Abdominal Circumference Operator-agnostic UltraSound measurement in Low-Income Countries using Artificial Intelligence
Creators: name: Sappia, María Sofía
name: Murphy, Keelin
name: de Korte, Chris
name: van Dillen, Jeroen
name: van Ginneken, Bram
name: López González, Julio Joaquín
name: Vijn, Frank
name: Geerts, Guido
name: Séguin, Enya
Publisher: name: Zenodo
Year: 2024
Subjects: abdominal circumference
prenatal ultrasound
low-income countries
novice operators
artificial intelligence
MICCAI 2024 challenges

description: Fetal growth restriction (FGR), affecting up to 10% of pregnancies, is a critical factor contributing to perinatal morbidity and mortality (1–3). Strongly linked to stillbirths, FGR can also lead to preterm labor, posing risks to the mother (4,5). This condition often results from an impediment to the fetus’ genetic growth potential due to various maternal, fetal, and placental factors (6). Measurements of the fetal abdominal circumference (AC) as seen on prenatal ultrasound are a key aspect of monitoring fetal growth. When smaller than expected, these measurements can be indicative of FGR, a condition linked to approximately 60% of fetal deaths (4). FGR diagnosis relies on repeated measurements of either the fetal abdominal circumference (AC), the expected fetal weight, or both. These measurements must be taken at least twice, with a minimum interval of two weeks between them for a reliable diagnosis (7). Additionally, an AC measurement that falls below the third percentile is, by itself, sufficient to diagnose FGR (7–9). However, the routine practice of biometric obstetric ultrasounds, crucial for AC measurements, is limited in low-resource settings due to the high cost of sonography equipment and the scarcity of trained sonographers.

The use of low-cost ultrasound devices and standardized blind-sweep protocols has been proposed for novice operators to acquire obstetric data in these settings (10–12). Blind-sweep acquisition protocols are characterized by operators performing scans without viewing the ultrasound images. These protocols yield sequences of 2D ultrasound frames that are captured as the ultrasound probe follows specific trajectories across the gravid abdomen. Unlike traditional clinical sonography, where experienced sonographers search for the standard plane to conduct biometry measurements, blind-sweep data poses a distinct set of challenges. The quality of the image data is limited and may not contain the precise standard planes conventionally used for measurements (13). Addressing these limitations, a growing body of literature focuses on the use of artificial intelligence (AI) to automate prenatal assessment tasks on free-hand ultrasound sequences acquired following standardized protocols, bypassing the need for expert sonographic interpretation. Such tasks include fetal biometry measurements (13,14), gestational age estimation (13,15–17) and pregnancy risk detection (14,15,18–22). These AI solutions have the potential to be embedded into mobile devices, offering a complete, offline, low-cost, and portable solution suitable for resource-limited settings, as demonstrated in (15,21).

Unlike previous challenges that focused on ultrasound imaging data acquired in clinical settings, this is the first challenge to propose the use of blind-sweep data for fetal biometry tasks. The goal is to develop and benchmark AI models for the automated measurement of fetal abdominal circumference on this specific data type, with the aim to broaden the accessibility of prenatal care in areas with limited resources. A similar task was explored in (23), where the authors propose an AI model to perform fetal biometry measurements including AC on automatically selected standard planes. This model specifically worked on data captured by expert sonographers. Unlike a standardized blind-sweep protocol, the sonographers in this study were directed to ensure that each imaging sequence they acquired included the standard planes necessary for accurate measurements.

Participants in this challenge will develop AI models to estimate AC in blind-sweep 2D prenatal abdominal ultrasound sequences, acquired by novice operators in five African peripheral healthcare units and one European hospital. The models must identify the optimal frame for measurement and accurately segment the fetal abdomen within that frame. They must provide the identified frame and the corresponding segmentation mask, which will be used to precisely measure the fetal abdominal circumference. The models will be evaluated against expert estimates derived from blind-sweep data. This challenge represents a first step into FGR detection in low-resource settings. Its main aim is to accurately estimate AC from blind sweep data acquired by novice operators. These estimates could eventually be used to detect FGR, though FGR detection is beyond the scope of the challenge itself. Our end goal is to create effective AI applications for ultrasound imaging that will help improve the care provided to pregnant women and neonates in these regions.

References

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