Placenta · 2026
Automating first-trimester placenta volume measurements in three-dimensional ultrasonography using virtual reality and artificial intelligence: The Rotterdam Periconception Cohort.
СтатьяНазвание и аннотация: Перевод в очереди
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Bastiaansen W A P, de Vos E S, Koning A H, Berns L, Klein S, Mulders A G M G J, Steegers-Theunissen R P M, Rousian M
Аннотация
INTRODUCTION: Volumetric measurements of the placenta in the first trimester provide insights into placental development and early markers for placenta-related complications. Current manual placental volume (PV) measurement using Virtual Organ Computer-Aided Analysis (VOCAL) on three-dimensional (3D) ultrasound is time-consuming and limited to two-dimensional (2D) planes. We aimed to validate a Virtual Reality (VR)-based 3D PV measurement and develop an automated Artificial Intelligence (AI) method for PV measurement.
METHODS: 3D ultrasound images from 83 singleton pregnancies (115 images) at 7, 9, and 11 weeks gestation from the Rotterdam Periconception Cohort were analyzed. A validation set of 414 paired images was added to assess AI-PV reproducibility using two sequential 3D ultrasounds. VOCAL-PV involved manual tracing at 15° intervals in 2D planes. VR-PV used an in-house VR system for manual voxel selection in 3D. AI-PV used nnU-Net trained on VR-PV. Absolute error, Dice scores, and measurement time were compared.
RESULTS: VR-PV showed average absolute errors of 2.0 cm3 (week 7), 4.4 cm3 (week 9), and 7.6 cm3 (week 11). AI-PV had similar errors respectively: 2.2 cm3, 4.5 cm3, and 9.2 cm3. Dice overlap between AI-PV and VR-PV was 79-82%. AI-PV reproducibility errors were respectively 0.7 cm3, 1.2 cm3, and 1.2 cm3. AI-PV measurement time was 1 min versus 11 min for VOCAL and VR.
DISCUSSION: VR-PV and AI-PV enable accurate first-trimester PV measurement. AI-PV eliminates manual input, reduces time, and supports scalable PV assessment for research and early clinical screening, with potential to improve early detection and management of placenta-related complications.
METHODS: 3D ultrasound images from 83 singleton pregnancies (115 images) at 7, 9, and 11 weeks gestation from the Rotterdam Periconception Cohort were analyzed. A validation set of 414 paired images was added to assess AI-PV reproducibility using two sequential 3D ultrasounds. VOCAL-PV involved manual tracing at 15° intervals in 2D planes. VR-PV used an in-house VR system for manual voxel selection in 3D. AI-PV used nnU-Net trained on VR-PV. Absolute error, Dice scores, and measurement time were compared.
RESULTS: VR-PV showed average absolute errors of 2.0 cm3 (week 7), 4.4 cm3 (week 9), and 7.6 cm3 (week 11). AI-PV had similar errors respectively: 2.2 cm3, 4.5 cm3, and 9.2 cm3. Dice overlap between AI-PV and VR-PV was 79-82%. AI-PV reproducibility errors were respectively 0.7 cm3, 1.2 cm3, and 1.2 cm3. AI-PV measurement time was 1 min versus 11 min for VOCAL and VR.
DISCUSSION: VR-PV and AI-PV enable accurate first-trimester PV measurement. AI-PV eliminates manual input, reduces time, and supports scalable PV assessment for research and early clinical screening, with potential to improve early detection and management of placenta-related complications.