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CORRECTION article

Front. Med., 02 February 2026

Sec. Obstetrics and Gynecology

Volume 13 - 2026 | https://doi.org/10.3389/fmed.2026.1792399

Correction: Application of artificial intelligence in diagnosis and management of fetal growth disorders: a comprehensive review

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    Frontiers Production Office

  • Frontiers Media SA, Lausanne, Switzerland

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Figure 3 was erroneously omitted at publication. The corrected Figure 3 with caption appears below.

Figure 3

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.

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.

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.

Summary

Keywords

artificial intelligence, fetal growth disorders, fetal growth restriction, fetal macrosomia, intrauterine fetal growth restriction, large-for-gestational-age, small-for-gestational-age

Citation

Frontiers Production Office (2026) Correction: Application of artificial intelligence in diagnosis and management of fetal growth disorders: a comprehensive review. Front. Med. 13:1792399. doi: 10.3389/fmed.2026.1792399

Received

20 January 2026

Accepted

20 January 2026

Published

02 February 2026

Approved by

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Volume

13 - 2026

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Copyright

*Correspondence: Frontiers Production Office,

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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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