Meeting Banner
Abstract #0711

APPLAUSE: Automatic Prediction of PLAcental health via U-net Segmentation and statistical Evaluation

Maximilian Pietsch1, Alison Ho2, Alessia Bardanzellu1, Aya Zeydan1, Joseph V Hajnal3, Lucy Chappell2, Mary A Rutherford3, and Jana Hutter1,4
1Centre for Medical Engineering, King's College London, London, United Kingdom, 2Women's Health, King's College London, London, United Kingdom, 3King's College London, London, United Kingdom, 4Centre for the Developing Brain, King's College London, London, United Kingdom

The placenta is key for any successful pregnancy. Deviations from the normal dynamic maturation throughout gestation are closely linked to major pregnancy complications. Automatic segmentation and age prediction based on a 30sec MRI T2* scan is enabled and evaluated in >100 pregnancies. High abnormality scores correlate with low birth weight, premature birth and histopathological findings. Retrospective application on a different cohort imaged at 1.5T illustrates the ability for direct clinical translation. The proposed machine-learning pipeline runs in close to real-time and, deployed in clinical settings, has the potential to become a cornerstone of diagnosis and intervention of placental insufficiency.

This abstract and the presentation materials are available to members only; a login is required.

Join Here