USE OF A NEURAL-NETWORK TO PREDICT STONE GROWTH AFTER SHOCK-WAVE LITHOTRIPSY

Objectives. To determine whether a neural network is superior to stand ard computational methods in predicting stone regrowth after shock wav e lithotripsy (SWL) and to determine whether the presence of residual fragments, as an independent variable, increases risk. Methods. We rev iewed the records of 98 patients with renal or ureteral calculi treate d by primary SWL at a single institution and followed up for at least 1 year; residual stone fragment growth or new stone occurrence was det ermined from abdominal radiographs. A neural network was programmed an d trained to predict an increased stone volume over time utilizing inp ut variables, including previous stone events, metabolic abnormality, directed medical therapy, infection, caliectasis, and residual fragmen ts after SWL. Patient data were partitioned into a training set of 65 examples and a test set of 33. The neural network did not encounter th e test set until training was complete. Results. The average follow-up period was 3.5 years (range 1 to 10). Of 98 patients, 47 had residual stone fragments 3 months after SWL; of these 47, 8 had increased ston e volume at last follow-up visit. Of 51 patients stone free after SWL, 4 had stone recurrence. Coexisting risk factors were incorporated int o a neural computational model to determine which of the risk factors was individually predictive of stone growth. The classification accura cy of the neural model in the test set was 91%, with a sensitivity of 91%, a specificity of 92%, and a receiver operating characteristic cur ve area of 0.964, results significantly better than those yielded by l inear and quadratic discriminant function analysis. Conclusions. A com putational tool was developed to predict accurately the risk of future stone activity in patients treated by SWL. Use of the neural network demonstrates that none of the risk factors for stone growth, including the presence of residual fragments, is individually predictive of con tinuing stone formation. (C) 1998, Elsevier Science Inc. All rights re served.