Health Technology/clinical-trials

Danish Study Evaluates AI Preterm Birth Prediction Across Different Cervical Ultrasound Settings — Clinical Practice Implications

247 Health News Editorial

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28 May 2026

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3 min read

·clinical-trials

Danish researchers are evaluating how different ultrasound imaging settings affect AI model performance in predicting preterm birth. This 2,000-patient study addresses critical questions about implementing AI diagnostic tools in routine obstetric practice.

Originally reported by ClinicalTrials.gov

References

  1. 1.Rigshospitalet. (2024). The Impact of Image Acquisition in Cervical Ultrasound on AI-Based Prediction of Preterm Birth in Clinical Practice: A Prospective Observational Study. ClinicalTrials.gov. https://clinicaltrials.gov/study/NCT07598097
  2. 2.Iams, J. D., Goldenberg, R. L., Meis, P. J., Mercer, B. M., Moawad, A., Das, A., ... & Caritis, S. N. (1996). The length of the cervix and the risk of spontaneous premature delivery. New England Journal of Medicine, 334(9), 567-572. https://doi.org/10.1056/NEJM199602293340904
  3. 3.Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1814259
  4. 4.Berghella, V., Palacio, M., Ness, A., Alfirevic, Z., Nicolaides, K. H., & Saccone, G. (2009). Cervical length screening for prevention of preterm birth in singleton pregnancy with threatened preterm labor. American Journal of Obstetrics and Gynecology, 200(5), 486-e1. https://doi.org/10.1016/j.ajog.2009.06.015
  5. 5.Society for Maternal-Fetal Medicine. (2021). SMFM Special Statement: Implementation of artificial intelligence (AI) in obstetrics and gynecology. American Journal of Obstetrics and Gynecology, 225(4), 387-392. https://doi.org/10.1016/j.ajog.2021.04.265
  6. 6.Chawanpaiboon, S., Vogel, J. P., Moller, A. B., Lumbiganon, P., Petzold, M., Hogan, D., ... & Gülmezoglu, A. M. (2019). Global, regional, and national estimates of levels of preterm birth in 2014. The Lancet Global Health, 7(1), e37-e46. https://doi.org/10.1016/S2214-109X(18)30451-0
  7. 7.Honest, H., Bachmann, L. M., Gupta, J. K., Kleijnen, J., & Khan, K. S. (2002). Accuracy of cervical transvaginal sonography in predicting preterm birth. Ultrasound in Obstetrics and Gynecology, 19(3), 305-311. https://doi.org/10.1046/j.1469-0705.2002.00811.x
  8. 8.Koivu, A., Sairanen, M., Airola, A., Laitinen, L., Savolainen, J., Halmesmäki, E., ... & Grönroos, T. (2022). Predicting risk of stillbirth and preterm pregnancies with machine learning. Scientific Reports, 12(1), 5354. https://doi.org/10.1038/s41598-022-10710-z
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247 Health News Editorial

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