1. Perceiving placental ultrasound image texture evolution during pregnancy with normal and adverse outcome through machine learning prism.
- Author
-
Arora U, Sengupta D, Kumar M, Tirupathi K, Sai MK, Hareesh A, Sai Chaithanya ES, Nikhila V, Bhavana N, Vigneshwar P, Rani A, and Yadav R
- Subjects
- Pregnancy, Humans, Female, Ultrasonography, Sensitivity and Specificity, Pregnancy Trimester, Third, Placenta diagnostic imaging, Machine Learning
- Abstract
Introduction: The objective was to perform placental ultrasound image texture (UPIA) in first (T1), second(T2) and third(T3) trimesters of pregnancy using machine learning( ML)., Methods: In this prospective observational study the 2D placental ultrasound (US) images from 11-14 weeks, 20-24 weeks, and 28-32 weeks were taken. The image data was divided into training, validating, and testing subsets in the ratio of 80%, 10%, and 10%. Three different ML techniques, deep learning, transfer learning, and vision transformer were used for UPIA., Results: Out of 1008 cases included in the study, 59.5% (600/1008) had a normal outcome. The image texture classification was compared between T1&T2, T2 &T3 and T1&T3 pairs. Using Inception v3 model, to classify T1& T2 images, gave the accuracy, Cohen Kappa score of 83.3%, 0.662 respectively. The image classification between T1&T3 achieved best results using EfficientNetB0 model, having the accuracy, Cohen Kappa score, sensitivity and specificity of 87.5%, 0.749, 83.4%, and 88.9% respectively. Comparison of placental image texture among cases with materno-fetal adverse outcome and controls was done using Efficient Net B0. The F1 score, was found to be 0.824 , 0.820, and 0.892 in T1, T2 and T3 respectively. The sensitivity and specificity of the model was 77.4% at 80.2% at T1 but increased to 81.0% and 93.9% at T2 &T3 respectively., Discussion: The study presents a novel technique to classify placental ultrasound image texture using ML models and could differentiate first and third-trimester normal placenta and normal and adverse pregnancy outcome images with good accuracy., Competing Interests: Declaration of competing interest There is no conflict of interest among any of the authors to disclose., (Copyright © 2023 Elsevier Ltd. All rights reserved.)
- Published
- 2023
- Full Text
- View/download PDF