Resources
Prenaital is a spin-out from the University of Copenhagen and the Technical University of Denmark (DTU), building on world-class medical AI research to transform prenatal diagnostics. Our technology is scientifically validated, ensuring accurate, unbiased, and robust detection of high-risk pregnancies.
Tolsgaard, M. G., Svendsen, M. B. S., Thybo, J. K., Petersen, O. B., Sundberg, K. M., & Christensen, A. N. (2021). Does artificial intelligence for classifying ultrasound imaging generalize between different populations and contexts? Ultrasound in Obstetrics and Gynecology, 57(2), 342-343. John Wiley & Sons Ltd. Read
Andreasen, L. A., Feragen, A., Christensen, A. N., Thybo, J. K., Svendsen, M. B.S., Zepf, K., Lekadir, K., & Tolsgaard, M. G. (2023). Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization. Scientific Reports, 13(1), 2221. Nature Publishing Group UK London. Read
Taksøe‐Vester, C. A., Mikolaj, K., Petersen, O. B. B., Vejlstrup, N. G., Christensen, A. N., Feragen, A., Nielsen, M., Svendsen, M. B. S., & Tolsgaard, M. G. (2024). Role of AI‐assisted automated cardiac biometrics in screening for fetal coarctation of aorta. Ultrasound in Obstetrics & Gynecology. John Wiley & Sons Ltd. Read
Bashir, Z., Lin, M., Feragen, A., MikolajeK., Taksoee-Vester, C., Christensen, A. N., Svendsen, M. B. S., Fabricius, M. H., Andreasen, L., & Nielsen, M. (2025). Clinical validation of explainable AI for fetal growth scans through multi-level, cross- institutional prospective end-user evaluation. Scientific Reports, 15(1), 2074. Nature Publishing Group UK London. Read
Lin, M., Zepf, K., Christensen, A. N., Bashir, Z., Svendsen, M. B. S., Tolsgaard, M., & Feragen, A. (2023). DTU-Net: Learning topological similarity for curvilinear structure segmentation. International Conference on Information Processing in Medical Imaging. Springer Nature Switzerland Cham. Read
Mikolaj, K., Lin, M., Bashir, Z., Svendsen, M. B. S., Tolsgaard, M., Nymark, A., & Feragen, A. (2023). Removing confounding information from fetal ultrasound images. arXiv preprint arXiv:2303.13918. Read
Wong, C. K., Ngo, M., Lin, M., Bashir, Z., Heen, A., Svendsen, M. B. S., Tolsgaard, M. G., Christensen, A. N., & Feragen, A. (2024). Deployment of deep learning models in real-world clinical settings: A case study in obstetric ultrasound. arXiv preprint arXiv:2404.00032. Read
These peer-reviewed studies reinforce our commitment to scientific excellence and clinical impact, ensuring that Prenaital’s AI solutions provide unparalleled accuracy and clinical value in prenatal care.