ArticleAnnals of translational medicine2021
Value of artificial intelligence model based on unenhanced computed tomography of urinary tract for preoperative prediction of calcium oxalate monohydrate stones
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 19 citations in OpenAlex.
- A prediction model for 30-day mortality of sepsis patients based on intravenous fluids and electrolytes.Medicine · 2022Trial
- Machine learning integrating CT radiomics and clinical variables for preoperative prediction of renal uric acid stones: a model development and validation study.Translational andrology and urology · 2026Article
- Pre-operatively predicting kidney stone recurrence: integrating radiomic features and clinical variables using machine learning.BMC medical imaging · 2025Article
- Review
- CT-based deep learning model for predicting the success of extracorporeal shock wave lithotripsy in treating ureteral stones larger than 1 cm.Urolithiasis · 2024Article
- CT-based radiomics for predicting success of shock wave lithotripsy in ureteral stones larger than 1 cm.World journal of urology · 2024Article
- Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review.Asian journal of urology · 2023Review
- A combined model based on CT radiomics and clinical variables to predict uric acid calculi which have a good accuracy.Urolithiasis · 2023Article
- Identifying ureteral stent encrustation using machine learning based on CT radiomics features: a bicentric study.Frontiers in medicine · 2023Article
- Development and validation of prediction model using nursing notes on sentiment scores for prognosis of patients with severe acute kidney injury receiving continuous renal replacement therapy based on computational intelligence algorithms.Annals of translational medicine · 2022Article
- Radiomics in Urolithiasis: Systematic Review of Current Applications, Limitations, and Future Directions.Journal of clinical medicine · 2022Review
- Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith.International journal of clinical practice · 2022Article
- Prediction of readmission in patients with acute exacerbation of chronic obstructive pulmonary disease within one year after treatment and discharge.BMC pulmonary medicine · 2021Article
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUrolithiasis 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
methodsPreoperative 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.
resultsA 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.
conclusionsThe AI model based on unenhanced CT images of the urinary tract can predict COM and non-COM stones
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