Development and validation of a deep learning-based automated computed tomography image segmentation and diagnostic model for infectious hydronephrosis: a retrospective multicentre cohort study.

Background: Accurately diagnosing whether hydronephrosis is complicated by infection is crucial for guiding appropriate clinical treatment. This study aimed to develop a fully automated segmentation and non-invasive diagnostic model for infectious hydronephrosis (IH) using CT images and a deep learning algorithm. Methods: A retrospective analysis of clinical information and annotated cross-sectional CT images from patients diagnosed with hydronephrosis between June 2, 2019 and June 30, 2024 at the Sun Yat-Sen Memorial Hospital (SYSMH), Heyuan People's Hospital (HPH), and Ganzhou People's Hospital (GPH) was performed. Data on cases of hydronephrosis were extracted from the hospital's medical record system. The SYSMH cohort was randomly divided into two subsets: the SYSMH training set (n = 279) and the SYSMH validation set (n = 93) in a 3:1 ratio. The HPH cohort and GPH cohort serve as external validation sets. A hydronephrosis segmentation model (HRSM) was developed using the Improved U-Net algorithm, and the segmentation accuracy evaluated by the Dice Similarity Coefficient (DSC). Using 3D Convolutional Neural Network established an IH risk score (IHRS) based on segmented images. Independent risk clinical data for IH were screened by logistic regression. An IH diagnostic model (IHDM) was then developed, incorporating the IHRS and clinical data, using five machine learning algorithms (Random Forests, K-Nearest Neighbor, Decision Tree, Logistic Regression and Support Vector Machine). The diagnostic performance of the IHDM was assessed by the Receiver Operating Characteristic (ROC) curve. Findings: The study initially included 1464 potential eligible cases, of which 864 were deemed qualified after preliminary examination. Ultimately, a total of 615 patients (363 female and 252 male) with hydronephrosis (including 5876 annotated cross-sectional CT images) were included in the study, 372 of whom were from SYSMH, 123 from HPH, and 120 from GPH. Based on bacterial culture results from percutaneous nephrostomy drainage of hydronephrosis, 291 cases were classified as IH, while 324 were non-IH. The DSC for the HRSM in the internal and two external validation cohorts were 0.922 (95% CI: 0.895, 0.949), 0.906 (95% CI: 0.869, 0.943), and 0.883 (95% CI: 0.857, 0.909), respectively, indicating high segmentation accuracy. The IHRS achieved a prediction accuracy of 78.5% (95% CI: 78.1%-78.9%) in the internal validation set. The IHDM developed using Support Vector Machine (SVM) combination with blood neutrophil count, fever within one week of history and IHRS performed best, demonstrated areas under the ROC curve of 0.919 (95% CI: 0.859-0.980), 0.902 (95% CI: 0.849-0.955), and 0.863 (95% CI: 0.800-0.926) in three cohorts, respectively. Interpretation: The automated HRSM demonstrated excellent segmentation performance for hydronephrosis, while the non-invasive IHDM provided significant diagnostic efficacy, facilitating infection assessment in patients with hydronephrosis. However, more diverse real-world multicenter validation studies are needed to verify the robustness of the model before it can be incorporated into clinical practice. Funding: The Key-Area Research and Development Program of Guangdong Province, and the National Natural Science Foundation of China.