Computational methods for the analysis of early-pregnancy brain ultrasonography: a systematic review

Abstract

Background: Early screening of the brain is becoming routine clinical practice. Currently, this screening is performed by manual measurements and visual analysis, which is time-consuming and prone to errors. Computational methods may support this screening. Hence, the aim of this systematic review is to gain insight into future research directions needed to bring automated early-pregnancy ultrasound analysis of the human brain to clinical practice.

Methods: We searched PubMed (Medline ALL Ovid), EMBASE, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar, from inception until June 2022. This study is registered in PROSPERO at CRD42020189888. Studies about computational methods for the analysis of human brain ultrasonography acquired before the 20th week of pregnancy were included. The key reported attributes were: level of automation, learning-based or not, the usage of clinical routine data depicting normal and abnormal brain development, public sharing of program source code and data, and analysis of the confounding factors.

Findings: Our search identified 2575 studies, of which 55 were included. 76% used an automatic method, 62% a learning-based method, 45% used clinical routine data and in addition, for 13% the data depicted abnormal development. None of the studies shared publicly the program source code and only two studies shared the data. Finally, 35% did not analyse the influence of confounding factors.

Interpretation: Our review showed an interest in automatic, learning-based methods. To bring these methods to clinical practice we recommend that studies: use routine clinical data depicting both normal and abnormal development, make their dataset and program source code publicly available, and be attentive to the influence of confounding factors. Introduction of automated computational methods for early-pregnancy brain ultrasonography will save valuable time during screening, and ultimately lead to better detection, treatment and prevention of neuro-developmental disorders.

Funding: The Erasmus MC Medical Research Advisor Committee (grant number: FB 379283).

Keywords: Computational methods; Embryonic and fetal brain; First trimester; Ultrasonography.

Fig. 1

Study selection.

Fig. 2

Overview of error in the head circumference (HC) per gestational week. The thickness of the bar indicates the number of subjects used for validation: thinnest: <35 subjects, middle: between 35 and 250 subjects, thickest: >250 subjects. A white bar indicates that no average error was reported. The error is shown up to the 20th week, since we were interested in the performance during early pregnancy.

Fig. 3

Overview of detection accuracy of the trans-cerebellar plane (TC), trans-thalamic plane (TT) and trans-ventricular plane (TV) per week gestational age. The thickness of the bar indicates the number of subjects used for validation: thinnest: <35 subjects, middle: between 35 and 250 subjects, thickest: >250 subjects. A white bar with a black edge indicates that no accuracy was reported. The accuracy is shown up to the 20th week, since we were interested in the performance during early pregnancy.

Fig. 4

Overview of the Dice score for segmentation of the fetal head in the trans-ventricular (TV) plane, cerebellum (CER) and choroid plexus (CP) per week gestational age. The thickness of the bar indicates the number of subjects used for validation: thinnest: <35 subjects, middle: between 35 and 250 subjects, thickest: >250 subjects. A white bar with a black edge indicates that no Dice score was reported. The Dice score is shown up to the 20th week, since we were interested in the performance during early pregnancy.