Artificial intelligence to understand fluctuation of fetal brain activity by recognizing facial expressions
Year: 2022
Type: article
Study design: Observational Study
Abstract: 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.
DOI: 10.1002/ijgo.14569
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,950
OpenAlex ID: W4308688000