Validation of a Deep Learning Model for Automated Ureteral Length Measurement on CT Against Gross Specimens

This study validates a deep learning model for automated ureteral length measurement on CT using gross specimen measurements as the reference standard. A deep learning model was trained on 3629 CT series from 1502 patients (cohort 1) to segment the ureter and extract centerline length. An independent prospective cohort (cohort 2) of 70 patients undergoing radical nephroureterectomy served as the hold-out test set. Each gross specimen was measured twice by one urologist (D1_length and D2_length), with their mean as the reference length (Ref_length). Model-predicted lengths (AI_length) were compared with Ref_length using repeated-measures one-way ANOVA with Tukey post hoc tests. Determinants of discrepancy were assessed with Spearman correlation and multiple linear regression. Marginal means (± SE) were 266 ± 3.85 mm (D1_length), 273 ± 3.25 mm (D2_length), 269 ± 2.75 mm (AI_length), and 269 ± 3.45 mm (Ref_length). Measurement method had a statistically significant but negligible effect on length (F(3, 207) = 2.73, p = 0.045, η²G = 0.007). D1_length and D2_length differed significantly from Ref_length (both p = 0.002), whereas AI_length was not statistically different from Ref_length (p > 0.999). In regression analysis, reference ureteral length was the only independently associated variable (coefficient - 0.54 mm per mm of reference length, 95% CI - 0.70 to - 0.39; p < 0.001); age, sex, body height, body weight, operation side, and lesion location were not significantly associated. The proposed deep learning pipeline achieved accurate fully automated ureteral segmentation and length measurement across diverse CT acquisition phases and demonstrated close agreement with surgical gross specimen measurements in a prospective clinical cohort, supporting its potential to assist preoperative planning and reduce reliance on manual measurement. The measurement discrepancy was governed primarily by ureteral length itself-with a small systematic underestimation in longer ureters-rather than by patient or procedural factors. However, this tendency, together with the single-center design, and the modest size of the prospective cohort, warrants further multicenter validation before routine clinical deployment.