Deep learning–based automated cervical length measurement improves spontaneous preterm birth risk stratification in women with a borderline short cervix (20–25 mm) at mid‐trimester: A retrospective cohort study

Abstract

Abstract Objective This study evaluates whether deep learning (DL)‐based automated cervical length (CL) measurement provides improved risk stratification for spontaneous preterm birth (PTB), compared with conventional CL measurement methods, in women with a borderline short cervix by conventional methods (CL 20–25 mm) on mid‐trimester ultrasound. Methods This retrospective cohort study was conducted in a tertiary referral hospital in South Korea between January 2019 and December 2023. Women with a conventionally measured borderline short cervix were reanalyzed using a previously validated DL‐based algorithm (CL‐Net) that traces the cervical canal, and were reclassified as DL‐Normal (DL‐CL > 25 mm) and DL‐Short (DL‐CL ≤ 25 mm) groups. The primary outcome was the risk of PTB before 37 weeks of gestation. Discrimination of conventional and DL‐based CL was compared using receiver operating characteristic (ROC) curves with the DeLong test, and the incremental value of DL‐based measurement was assessed by reclassification analyses. Results Of 171 women, 39 (22.8%) were reclassified into a DL‐Short and 132 (77.2%) into a DL‐Normal group. PTB before 37 weeks occurred in 28.2% of the DL‐Short group and 6.1% of the DL‐Normal group ( P < 0.01), and a DL‐Short cervix was independently associated with PTB (adjusted odds ratio, 6.37; 95% confidence interval, 2.28–17.84). DL‐based CL showed better discrimination than conventional CL (area under the curve, 0.70 vs. 0.59; DeLong P = 0.042), with no evidence of poor calibration (Hosmer–Lemeshow, P = 0.36). The DL‐Short group had a higher PTB rate despite greater progesterone use. For a DL‐based CL ≤ 25 mm, sensitivity, specificity, positive predictive value, and negative predictive value for PTB were 57.9%, 81.6%, 28.2%, and 93.9%, respectively. Conclusion Deep learning‐based CL assessment, which follows the true anatomical pathway of the cervical canal, provides an objective and reproducible way to refine PTB risk stratification in women with a borderline short cervix. By identifying women at high risk of PTB more precisely than conventional methods, this approach could inform clinical practice, potentially helping to avoid unnecessary interventions in lower‐risk women while ensuring appropriate preventive care for at risk patients with a borderline short cervix.