부산대학교 도서관

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. Synopsis: Fetal brain activity was observed quantitatively and objectively by artificial intelligence recognizing facial expressions through discrete Fourier transform and chaos analysis. [ABSTRACT FROM AUTHOR]