Extracorporeal Shock Wave Lithotripsy: Principles of Fragmentation Techniques

Doğan, Hasan Serkan; Tekgül, Serdar

doi:10.1007/978-1-4471-5394-8_27

Cited by 1 publication

(2 citation statements)

References 36 publications

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“…The limitation of the study was that the model was based on one urologist at a single institution, and a single instrument and the approach has not been externally validated. The Dongan and Tekgul [ 63 ] predicted stone-free rates and complication rates. Yanaral et al [ 64 ] argued that both Onal et al [ 62 ] and Dongan and Tekgul [ 63 ] studies could be improved by the addition of variables such as stone density, degree of obstruction, shock power, and number of shocks applied.…”

Section: Discussionmentioning

confidence: 99%

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Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients

Noble,

Hamilton,

Gerber

2024

PLoS ONE

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Kidney stones form when mineral salts crystallize in the urinary tract. While most stones exit the body in the urine stream, some can block the ureteropelvic junction or ureters, leading to severe lower back pain, blood in the urine, vomiting, and painful urination. Imaging technologies, such as X-rays or ureterorenoscopy (URS), are typically used to detect kidney stones. Subsequently, these stones are fragmented into smaller pieces using shock wave lithotripsy (SWL) or laser URS. Both treatments yield subtly different patient outcomes. To predict successful stone removal and complication outcomes, Artificial Neural Network models were trained on 15,126 SWL and 2,116 URS patient records. These records include patient metrics like Body Mass Index and age, as well as treatment outcomes obtained using various medical instruments and healthcare professionals. Due to the low number of outcome failures in the data (e.g., treatment complications), Nearest Neighbor and Synthetic Minority Oversampling Technique (SMOTE) models were implemented to improve prediction accuracies. To reduce noise in the predictions, ensemble modeling was employed. The average prediction accuracies based on Confusion Matrices for SWL stone removal and treatment complications were 84.8% and 95.0%, respectively, while those for URS were 89.0% and 92.2%, respectively. The average prediction accuracies for SWL based on Area-Under-the-Curve were 74.7% and 62.9%, respectively, while those for URS were 77.2% and 78.9%, respectively. Taken together, the approach yielded moderate to high accurate predictions, regardless of treatment or outcome. These models were incorporated into a Stone Decision Engine web application (http://peteranoble.com/webapps.html) that suggests the best interventions to healthcare providers based on individual patient metrics.

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Section: Discussionmentioning

confidence: 99%

“…The Dongan and Tekgul [ 63 ] predicted stone-free rates and complication rates. Yanaral et al [ 64 ] argued that both Onal et al [ 62 ] and Dongan and Tekgul [ 63 ] studies could be improved by the addition of variables such as stone density, degree of obstruction, shock power, and number of shocks applied.…”

Section: Discussionmentioning

confidence: 99%