Evidence map›Paper›PMID 41315675›Full record

ArticleScientific reports2025

Characteristics of fetal facial expression changes using artificial intelligence: a pilot study.

Yasunari Miyagi, Toshiyuki Hata, Takahito Miyake

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yasunari MiyagiDepartment of Obstetrics and Gynecology, Miyake Ofuku Clinic, 393-1 Ofuku, Minami ward, Okayama city, Okayama prefecture, 701-0204, Japan. ymiyagi@mac.com.
Toshiyuki HataDepartment of Obstetrics and Gynecology, Miyake Clinic, 369-8 Ofuku, Minami ward, Okayama city, Okayama prefecture, 701-0204, Japan.
Takahito MiyakeDepartment of Obstetrics and Gynecology, Miyake Ofuku Clinic, 393-1 Ofuku, Minami ward, Okayama city, Okayama prefecture, 701-0204, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We aimed to investigate the frequency, changes, and chaotic correlation dimensions of fetal facial expression videos using artificial intelligence (AI) and speculate the state of the fetal brain activity. We applied our original AI for classifying fetal facial expressions to 57,208 frames, total of 95.27 min, from 47singleton pregnancies at 28 to 37 weeks of gestation, obtained at Miyake Clinic between December 2023 and February 2024 at 0.1-second intervals. Time, transitions and correlation dimensions of the facial expressions were investigated. There was a significant difference between expressions. Neutral and mouthing showed significantly longer durations; 71.0, 9.4-174.8 (Mean, 5-95%ile) and 53.3, 0.7-127.3 s for neutral and mouthing, respectively. The longest transitions were neutral to mouthing at 2,237.5 s. The median correlation dimensions for before, during, and after neutral and mouthing were 1.14, 1.22, and 1.23, and 1.07, 1.15, and 1.24, respectively. Analyzing fetal facial expression videos using AI may raise the possibility of being able to indirectly quantify brain activity. The ability to infer fetal brain activity via fetal facial expressions both qualitatively and quantitatively might be considered to have significant biological implications.

Indexed as

Artificial IntelligenceFacial ExpressionFetusBrainFemaleGestational AgeHumansPilot ProjectsPregnancyVideo Recording4D ultrasoundArtificial intelligenceChaotic dimensionFetal brain functionFetal facial expressionFree energy principle

Identifiers

PMID41315675
PMCPMC12753686

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.