This technology is a deep learning algorithm that uses ultrasound imaging data to extract cervix geometry and predict preterm birth (PTB) risk.
The current gold standard for prediction of preterm birth (PTB) risk relies on clinical assessment of transvaginal ultrasound (TVUS) images to measure cervical length (CL). However, this method is an incomplete measurement of cervical health and is inconsistent, especially for patients with no prior history of PTB or who are pregnant for the first time. There is currently no automated, objective, and quantitative method to accurately assess PTB risk.
This technology uses machine learning to automatically detect and label cervical physiological features from TVUS images of patients in the 2nd and 3rd trimesters of pregnancy, records PTB markers and other important cervical and lower uterine features, combines additional patient data from electronic medical records with TVUS sonograms, and applies deep learning models to predict PTB risk. The algorithm has been trained and tested on a data set from Columbia patients and successfully recognized cervical features across multiple cervical phenotypes. As such, this technology may be used to improve prediction of PTB.
Patent pending
IR CU22367
Licensing Contact: Dovina Qu