Artificial intelligence application in the prediction of spontaneous preterm birth by cervical length in the first trimester of pregnancy: Comparison of three measurement methods.

YI-YUN TAITseng, Bor-YannBor-YannTsengYang, Zhu-HanZhu-HanYangYu, Chi-HuaChi-HuaYuPoon, Liona CLiona CPoon2026-02-022026-02-022026-01-09https://scholars.lib.ntu.edu.tw/handle/123456789/735732Objectives: The current study evaluates the efficacy of artificial intelligence (AI)–assisted measurement of cervical length (CL) in predicting spontaneous preterm birth (sPTB), comparing the traditional single-line and two-line methods with the innovative AI-line method in the first trimester of pregnancy. Materials and Methods: This study is a retrospective secondary analysis of ultrasound images collected prospectively from women with a viable singleton pregnancy who were undergoing Down syndrome screening at Prince of Wales Hospital, Hong Kong SAR. CL was measured using transvaginal ultrasound, with a secondary analysis of archived 1664 images acquired during a prospective study and processed through a ResUNet-based model. This model, combining UNet and ResNet architectures, a modified ResUNet framework, aimed to overcome the limitations of current measurement techniques by providing a more accurate prediction of CL, particularly in cases where the cervix is curved. Results: The AI-line method demonstrated superior accuracy in predicting sPTB at <37 and <32 weeks of gestation compared with conventional methods, with higher areas under the receiver operating characteristic curve (AUROC). The AUROC of CL measured by the AI-line method (0.676 [95% CI, 0.616–0.735], P < 0.05) in predicting sPTB at <37 weeks of gestation was significantly higher than the single-line (0.537 [95% CI, 0.474–0.6]) and two-line (0.54 [95% CI, 0.473–0.66]) methods. For the prediction of sPTB at <32 weeks of gestation, the AI-line method achieved an AUROC of 0.777 (95% CI, 0.703–0.850). Conclusion: The AI-line method offers a more accurate measurement of CL in the first trimester, showing potential as a tool for early screening of sPTB risk. The study's results could significantly influence clinical decision-making, providing a basis for the potential future clinical application of AI in prenatal care.enResUNet modelartificial intelligencecervical length measurementspontaneous preterm birthtransvaginal ultrasound[SDGs]SDG3Artificial intelligence application in the prediction of spontaneous preterm birth by cervical length in the first trimester of pregnancy: Comparison of three measurement methods.journal article10.1002/ijgo.7074441510574