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

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.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-11-28
DOI
https://doi.org/10.1038/s41598-025-30593-2
Primary Topic
Neonatal and fetal brain pathology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Takahito M. Miyake, Yasunari Miyagi, Toshiyuki Hata
Scientific Reports
Neonatal and fetal brain pathology
article

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

Takahito M. Miyake, Yasunari Miyagi, Toshiyuki Hata
article en

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.

Scientific ReportsVol. 15(1)
Okayama Psychiatric Medical Center (JP), Okayama Prefecture (JP)
Openalex Percentile: Top 52%
Neonatal and fetal brain pathology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Characteristics of fetal facial expression changes using artificial intelligence: a pilot study — Takahito M. Miyake, Yasunari Miyagi, et al. · Scientific Reports (2025) | TGRS Research Map | TGRS