JMIR Publications

ABSTRACT

Background:

Non-invasive volumetric measurements during the first trimester of pregnancy provide unique insight into human embryonic growth and development. However, current methods, are not used in routine care due to their time-consuming nature, the requirement for specialized training, and the introduction of rater variability.

Objective:

To developed and evaluate an automatic Artificial Intelligence (AI) algorithm to segment the region of interest and measure embryonic volume (EV) and head volume (HV) during the first trimester of pregnancy.

Methods:

We utilized three-dimensional (3D) ultrasound datasets from the Rotterdam Periconception Cohort, collected between 7 and 11 weeks of gestational age. To develop the AI algorithms for measuring EV and HV, we used nnU-net, a state-of-the-art segmentation algorithm that is publicly available. We tested the algorithms on 164 (EV) and 92 (HV) datasets, both acquired before 2020. The AI algorithm's generalization to future data was evaluated by testing on 116 (EV) and 58 (HV) datasets acquired in 2020.

Results:

The model's performance was assessed using the intraclass correlation coefficient (ICC) between the volume obtained using AI and VR. We found that segmentation of both the EV and HV using AI took around a minute, additionally, rating took another minute, hence in total, volume measurement took 2 minutes per ultrasound datasets, while experienced raters needed 5-10 minutes using an interactive Virtual Reality tool. For both the EV and HV, we found an ICC of 0.998 on the test set acquired before 2020 and an ICC of 0.996 (EV) and 0.997 (HV) for data acquired in 2020.

Conclusions:

Since automatic volumetric assessment now only takes a couple of minutes, the use of these measurements in pregnancy for monitoring growth and development during this crucial period, becomes feasible, which may lead to better screening, diagnostics, and treatment of developmental disorders in pregnancy.