A new model based on artificial intelligence to screening preterm birth

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.

Keywords:

• Preterm birth
• cervical length
• transvaginal ultrasound
• artificial intelligence

Acknowledgments

These authors would like to thank the Health and Medical Equipment Division of Samsung Brazil for offering the WS80A ultrasound system and HERA W9 ultrasound system to perform the exams during the study. We are also grateful to Mr. Rudolf Wiedemann for his support with the present article’s English version.

Author’s contribution

The authors’ contribution, the interpretation of the data, the article’s writing, the critical review of the intellectual content, and the final approval of the version to be published were similar. All authors accept responsibility for the paper as published.

Disclosure statement

The authors report no conflict of interest: Including relevant financial, personal, political, intellectual, or religious interests.

Data availability statement

Data available on request from the authors.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.