A new model based on artificial intelligence to screening preterm birth
Valter Lacerda de Andrade, Marcelo Santucci França, Roberto Ângelo Fernandes Santos, Alan Roberto Hatanaka, J. Cruz, Tatiana Emy Kawanami Hamamoto, Évelyn Trainá, Stéphanno Gomes Pereira Sarmento, Júlio Elito, David Baptista da Silva Pares, Rosiane Mattar, Edward Araujo Júnior, Antônio Fernandes Moron
Abstract
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 (≤ 0.200), previous PTB < 37 weeks, previous curettage, no antibiotic treatment during pregnancy, and weight (≤ 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.