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

Yasunari Miyagi1,2, Toshiyuki Hata3, Takahito Miyake4,3

  • 1Department of Obstetrics and Gynecology, Miyake Ofuku Clinic, 393-1 Ofuku, Minami ward, Okayama city, Okayama prefecture, 701-0204, Japan. ymiyagi@mac.com.

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Summary

Artificial intelligence (AI) analysis of fetal facial expressions reveals distinct patterns and durations, suggesting potential for indirectly assessing fetal brain activity. This research offers new insights into prenatal development and neurological assessment.

Keywords:
4D ultrasoundArtificial intelligenceChaotic dimensionFetal brain functionFetal facial expressionFree energy principle

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Area of Science:

  • Prenatal Medicine
  • Neuroscience
  • Artificial Intelligence

Background:

  • Fetal facial expressions offer potential insights into neurological development.
  • Quantifying these expressions and their dynamics is challenging.

Purpose of the Study:

  • To investigate the frequency, changes, and chaotic correlation dimensions of fetal facial expressions using AI.
  • To explore the potential for inferring fetal brain activity from these expressions.

Main Methods:

  • Applied a novel AI algorithm to classify fetal facial expressions from 57,208 video frames (95.27 minutes).
  • Analyzed data from 47 singleton pregnancies between 28 and 37 weeks of gestation.
  • Investigated expression durations, transitions, and correlation dimensions at 0.1-second intervals.

Main Results:

  • Significant differences in expression durations were observed, with neutral and mouthing expressions lasting significantly longer.
  • The longest transition time was recorded between neutral and mouthing expressions (2,237.5 seconds).
  • Median correlation dimensions varied for neutral and mouthing expressions before, during, and after their occurrence.

Conclusions:

  • AI analysis of fetal facial expressions may provide a method to indirectly quantify fetal brain activity.
  • This approach holds potential for both qualitative and quantitative inference of fetal brain activity, with significant biological implications.