International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026
Artificial intelligence application in the prediction of spontaneous preterm birth by cervical length in the first trimester of pregnancy: Comparison of three measurement methods.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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Tai Yi-Yun, Tseng Bor-Yann, Yang Zhu-Han, Yu Chi-Hua, Poon Liona C
Аннотация
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 <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.
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.
Предметные рубрики MeSH
Secondary Data AnalysisRetrospective StudiesProspective StudiesHong KongReproducibility of ResultsPredictive Value of TestsArtificial IntelligencePremature BirthPregnancy Trimester, FirstDiagnosis, Computer-AssistedHumansFemalePregnancyAdultCervical Length MeasurementCervix UteriROC CurveGestational AgeDown Syndrome