Abstract
BACKGROUND: Urolithiasis is a global disease with a high incidence and recurrence rate, and stone composition is closely related to the choice of treatment and preventive measures. Calcium oxalate monohydrate (COM) is the most common in clinical practice, which is hard and difficult to fragment. Preoperative identification of its components and selection of effective surgical methods can reduce the risk of patients having a second operation. Methods that can be used for stone composition analysis include infrared spectroscopy, X-ray diffraction, and polarized light microscopy, but they are all performed on stone specimens in vitro after surgery. This study aimed to design and develop an artificial intelligence (AI) model based on unenhanced computed tomography (CT) images of the urinary tract, and to investigate the predictive ability of the model for COM stones in vivo preoperatively, so as to provide surgeons with more accurate diagnostic information. METHODS: Preoperative unenhanced CT images of patients with urinary calculi whose components were determined by infrared spectroscopy in a single center were retrospectively analyzed, including 337 cases of COM stones and 170 of non-COM stones. All images were manually segmented and the image features were extracted, and randomly divided into the training and testing sets in a ratio of 7:3. The least absolute shrinkage and selection operation algorithm (LASSO) was used to construct the AI model, and classification of the training and testing sets was carried out. RESULTS: A total of 1,218 radiomics imaging features were extracted, and 8 features with non-zero coefficients were finally obtained. The sensitivity, specificity and accuracy of the AI model were 90.5%, 84.3% and 88.5% for the training set, and 90.1%, 84.3% and 88.3% for the testing set. The area under the curve was 0.935 for the training set and 0.933 for the testing set. CONCLUSIONS: The AI model based on unenhanced CT images of the urinary tract can predict COM and non-COM stones in vivo preoperatively, and the model has high sensitivity, specificity and accuracy.