Min Soo Choo1, Saangyong Uhmn1, Jong Keun Kim1, Jun Hyun Han1, Dong-Hoi Kim1, Jin Kim1, Seong Ho Lee2. 1. Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Geonggi-do, Republic of Korea; Department of Computer Engineering, Hallym University (SU, DHK, JK), Chuncheon, Gangwon-do, Republic of Korea. 2. Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Geonggi-do, Republic of Korea; Department of Computer Engineering, Hallym University (SU, DHK, JK), Chuncheon, Gangwon-do, Republic of Korea. Electronic address: shleeuro@hallym.ac.kr.
Abstract
PURPOSE: The aim of this study was to develop and validate a decision support model using a machine learning algorithm to predict treatment success after single session shock wave lithotripsy in ureteral stone cases. MATERIALS AND METHODS: Of the 1,803 patients treated with shock wave lithotripsy we selected those with ureteral stones who had preoperative computerized tomography available. Treatment success after single session shock wave lithotripsy was defined as freedom from stones or residual stone fragments less than 2 mm long on computerized tomography or plain x-ray of the kidneys, ureters and bladder 2 weeks later. Decision tree analysis was done using a machine learning algorithm to identify relevant parameters. A decision support model was developed to calculate the probability of treatment success. RESULTS: A total of 791 patients were enrolled in study. Mean ± SD stone length was 5.9 ± 2.3 mm and mean stone volume was 89.3 ± 140.0 mm3. The overall treatment success rate after SWL was 64.4% (509 cases). The rate for upper, middle and lower ureter stones was 59.8%, 65.5% and 69.6%, respectively. On decision tree analysis the top 3 performance criteria factors were volume, length and HU. Decision models were constructed with all possible combinations of factors. The model with 15 factors had greater than 92% accuracy and an average ROC AUC of 0.951. CONCLUSIONS: We applied a machine learning algorithm, a subfield of artificial intelligence, to predict the outcome after single session shock wave lithotripsy for ureteral stones. A 92.29% accurate decision model was developed with 15 factors and an average ROC AUC of 0.951.
PURPOSE: The aim of this study was to develop and validate a decision support model using a machine learning algorithm to predict treatment success after single session shock wave lithotripsy in ureteral stone cases. MATERIALS AND METHODS: Of the 1,803 patients treated with shock wave lithotripsy we selected those with ureteral stones who had preoperative computerized tomography available. Treatment success after single session shock wave lithotripsy was defined as freedom from stones or residual stone fragments less than 2 mm long on computerized tomography or plain x-ray of the kidneys, ureters and bladder 2 weeks later. Decision tree analysis was done using a machine learning algorithm to identify relevant parameters. A decision support model was developed to calculate the probability of treatment success. RESULTS: A total of 791 patients were enrolled in study. Mean ± SD stone length was 5.9 ± 2.3 mm and mean stone volume was 89.3 ± 140.0 mm3. The overall treatment success rate after SWL was 64.4% (509 cases). The rate for upper, middle and lower ureter stones was 59.8%, 65.5% and 69.6%, respectively. On decision tree analysis the top 3 performance criteria factors were volume, length and HU. Decision models were constructed with all possible combinations of factors. The model with 15 factors had greater than 92% accuracy and an average ROC AUC of 0.951. CONCLUSIONS: We applied a machine learning algorithm, a subfield of artificial intelligence, to predict the outcome after single session shock wave lithotripsy for ureteral stones. A 92.29% accurate decision model was developed with 15 factors and an average ROC AUC of 0.951.
Authors: Andrew B Chen; Taseen Haque; Sidney Roberts; Sirisha Rambhatla; Giovanni Cacciamani; Prokar Dasgupta; Andrew J Hung Journal: Urol Clin North Am Date: 2021-10-23 Impact factor: 2.766
Authors: K Günzel; A Magheli; E Baco; H Cash; S Heinrich; H Neubert; J Schlegel; M Schostak; T Henkel; P Asbach; S Hinz Journal: World J Urol Date: 2021-04-18 Impact factor: 4.226