International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025
Ultrasound placental image texture analysis using artificial intelligence and deep learning models to predict hypertension in pregnancy.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
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Arora Urvashi, Vigneshwar Palani, Sai Munagala Krishna, Yadav Reena, Sengupta Debarka, Kumar Manisha
Аннотация
INTRODUCTION: This study considers the application of ultrasound placental image texture analysis for the prediction of hypertensive disorders of pregnancy (HDP) using deep learning (DL) algorithm.
METHOD: In this prospective observational study, placental ultrasound images were taken serially at 11-14 weeks (T1), 20-24 weeks (T2), and 28-32 weeks (T3). Pregnant women with blood pressure at or above 140/90 mmHg on two occasions 4 h apart were considered to have HDP. The image data of women with HDP were compared with those with a normal outcome using DL techniques such as convolutional neural networks (CNN), transfer learning, and a Vision Transformer (ViT) with a TabNet classifier. The accuracy and the Cohen kappa scores of the different DL techniques were compared.
RESULTS: A total of 600/1008 (59.5%) subjects had a normal outcome, and 143/1008 (14.2%) had HDP; the reminder, 265/1008 (26.3%), had other adverse outcomes. In the basic CNN model, the accuracy was 81.6% for T1, 80% for T2, and 82.8% for T3. Using the Efficient Net B0 transfer learning model, the accuracy was 87.7%, 85.3%, and 90.3% for T1, T2, and T3, respectively. Using a TabNet classifier with a ViT, the accuracy and area under the receiver operating characteristic curve scores were 91.4% and 0.915 for T1, 90.2% and 0.904 for T2, and 90.3% and 0.907 for T3. The sensitivity and specificity for HDP prediction using ViT were 89.1% and 91.7% for T1, 86.6% and 93.7% for T2, and 85.6% and 94.6% for T3.
CONCLUSION: Ultrasound placental image texture analysis using DL could differentiate women with a normal outcome and those with HDP with excellent accuracy and could open new avenues for research in this field.
METHOD: In this prospective observational study, placental ultrasound images were taken serially at 11-14 weeks (T1), 20-24 weeks (T2), and 28-32 weeks (T3). Pregnant women with blood pressure at or above 140/90 mmHg on two occasions 4 h apart were considered to have HDP. The image data of women with HDP were compared with those with a normal outcome using DL techniques such as convolutional neural networks (CNN), transfer learning, and a Vision Transformer (ViT) with a TabNet classifier. The accuracy and the Cohen kappa scores of the different DL techniques were compared.
RESULTS: A total of 600/1008 (59.5%) subjects had a normal outcome, and 143/1008 (14.2%) had HDP; the reminder, 265/1008 (26.3%), had other adverse outcomes. In the basic CNN model, the accuracy was 81.6% for T1, 80% for T2, and 82.8% for T3. Using the Efficient Net B0 transfer learning model, the accuracy was 87.7%, 85.3%, and 90.3% for T1, T2, and T3, respectively. Using a TabNet classifier with a ViT, the accuracy and area under the receiver operating characteristic curve scores were 91.4% and 0.915 for T1, 90.2% and 0.904 for T2, and 90.3% and 0.907 for T3. The sensitivity and specificity for HDP prediction using ViT were 89.1% and 91.7% for T1, 86.6% and 93.7% for T2, and 85.6% and 94.6% for T3.
CONCLUSION: Ultrasound placental image texture analysis using DL could differentiate women with a normal outcome and those with HDP with excellent accuracy and could open new avenues for research in this field.