American journal of obstetrics & gynecology MFM · 2026
Artificial intelligence-based ultrasound screening for antenatal detection of placenta accreta spectrum.
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Hammerquist Alexandra L, Lombaard Hendrik A, Sarada Sanmay, Martinez Yamely H Mendez, Reed Christina C, Barrozo Enrico R, Munoz Jessian L, Lee Wesley, Shamshirsaz Amir A, Belfort Michael A, Jochum Michael D
Аннотация
BACKGROUND: Placenta accreta spectrum is a leading cause of maternal morbidity and mortality and is increasing in incidence; however, only 30% to 50% of cases are diagnosed antenatally. Although ultrasonographic findings may inform the risk of placenta accreta spectrum, multiple factors may lead to inconclusive results or misdiagnosis.
OBJECTIVE: We hypothesized that a novel artificial intelligence model could be used as a screening tool to accurately predict placenta accreta spectrum through image classification of 2-dimensional placental ultrasounds.
STUDY DESIGN: This single-center retrospective study included 756 placental ultrasound Digital Imaging and Communications in Medicine (DICOM) files, from which 38,907 grayscale portable network graphics frames were extracted from 113 patients at risk for placenta accreta spectrum from 2018 to 2025. The mean gestational age at ultrasound was 30.89±3.67 weeks. Patients were stratified to into training (n=79), validation (n=17), and test (n=17) 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 the number of previous cesarean deliveries and previa status, were input as variables into training a logistic regression, random forest, and gradient boosting classifier, which were ensembled for the final prediction of placenta accreta spectrum 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% confidence interval, 63.6%-98.5%) of cases, with a sensitivity of 100% (95% confidence interval, 66.4%-100.0%) and a specificity of 75% (95% confidence interval, 34.9%-96.8%). The positive predictive value was 81.8% (95% confidence interval, 48.2%-97.7%), and the negative predictive value was 100% (95% confidence interval, 54.1%-100.0%). There were no false negatives in the testing cohort. The area under the receiver operating characteristic curve was 0.972 (95% confidence interval, 0.875-1.000). Model variable importance scores were concentrated at the placental interface, highlighting biological plausibility.
CONCLUSION: This novel artificial intelligence model achieved accurate and sensitive placenta accreta spectrum prediction before delivery. The results of the model support its potential use as a screening tool for the earlier diagnosis of placenta accreta spectrum, warranting future prospective trials. El resumen está disponible en Español al final del artículo.
OBJECTIVE: We hypothesized that a novel artificial intelligence model could be used as a screening tool to accurately predict placenta accreta spectrum through image classification of 2-dimensional placental ultrasounds.
STUDY DESIGN: This single-center retrospective study included 756 placental ultrasound Digital Imaging and Communications in Medicine (DICOM) files, from which 38,907 grayscale portable network graphics frames were extracted from 113 patients at risk for placenta accreta spectrum from 2018 to 2025. The mean gestational age at ultrasound was 30.89±3.67 weeks. Patients were stratified to into training (n=79), validation (n=17), and test (n=17) 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 the number of previous cesarean deliveries and previa status, were input as variables into training a logistic regression, random forest, and gradient boosting classifier, which were ensembled for the final prediction of placenta accreta spectrum 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% confidence interval, 63.6%-98.5%) of cases, with a sensitivity of 100% (95% confidence interval, 66.4%-100.0%) and a specificity of 75% (95% confidence interval, 34.9%-96.8%). The positive predictive value was 81.8% (95% confidence interval, 48.2%-97.7%), and the negative predictive value was 100% (95% confidence interval, 54.1%-100.0%). There were no false negatives in the testing cohort. The area under the receiver operating characteristic curve was 0.972 (95% confidence interval, 0.875-1.000). Model variable importance scores were concentrated at the placental interface, highlighting biological plausibility.
CONCLUSION: This novel artificial intelligence model achieved accurate and sensitive placenta accreta spectrum prediction before delivery. The results of the model support its potential use as a screening tool for the earlier diagnosis of placenta accreta spectrum, warranting future prospective trials. El resumen está disponible en Español al final del artículo.