Artificial intelligence to understand fluctuation of fetal brain activity by recognizing facial expressions
Open accessAbstract
OBJECTIVE: To examine whether artificial intelligence can achieve discoveries regarding fetal brain activity. METHODS: In this observational study, the authors collected images of fetal faces using a four-dimensional ultrasound technique obtained from singleton pregnancies of outpatients in routine practice at 27 to 37 weeks of gestation between February 1 and December 31, 2021. The authors developed an artificial intelligence classifier to recognize seven facial expressions of fetuses, then applied it to video files of fetal facial images to generate the probabilities, as confidence scores, of each expression category. Discrete Fourier transform and chaotic analysis were used to investigate the scores. Mann-Whitney test, t test, variance test, and one-way analysis of variance were used for statistical analysis. RESULTS: Facial expression changes were observed in cycles averaging 66 to 73 s. The power spectrum showed that mouthing and neutral expressions were the most prevalent. There was a difference between categories for the spectrum (p = 0.004). Two different states--dense and sparse--of confidence scores were discovered. The correlation dimension was 1.19 ± 0.22 and 1.33 ± 0.27 for dense and sparse, respectively (p = 0.047). CONCLUSION: This method objectively and quantitatively demonstrated fetal brain activity and may provide insight into how the fetus spends its time in utero.
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Miyagi et al. proposed an AI classifier analyzing 4D US volumes to assess fetal facial expressions and classify them into different categories.
“Miyagi et al. proposed an AI classifier analyzing 4D US volumes to assess fetal facial expressions and classify them into different categories [102,103]”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [102]
The Abstract says the authors had previously reported a classifier for seven fetal facial-expression categories. The Materials and Methods section says this study applied it to individual JPG images extracted from four-dimensional ultrasound videos, not directly to 4D ultrasound volumes. The paper applies the classifier here rather than first proposing it. (LLM, conf=0.87)
Experimental — accuracy not yet measuredIn utero stress may result in scowling.
“in utero stress may result in scowling”
Eye blinking, mouthing, smiling, and yawning have been described to indicate fetal brain maturation.
“Facial expressions such as eye blinking, mouthing, smiling, and yawning have been described to indicate fetal brain maturation”
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Cites
Characterization of Strange Attractors
Grassberger, P., & Procaccia, I. (1983) · Physical Review Letters · doi:10.1103/physrevlett.50.346 · Cited by 4,840 · see citing works
Fractal measures and their singularities: The characterization of strange sets
Halsey, T. C., Jensen, M. H., Kadanoff, L. P., Procaccia, I., & Shraiman, B. I. (1986) · Physical Review A · doi:10.1103/physreva.33.1141 · Cited by 3,328 · see citing works
The infinite number of generalized dimensions of fractals and strange attractors
Hentschel, H. G. E., & Procaccia, I. (1983) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(83)90235-x · Cited by 1,812 · see citing works
The dimension of chaotic attractors
Farmer, J. D., Ott, E., & Yorke, J. A. (1983) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(83)90125-2 · Cited by 1,105 · see citing works
The emergence of fetal behaviour. I. Qualitative aspects
de Vries, J. I. P., Visser, G. H. A., & Prechtl, H. F. R. (1982) · Early Human Development · doi:10.1016/0378-3782(82)90033-0 · Cited by 896 · see citing works
Dimensions and entropies of strange attractors from a fluctuating dynamics approach
Grassberger, P., & Procaccia, I. (1984) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(84)90269-0 · Cited by 560 · see citing works
Kim, J., Kim, B., Roy, P. P., & Jeong, D.-M. (2019) · IEEE Access · doi:10.1109/access.2019.2907327 · Cited by 309 · see citing works
Prechtl, H. F. R. (1997) · Early Human Development · doi:10.1016/s0378-3782(97)00088-1 · Cited by 246 · see citing works
New scoring system for fetal neurobehavior assessed by three- and four-dimensional sonography
Kurjak, A., Mišković, B., Stanojević, M., Amiel‐Tison, C., Ahmed, B. I., Azumendi, G., Vasilj, O., Andonotopo, W., Turudic, T., & Salihagič-Kadić, A. (2008) · Journal of Perinatal Medicine · doi:10.1515/jpm.2008.007 · Cited by 186 · see citing works
The Visual Scoring of Sleep in Infants 0 to 2 Months of Age
Grigg-Damberger, M. M. (2016) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.5600 · Cited by 180 · see citing works
Do Facial Expressions Develop before Birth?
Reissland, N., Francis, B. J., Mason, J. M., & Lincoln, K. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024081 · Cited by 144 · see citing works
Dopamine and inhibitory action control: evidence from spontaneous eye blink rates
Colzato, L. S., van den Wildenberg, W. P. M., van Wouwe, N. C., Pannebakker, M. M., & Hommel, B. (2009) · Experimental Brain Research · doi:10.1007/s00221-009-1862-x · Cited by 139 · see citing works
Fractal measures and their singularities: The characterization of strange sets
Halsey, T. C., Jensen, M. H., Kadanoff, L. P., Procaccia, I., & Shraiman, B. I. (1987) · Nuclear Physics B - Proceedings Supplements · doi:10.1016/0920-5632(87)90036-3 · Cited by 127 · see citing works
An Attempt to Standardize Kurjak's Antenatal Neurodevelopmental Test: Osaka Consensus Statement
Predojević, M., Mišković, B., Ahmed, B. I., Vlădăreanu, R., Kadić, A. S., Shaddad, A. N., Al-Noobi, M., Talić, A., Stanojević, M., Lebit, D., et al. (2011) · Donald School Journal of Ultrasound in Obstetrics & Gynecology · doi:10.5005/jp-journals-10009-1209 · Cited by 78 · see citing works
Ultrasound for evaluation of fetal neurobehavioural development: from 2‐D to 4‐D ultrasound
Hata, T., Dai, S., & Marumo, G. (2009) · Infant and Child Development · doi:10.1002/icd.659 · Cited by 68 · see citing works
Self-citationMiyagi, Y., Takehara, K., Nagayasu, Y., & Miyake, T. M. (2019) · Oncology Letters · doi:10.3892/ol.2019.11214 · Cited by 59 · see citing works
Self-citationMiyagi, Y., Habara, T., Hirata, R., & Hayashi, N. (2019) · Reproductive Medicine and Biology · doi:10.1002/rmb2.12284 · Cited by 36 · see citing works
Self-citationRecognition of facial expression of fetuses by artificial intelligence (AI)
Miyagi, Y., Hata, T., Bouno, S., Koyanagi, A., & Miyake, T. (2021) · Journal of Perinatal Medicine · doi:10.1515/jpm-2020-0537 · Cited by 34 · see citing works
Self-citationFetal face as important indicator of fetal brain function
AboEllail, M. A. M., & Hata, T. (2017) · Journal of Perinatal Medicine · doi:10.1515/jpm-2016-0377 · Cited by 33 · see citing works
Self-citationCurrent status of fetal neurodevelopmental assessment: Four‐dimensional ultrasound study
Hata, T. (2016) · Journal of obstetrics and gynaecology research · doi:10.1111/jog.13099 · Cited by 27 · see citing works
Self-citationMiyagi, Y., Tada, K., Yasuhi, I., Maekawa, Y., Okura, N., Kawakami, K., Yamaguchi, K. D., Ogawa, M., Kodama, T. S., Nomiyama, M., et al. (2019) · Journal of obstetrics and gynaecology research · doi:10.1111/jog.14166 · Cited by 20 · see citing works
Self-citationRecognition of Fetal Facial Expressions Using Artificial Intelligence Deep Learning
Hata, T., & Miyagi, Y. (2021) · Donald School Journal of Ultrasound in Obstetrics & Gynecology · doi:10.5005/jp-journals-10009-1710 · Cited by 18 · see citing works
Self-citationKawakami, F., & Yanaihara, T. (2012) · Infant Behavior and Development · doi:10.1016/j.infbeh.2012.04.002 · Cited by 17 · see citing works
HDlive and 4D Ultrasound in the Assessment of Fetal Facial Expressions
(2015) · Donald School Journal of Ultrasound in Obstetrics & Gynecology · doi:10.5005/jp-journals-10009-1388 · Cited by 15 · see citing works
Variations of multifractal structure in the fetal heartbeats.
Miyagi, Y., Miyagi, Y., Sanae, T., & Kudo, T. (2003) · PubMed · doi:10.18926/amo/32821 · Cited by 4 · see citing works
Self-citationFetal Face as Predictor of Fetal Brain
(2018) · Donald School Journal of Ultrasound in Obstetrics & Gynecology · doi:10.5005/jp-journals-10009-1552 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- de Vries JIP, Visser GHA, Prechtl HFR. The emergence of fetal be- haviour. l. Qualitative aspects. Early Hum Dev. 1982;7:301-322.
Cited by
Sorted by citations
Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44 · see citing works
A Deep-Learning-Based Method Can Detect Both Common and Rare Genetic Disorders in Fetal Ultrasound
Tang, J., Han, J., Xue, J., Zhen, L., Yang, X., Pan, M., Hu, L., Li, R., Jiang, Y., Zhang, Y., et al. (2023) · Biomedicines · doi:10.3390/biomedicines11061756 · Cited by 30 · see citing works
Fetal brain activity and the free energy principle
Miyagi, Y., Hata, T., & Miyake, T. (2023) · Journal of Perinatal Medicine · doi:10.1515/jpm-2023-0092 · Cited by 6 · see citing works
Self-citationHarish, S., & Reeja, S. R. (2024) · International Conference on Emerging Systems and Intelligent Computing (ESIC) · doi:10.1109/esic60604.2024.10481665 · Cited by 6 · see citing works
Pavanya, M., Chadaga, K., Jaganathan, V., Vasudeva, A., Rao, B. K., & Bhat, S. K. (2025) · Healthcare Technology Letters · doi:10.1049/htl2.70014 · Cited by 5 · see citing works
Miyagi, Y., Tada, K., Yasuhi, I., Tsumura, K., Maegawa, Y., Tanaka, N., Mizunoe, T., Emoto, I., Maeda, K., & Kawakami, K. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13061826 · Cited by 6 · see citing works
Self-citationAoyama, R., Komatsu, M., Harada, N., Komatsu, R., Sakai, A., Takeda, K., Teraya, N., Asada, K., Kaneko, S., Iwamoto, K., et al. (2024) · Bioengineering · doi:10.3390/bioengineering11121256 · Cited by 4 · see citing works
Integrating KANET and Doppler indices to predict neurodevelopmental delays in high-risk pregnancies
Andonotopo, W., Bachnas, M. A., Dewantiningrum, J., Pramono, B. A., Sulistyowati, S., Stanojević, M., & Kurjak, A. (2025) · Journal of Perinatal Medicine · doi:10.1515/jpm-2025-0051 · Cited by 4 · see citing works
Tonni, G., Grisolia, G., Tonni, S., de Andrade, V. L., Ruano, R., & Sepúlveda, W. (2024) · Journal of Clinical Ultrasound · doi:10.1002/jcu.23870 · Cited by 4 · see citing works
Kinetic Energy and the Free Energy Principle in the Birth of Human Life
Miyagi, Y., Mio, Y., Yumoto, K., Hirata, R., Habara, T., & Hayashi, N. (2024) · Reproductive Medicine · doi:10.3390/reprodmed5020008 · Cited by 2 · see citing works
Self-citationAndonotopo, W., Bachnas, M. A., Stanojevic, M., & Kurjak, A. (2026) · Donald School Journal of Ultrasound in Obstetrics & Gynecology · doi:10.5005/jp-journals-10009-2106 · Cited by 1 · see citing works
Does fetus feel stress or pain on uterine contraction?
Hata, T., AboEllail, M. A. M., Miyake, T. M., & Kanenishi, K. (2022) · Journal of Perinatal Medicine · doi:10.1515/jpm-2022-0514
Self-citationCharacteristics of fetal facial expression changes using artificial intelligence: a pilot study
Miyagi, Y., Hata, T., & Miyake, T. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-30593-2
Self-citationEmerging applications of artificial intelligence for obstetric ultrasound: A scoping review
Gupta, V., Santos, N., Ripa, S. D., & Walker, D. A. (2026) · International Journal of Gynecology & Obstetrics · doi:10.1002/ijgo.70789 · Cited by 2 · see citing works
Retracted: Early Diagnosis of Rare Genetic Disorders Using AI-Powered Genomic Sequencing
Iram, F., Gopukumar, S. T., R, K. K., Ranjitha, B., Mubarak, S., & Jegajothi, B. (2025) · Global Conference on Information Technology and Communication Networks (GITCON) · doi:10.1109/gitcon65266.2025.11376862
Retracted
- Authors
- Miyagi, Y., Hata, T., Bouno, S., Koyanagi, A., & Miyake, T.
- Affiliations
- Saitama International Medical Center (JP)
- Kagawa University (JP)
- Department of Obstetrics and Gynecology Miyake Clinic Okayama City Okayama Prefecture Japan
- Corresponding author
- Miyagi, Yasunari
- Venue
- International Journal of Gynecology & Obstetrics
- Publisher
- Wiley
- Published
- 2022-11-26
- Volume
- 161
- Issue
- 3
- Pages
- 877-885
- ISSN
- 0020-6695 (ISSN-L)
- 0020-7292
- 1879-3479
- OpenAlex
- W4308688000
- Cited by
- 15
- References
- 26
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 5 · 2023: 3 · 2022: 1
- FWCI
- 3.56
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Gynecology & Obstetrics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- fetal brain activity, fetal facial expressions, facial expression recognition, artificial intelligence classifiers, 4D ultrasound, power spectrum, correlation dimension
- MeSH terms
- Artificial Intelligence, Brain, Facial Expression, Female, Fetus, Humans, Pregnancy, Ultrasonography, Prenatal
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