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

OBJECTIVES: 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