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Updated: Aug 5, 2026
High Frequency Ultrasound for the Analysis of Fetal and Placental Development In Vivo
Published on: November 8, 2018
Artificial Intelligence Based Ultrasound Screening for Antenatal Detection of Placenta Accreta Spectrum
Alexandra L Hammerquist1, Hendrik A Lombaard1, Sanmay Sarada2
1Department of Obstetrics and Gynecology, Baylor College of Medicine and Texas Children's Hospital, Houston, TX, 77030, USA; Department of Obstetrics and Gynecology, Baylor College of Medicine and Texas Children's Hospital, Division of Maternal-fetal Medicine, Houston, TX.
Background:
Placenta Accreta Spectrum is a leading cause of maternal morbidity and mortality and is increasing in incidence, yet only 30-50% of cases are diagnosed antenatally in some cases. While ultrasonographic findings may inform risk of Placenta Accreta Spectrum, multiple factors may lead to inconclusive or misdiagnosis.
Objective:
We hypothesized that a novel artificial intelligence model could be used as a screening tool to accurately predict Placenta Accreta Spectrum by image classification of 2D placental ultrasounds.
Study Design:
This single-center retrospective study included 756 placental ultrasound DICOM files from which 38,907 grayscale PNG frames were extracted from 113 patients at risk for PAS from 2018 to 2025. Mean gestational age at ultrasound was 30.89 ± 3.67 weeks. Patients were stratified to produce 79/17/17 train/validation/test groups. Images were classified by final pathologic grades. We used an ImageNet pretrained EfficientNetB0 backbone, followed by global average pooling and a regularized fully connected layer with sigmoid activation for binary classification. Frame level probabilities from the convolutional neural network output were averaged to patient level consensus scores. These scores, together with number of prior Cesarean sections and previa status were input as variables into training a logistic regression, random forest, and gradient boosting classifier which were ensembled for final prediction of PAS incorporating patient identifiable risk factors.
Results:
The convolutional neural network ensembled model predicted the presence or absence of Placenta Accreta Spectrum accurately in 88% (95% CI 63.6%-98.5%) of cases with a sensitivity of 100% (95% CI 66.4%-100.0%) and specificity of 75% (95% CI 34.9%-96.8%). The positive predictive value was 81.8% (95% CI 48.2%-97.7%) and the negative predictive value was 100% (95% CI 54.1%-100.0%). There were no false negatives in the testing cohort. The AUC-ROC was 0.972 (95% CI 0.875-1.000). Model variable importance scores concentrated at the placental interface, highlighting biological plausibility.
Conclusions:
This novel artificial intelligence model achieved accurate, sensitive Placenta Accreta Spectrum prediction before delivery. The results of the model support its potential use as a screening tool for earlier diagnosis of Placenta Accreta Spectrum screening, warranting future prospective trials.
