Background: Accurate preoperative assessment of ureteral length is crucial for effective ureteral stenting., Purpose: Utilize a deep learning approach to measure ureter length on CT urography (CTU) images and compare the obtained results with those derived from other estimation methods., Methods: In a retrospective cohort (cohort A, n = 411), CTU images were collected and used to develop a 3D deep learning model for the segmentation of bilateral ureters. The centerline of the ureters was determined based on the segmentation, and the length of the ureters was automatically obtained (CTU_ai). Another cohort (cohort B, n = 220) was collected as the hold-out test for the model. All patients in cohort B had KUB, non-contrast enhanced CT (CT NoC), and CTU images. Cohort B utilized eight measurement methods, with one annotated by two radiologists serving as the reference standard (CTU_ref) and the remaining seven as the studied methods, including three measurement methods applied to CTU (CTU_ai, CTU_oblique, CTU_slice), two applied to CT NoC (CT_oblique, CT_slice), and two applied to KUB (KUB_short, KUB_long). The results of the seven studied methods were compared to those of the reference in cohort B., Results: Among the 220 patients (96 females, 124 males), 437 ureters were measured for length (218 left, 219 right), with a median length of 24.7 (IQR 23.2-26.2) cm. No significant differences were observed between genders or laterality (both P > 0.05). Moreover, there was no correlation between ureteral length and age (r = -0.027, P = 0.573). The ureteral length measured by CTU_ai was not significantly different from that measured by CTU_ref (P = 0.514), whereas the length measured by the other studied methods was significantly different from that measured by CTU_ref (all P < 0.001). The ICC values with their 95 % confidence intervals (CIs) for the comparison between the reference standard (CTU_ref) and the other measurement methods: CTU_ai (ICC = 0.852, 95 % CI 0.825-0.876), CTU_oblique (ICC = 0.351, 95 % CI -0.083-0.689), CTU_slice (ICC = 0.269, 95 % CI -0.095-0.573), CTU_oblique_slice (ICC = 0.059, 95 % CI -0.032-0.218), CTU_slice (ICC = 0.049, 95 % CI -0.028-0.188), KUB_short (ICC = 0.151, 95 % CI 0.051-0.247), and KUB_long (ICC = 0.147, 95 % CI 0.034-0.253). For CTU_ai, in 89.0 % of the ureters, the ureteral length deviation was within 20 mm of the reference standard, which was the highest among all the studied methods (all P < 0.001)., Conclusion: The deep learning model offers a reliable and accurate tool for ureteral length measurement on CTU images, which could enhance the effectiveness of ureteral stenting procedures. Its performance surpasses traditional measurement methods, making it a promising technology for integration into clinical practice., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: He Wang reports financial support was provided by Peking University First Hospital. Pengsheng Wu, Jialun Li reports a relationship with Beijing Smart Tree Medical Technology Co., Ltd. that includes: employment. The work they did in this study is unrelated to the company's business. Oher author: None Declared., (Copyright © 2024. Published by Elsevier Ltd.)