A new model based on artificial intelligence to screening preterm birth.

Autor:	Andrade Júnior VL; Graduate and Postgraduate Department, Impacta School of Technology, São Paulo, Brazil., França MS; Screening and Prevention of Preterm Birth Sector, Discipline of Fetal Medicine, Department of Obstetrics, Paulista School of Medicine - Federal University of Sao Paulo (EPM-UNIFESP), São Paulo, Brazil., Santos RAF; Graduate and Postgraduate Department, Impacta School of Technology, São Paulo, Brazil., Hatanaka AR; Screening and Prevention of Preterm Birth Sector, Discipline of Fetal Medicine, Department of Obstetrics, Paulista School of Medicine - Federal University of Sao Paulo (EPM-UNIFESP), São Paulo, Brazil., Cruz JJ; Fetal Medicine Unit, Centro Hospitalar Universitário de Lisboa Central, Lisboa, Portugal., Hamamoto TEK; Screening and Prevention of Preterm Birth Sector, Discipline of Fetal Medicine, Department of Obstetrics, Paulista School of Medicine - Federal University of Sao Paulo (EPM-UNIFESP), São Paulo, Brazil., Traina E; Screening and Prevention of Preterm Birth Sector, Discipline of Fetal Medicine, Department of Obstetrics, Paulista School of Medicine - Federal University of Sao Paulo (EPM-UNIFESP), São Paulo, Brazil., Sarmento SGP; Department of Obstetrics and Gynecology, Medical School of Jundiaí (FMJ), Jundiaí, Brazil., Elito Júnior J; Screening and Prevention of Preterm Birth Sector, Discipline of Fetal Medicine, Department of Obstetrics, Paulista School of Medicine - Federal University of Sao Paulo (EPM-UNIFESP), São Paulo, Brazil., Pares DBDS; Screening and Prevention of Preterm Birth Sector, Discipline of Fetal Medicine, Department of Obstetrics, Paulista School of Medicine - Federal University of Sao Paulo (EPM-UNIFESP), São Paulo, Brazil., Mattar R; Screening and Prevention of Preterm Birth Sector, Discipline of Fetal Medicine, Department of Obstetrics, Paulista School of Medicine - Federal University of Sao Paulo (EPM-UNIFESP), São Paulo, Brazil., Araujo Júnior E; Screening and Prevention of Preterm Birth Sector, Discipline of Fetal Medicine, Department of Obstetrics, Paulista School of Medicine - Federal University of Sao Paulo (EPM-UNIFESP), São Paulo, Brazil., Moron AF; Screening and Prevention of Preterm Birth Sector, Discipline of Fetal Medicine, Department of Obstetrics, Paulista School of Medicine - Federal University of Sao Paulo (EPM-UNIFESP), São Paulo, Brazil.
Jazyk:	angličtina
Zdroj:	The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians [J Matern Fetal Neonatal Med] 2023 Dec; Vol. 36 (2), pp. 2241100.
DOI:	10.1080/14767058.2023.2241100
Abstrakt:	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.
Databáze:	MEDLINE
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