@Article{info:doi/10.2196/60887,
author="Bastiaansen, Wietske A P
and Klein, Stefan
and Hojeij, Batoul
and Rubini, Eleonora
and Koning, Anton H J
and Niessen, Wiro
and Steegers-Theunissen, R{\'e}gine P M
and Rousian, Melek",
title="Automatic Human Embryo Volume Measurement in First Trimester Ultrasound From the Rotterdam Periconception Cohort: Quantitative and Qualitative Evaluation of Artificial Intelligence",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="31",
volume="27",
pages="e60887",
keywords="first trimester, artificial intelligence, embryo, ultrasound, biometry; US; Rotterdam; The Netherlands; Cohort; quantitative; qualitative; evaluation; noninvasive; pregnancy; embryonic growth; algorithm; embryonic volume; monitoring; development",
abstract="Background: Noninvasive volumetric measurements during the first trimester of pregnancy provide unique insight into human embryonic growth and development. However, current methods, such as semiautomatic (eg, virtual reality [VR]) or manual segmentation (eg, VOCAL) are not used in routine care due to their time-consuming nature, requirement for specialized training, and introduction of inter- and intrarater variability. Objective: This study aimed to address the challenges of manual and semiautomatic measurements, our objective is to develop 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 used 3D ultrasound datasets from the Rotterdam Periconception Cohort, collected between 7 and 11 weeks of gestational age. We measured the EV in gestational weeks 7, 9 and 11, and the HV in weeks 9 and 11. 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 data acquired in the future was evaluated by testing on 116 (EV) and 58 (HV) datasets from 2020. The performance of the model was assessed using the intraclass correlation coefficient (ICC) between the volume obtained using AI and using VR. In addition, 2 experts qualitatively rated both VR and AI segmentations for the EV and HV. Results: 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 dataset, while experienced raters needed 5-10 minutes using a VR 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. During qualitative rating for the EV, a comparable proportion (AI: 42{\%}, VR: 38{\%}) were rated as excellent; however, we found that major errors were more common with the AI algorithm, as it more frequently missed limbs. For the HV, the AI segmentations were rated as excellent in 79{\%} of cases, compared with only 17{\%} for VR. Conclusions: We developed 2 fully automatic AI algorithms to accurately measure the EV and HV in the first trimester on 3D ultrasound data. In depth qualitative analysis revealed that the quality of the measurement for AI and VR were similar. 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. ",
issn="1438-8871",
doi="10.2196/60887",
url="https://www.jmir.org/2025/1/e60887",
url="https://doi.org/10.2196/60887"
}