Figure 3 was erroneously omitted at publication. The corrected Figure 3 with caption appears below.
AI integration in prenatal growth assessment. Created with biorender.com. cfDNA, cell-free DNA; FGR, fetal growth restriction; LGA, large-for-gestational-age; SGA, small-for-gestational-age.
Infographic showing the process from input data to clinical impact using AI methods. Input data includes ultrasound images, Doppler indices, maternal history, serum biomarkers, and cf DNA. AI methods used are machine learning, deep learning, and Bayesian networks. These methods predict small for gestational age, fetal growth restriction, large for gestational age, macrosomia, estimate fetal birth weight, and assess risk. Clinical impacts are faster diagnosis, reduced variability, and improved clinical outcomes.
Figure 3 was not cited in the article. The citation has now been inserted in the section Materials and methods, subsection Fetal macrosomia and large-for-gestational-age (LGA) fetus, Paragraph Number 6 and should read:
“Integrating clinical, biochemical, and sonographic data captures complex interactions that drive fetal overgrowth, which traditional methods often miss (Figure 3).”
The original version of this article has been updated.
Approved by: Frontiers Editorial Office, Frontiers Media SA, Switzerland