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Updated: Sep 19, 2025
High Frequency Ultrasound for the Analysis of Fetal and Placental Development In Vivo
Published on: November 8, 2018
ACOUSLIC-AI challenge report: Fetal abdominal circumference measurement on blind-sweep ultrasound data from
M Sofia Sappia1, Chris L de Korte2, Bram van Ginneken3
1Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, Gelderland, The Netherlands; Medical Ultrasound Imaging Center, Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, Gelderland, The Netherlands.
Insights
AI models can now accurately measure fetal abdominal circumference (AC) from low-cost ultrasound scans, improving fetal growth monitoring in low-resource settings. This technology aims to reduce perinatal mortality and morbidity by making essential diagnostics more accessible.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Healthcare
- Maternal-Fetal Medicine
Background:
- Fetal growth restriction impacts up to 10% of pregnancies, leading to significant perinatal mortality and morbidity.
- Ultrasound measurement of fetal abdominal circumference (AC) is crucial for monitoring fetal growth.
- Biometric obstetric ultrasounds are limited in low-resource settings due to equipment cost and lack of trained personnel.
Purpose of the Study:
- To investigate the feasibility of automatically estimating fetal AC from ultrasound scans acquired by novice operators using low-cost devices.
- To address the limitations of current fetal growth monitoring in low-resource settings.
- To develop and benchmark AI models for operator-agnostic fetal AC measurement.
Main Methods:
- The ACOUSLIC-AI challenge was organized, collecting training data from Sierra Leone and validation/test sets from Tanzania and a European hospital.
- Sixteen international teams participated, developing AI models to estimate fetal AC from blind-sweep ultrasound scans.
- AI models were evaluated on fetal abdomen frame selection, segmentation, and AC measurement, with performance compared to clinical standards.
Main Results:
- The top-performing AI models demonstrated limits of agreement (LoA) for fetal AC measurements comparable to interobserver LoA in the literature.
- The developed algorithms show promise for accurate fetal AC estimation even with novice operators and low-cost equipment.
- Publicly accessible AI models provide a benchmark for future advancements in automated fetal biometry.
Conclusions:
- AI-driven automated fetal AC measurement is feasible and accurate, even with low-cost ultrasound devices and novice operators.
- These algorithms can significantly improve fetal growth monitoring accessibility in low-resource settings.
- The ACOUSLIC-AI challenge results establish a new benchmark for fetal abdomen frame selection and segmentation, reducing measurement variability.
Abstract:
Fetal growth restriction, affecting up to 10% of pregnancies, is a critical factor contributing to perinatal mortality and morbidity. Ultrasound measurements of the fetal abdominal circumference (AC) are a key aspect of monitoring fetal growth. However, the routine practice of biometric obstetric ultrasounds is limited in low-resource settings due to the high cost of sonography equipment and the scarcity of trained sonographers. To address this issue, we organized the ACOUSLIC-AI (Abdominal Circumference Operator-agnostic UltraSound measurement in Low-Income Countries) challenge to investigate the feasibility of automatically estimating fetal AC from blind-sweep ultrasound scans acquired by novice operators using low-cost devices. Training data, collected from three Public Health Units (PHUs) in Sierra Leone are made publicly available. Private validation and test sets, containing data from two PHUs in Tanzania and a European hospital, are provided through the Grand-Challenge platform. All sets were annotated by experienced readers. Sixteen international teams participated in this challenge, with six teams submitting to the Final Test Phase. In this article, we present the results of the three top-performing AI models from the ACOUSLIC-AI challenge, which are publicly accessible. We evaluate their performance in fetal abdomen frame selection, segmentation, abdominal circumference measurement, and compare their performance against clinical standards for fetal AC measurement. Clinical comparisons demonstrated that the limits of agreement (LoA) for A2 in fetal AC measurements are comparable to the interobserver LoA reported in the literature. The algorithms developed as part of the ACOUSLIC-AI challenge provide a benchmark for future algorithms on the selection and segmentation of fetal abdomen frames to further minimize fetal abdominal circumference measurement variability.
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