutaneous nephrolithotomy; RIRS, retrograde intrarenal surgery; SE, standard error. …”

“…Recently, Jeong et al. [ 57 ] developed a monitoring system for reducing tissue exposure to laser energy during retrograde intrarenal surgery, based on measuring the specific shockwave form produced during the interaction of laser with a soft (tissue) or hard (stone) material and AI. Shockwave measurement was performed through an accelerometer adapted to the ureteroscope and the data were produced in a simulated surgical environment.…”

“…Shockwave measurement was performed through an accelerometer adapted to the ureteroscope and the data were produced in a simulated surgical environment. The above data were introduced in a ML model in order to enhance its predicting ability for tissue exposure and subsequent damage by laser energy [ 57 ]. The trained model was capable to warn surgeon of damage-inducing tissue exposure to laser energy with a latency time of 0.5 s and an accuracy of 95%.…”