What question did this study set out to answer?

The aim is to create a predictive system for kidney stone fragmentation using NCCT images.

July 12, 2016Open Access

Prediction of fragmentation of kidney stones: A statistical approach from NCCT images

Key Points

The aim is to create a predictive system for kidney stone fragmentation using NCCT images.
Extracted features using first order statistical methods from NCCT images.
Predicted breakability of stones using a trained neural network.
Analyzed data from patients undergoing extracorporeal shockwave lithotripsy.
Achieved 80.7% sensitivity in true positive cases with the predictive model.
The accuracy rate for correctly identifying true positive and true negative cases was 90%.
True negative cases showed a specificity of 98.4%.

Abstract

Introduction: We sought to develop a system to predict the fragmentation of stones using non-contrast computed tomography (NCCT) image analysis of patients with renal stone disease.Methods: The features corresponding to first order statistical (FOS) method were extracted from the region of interest in the NCCT scan image of patients undergoing extracorporeal shockwave lithotripsy (ESWL) treatment and the breakability was predicted using neural network. Results: When mean was considered as the feature, the results indicated that the model developed for prediction had sensitivity of 80.7% in true positive (TP) cases. The percent accuracy in identifying correctly the TP and true negative (TN) cases was 90%. TN cases were identified with a specificity of 98.4%.Conclusions: Application of statistical methods and training the neural network system will enable accurate prediction of the fragmentation and outcome of ESWL treatment.

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