A new model based on artificial intelligence to screening preterm birth

Objective The objective of this study is to create a new screening for spontaneous preterm birth (sPTB) based on artificial intelligence (AI). Methods This study included 524 singleton pregnancies from 18th to 24th-week gestation after transvaginal ultrasound cervical length (CL) analyzes for screening sPTB < 35 weeks. AI model was created based on the stacking-based ensemble learning method (SBELM) by the neural network, gathering CL < 25 mm, multivariate unadjusted logistic regression (LR), and the best AI algorithm. Receiver Operating Characteristics (ROC) curve to predict sPTB < 35 weeks and area under the curve (AUC), sensitivity, specificity, accuracy, predictive positive and negative values were performed to evaluate CL < 25 mm, LR, the best algorithms of AI and SBELM. Results The most relevant variables presented by LR were cervical funneling, index straight CL/internal angle inside the cervix (& LE; 0.200), previous PTB < 37 weeks, previous curettage, no antibiotic treatment during pregnancy, and weight (& LE; 58 kg), no smoking, and CL < 30.9 mm. Fixing 10% of false positive rate, CL < 25 mm and SBELM present, respectively: AUC of 0.318 and 0.808; sensitivity of 33.3% and 47,3%; specificity of 91.8 and 92.8%; positive predictive value of 23.1 and 32.7%; negative predictive value of 94.9 and 96.0%. This machine learning presented high statistical significance when compared to CL < 25 mm after T-test (p < .00001). Conclusion AI applied to clinical and ultrasonographic variables could be a viable option for screening of sPTB < 35 weeks, improving the performance of short cervix, with a low false-positive rate.