Machine Learning Analysis of CT Images for the Prediction of Extracorporeal Shock Wave Lithotripsy Efficacy
Yuanchao Cao1, Hang Yuan1, Yi Qiao1
1Department of Urology, Affiliated Hospital of Qingdao University, 266003 Qingdao, Shandong, China.
Background:
The study aimed to evaluate the use decision support analysis for the prediction of extracorporeal shock wave lithotripsy (ESWL) efficacy and to analyze the factors influencing outcomes in patients who underwent ESWL using machine learning (ML) methods.
Methods:
This retrospective study analyzed the clinical data, including preoperative computed tomography (CT) images, of 302 patients who received a single ESWL session treatment for urinary stone between May and October 2022 in the Department of Urology. The data was preprocessed and incorporated into an ML model, and the dataset was validated at a ratio of 4:1. The area under the curve (AUC) and the confusion matrix were used to evaluate the predictive efficacy of the model.
Results:
The CT image-based ML model predicting ESWL efficacy for urinary stone removal achieved an AUC of 0.86, precision of 88.33%, F1 score of 86.57%, sensitivity of 82.86%, and specificity of 88.89%. The model achieved an AUC of 0.95 for kidney stones, with 95.45% precision, 96.00% F1 score, 100.00% sensitivity, and 90.00% specificity. The AUC value for upper ureteral stones was 0.89, with 89.14% precision, 88.05% F1 score, 83.33% sensitivity, and 94.51% specificity, while that for mid-ureteral stones was 0.85, with 82.93% precision, 84.09% F1 score, 74.00% sensitivity, and 96.88% specificity, and the success rate of ESWL for lower ureteral stones was 100.00%, with an AUC of 1.00.
Conclusions:
ML analysis was used to predict outcomes following ESWL treatment for urinary stone. The ML-based model achieved an AUC of 0.86. The use of ML algorithms can provide matched insights to domain knowledge on effective and influential factors for the prediction of ESWL outcomes.
