Development of birth weight estimation model for Ethiopian population from sonographic evaluation

Abstract

Background: Fetal birth weight (FBW) estimation involves predicting the weight of a fetus prior to delivery. This prediction serves as a crucial input for ensuring effective, accurate, and appropriate obstetric planning, management, and decision-making. Typically, there are two methods used to estimate FBW: the clinical method (which involves measuring fundal height and performing abdominal palpation) or sonographic evaluation. The accuracy of clinical method estimation relies heavily on the experience of the clinician. Sonographic evaluation involves utilizing various mathematical models to estimate FBW, primarily relying on fetal biometry. However, these models often demonstrate estimation errors that exceed acceptable levels, which can result in inadequate labor and delivery management planning. One source of this estimation error is sociodemographic variations between population groups in different countries. Additionally, inter- and intra-observer variability during fetal biometry measurement also contributes to errors in FBW estimation.

Methods: In this research, a novel mathematical model was proposed through multiple regression analysis to predict FBW with an accepted level of estimation error. To develop the model, population data consisting of fetal biometry, fetal ultrasound images, obstetric variables, and maternal sociodemographic factors (age, marital status, ethnicity, educational status, occupational status, income, etc.) of the mother were collected. Two approaches were used to develop the mathematical model. The first method was based on fetal biometry data measured by a physician and the second used fetal biometry data measured using an image processing algorithm. The image processing algorithm comprises preprocessing, segmentation, feature extraction, and fetal biometry measurement.

Results: The model developed using the two approaches were tested to assess their performance in estimating FBW, and they achieved mean percentage errors of 7.53% and 5.89%, respectively. Based on these results, the second model was chosen as the final model.

Conclusion: The findings indicate that the developed model can estimate FBW with an acceptable level of error for the Ethiopian population. Furthermore, this model outperforms existing models for FBW estimation. The proposed approach has the potential to reduce infant and maternal mortality rates by providing accurate fetal birth weight estimates for informed obstetric planning.

Keywords: FBW estimation; Mathematical model; Multiple linear regression; Sonographic evaluation.

Fig. 1

Block diagram of the model development

Fig. 2

HC and BPD measurement steps, (a) original image, (b) denoised and cropped image, (c) segmented image, (d) convex hull then edge detected image, (e) ellipse fitted image

Fig. 3

AC measurement steps, (a) original image, (b) denoised and cropped image, (c) morphological opened image, (d) contrast adjusted, (e) segmented image, (f) convex hull then edge detected image, (g) ellipse fitted image

Fig. 4

FL measurement steps, (a) original image, (b) denoised and cropped image, (c) segmented image, (d) rectangle box fitted