Clinical Research

AI-Driven Biometric Analysis for Preterm Birth Risk Assessment

Background

Preterm births (babies born earlier than expected) and stillbirths (babies dying before birth) are significant social concerns. Although various factors such as maternal age, infections, and genetic abnormalities have been identified as potential causes, many remain unknown.

The risk of preterm births is also increasing due to changes in social factors, such as the trend toward advanced maternal age. In 2020, a WHO report highlighted the global burden of preterm births, describing it as a ‘silent emergency’ due to the lack of improvement in preterm birth rates over recent decades.

Preterm births and stillbirths are not just individual issues; their societal impact is profound. The extensive costs associated with tertiary care and subsequent life support are significant. In the USA, over $20 billion are spent annually on preterm birth management, while in the UK, nearly £2.5 billion are allocated, underscoring the substantial societal impact of preterm births.

Overview of the Research

This study aims to support medical professionals by analyzing biometric data to identify high-risk preterm birth patients in out-of-hospital settings, where medical resources are limited. By leveraging easily obtainable biometric information, the study uses AI to assess the risk of preterm birth.

The research will help to illuminate the “black box” of pregnancy outside hospital environments, potentially reducing unnecessary medical interventions and ensuring that medical resources are optimally allocated to those truly at high risk.

About the Research Results

We are currently preparing for a presentation at an academic conference and the publication of our first clinical research paper. The findings will also be shared on this page.

Inquiries

If participants in this study have any questions, please contact us using the form provided in the consent document.

Please note that due to privacy protection considerations, we are unable to respond to inquiries about this clinical research through the ‘Contact Us’ page on this website. We appreciate your understanding.

For further information, please refer to our privacy policy available at the following link.

<references>

1.  Dagklis T, Akolekar R, Villalain C, Tsakiridis I, Kesrouani A, Tekay A, et al. Management of preterm labor: Clinical practice guideline and recommendation by the WAPM-World Association of Perinatal Medicine and the PMF-Perinatal Medicine Foundation. Eur J Obstet Gynecol Reprod Biol. 2023 Dec;291:196–205.

2.  Haruyama R, Gilmour S, Ota E, Abe SK, Rahman MM, Nomura S, et al. Causes and risk factors for singleton stillbirth in Japan: Analysis of a nationwide perinatal database, 2013–2014. Sci Rep. 2018 Mar 7;8(1):4117.

3. Vohr BR, Heyne R, Bann C, Das A, Higgins RD, Hintz SR, et al. High Blood Pressure at Early School Age Among Extreme Preterms. Pediatrics. 2018 Aug;142(2):e20180269.

4.  Hovi P, Andersson S, Eriksson JG, Järvenpää AL, Strang-Karlsson S, Mäkitie O, et al. Glucose regulation in young adults with very low birth weight. N Engl J Med. 2007 May;356(20):2053–63.

5.  Meertens LJE, van Montfort P, Scheepers HCJ, van Kuijk SMJ, Aardenburg R, Langenveld J, et al. Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation. Acta Obstet Gynecol Scand. 2018 Aug;97(8):907–20.

6.  Honest H, Bachmann LM, Sundaram R, Gupta JK, Kleijnen J, Khan KS. The accuracy of risk scores in predicting preterm birth–a systematic review. J Obstet Gynaecol J Inst Obstet Gynaecol. 2004 Jun;24(4):343–59.

7.  Bellussi F, Po’ G, Livi A, Saccone G, De Vivo V, Oliver EA, et al. Fetal Movement Counting and Perinatal Mortality: A Systematic Review and Meta-analysis. Obstet Gynecol. 2020 Feb;135(2):453–62.