Recognition of Fetal Facial Expressions Using Artificial Intelligence Deep Learning

Hata, Toshiyuki; Miyake, Takahito; Koyanagi, Aya; Bouno, Saori; Miyagi, Yasunari

doi:10.5005/jp-journals-10009-1710

Cited by 10 publications

(6 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was performed in MP4 format at a rate of 10.008 frames per second using a GE Voluson E10 BT20 (GE Healthcare). Informed consent was obtained from all participants at Miyake Clinic between February 1 and December 31, 2021, with completely deidentified data enrolled 14,15 . We transferred the videos to an offline AI system at Medical Data Labo, Japan.…”

Section: Methodsmentioning

confidence: 99%

“…We transferred the videos to an offline AI system at Medical Data Labo, Japan. This retrospective, noninterventional study was approved by the institutional review board of Miyake Clinic (institutional review board number 2019–10) 14,15 . We converted each video frame into separated JPG‐format images, from which the region of interest was cropped into a square and saved at 100 × 100 pixels.…”

Section: Methodsmentioning

confidence: 99%

“…Informed consent was obtained from all participants at Miyake Clinic between February 1 and December 31, 2021, with completely deidentified data enrolled. 14,15 We transferred the videos to an offline AI system at Medical Data Labo, Japan. This retrospective, noninterventional study was approved by the institutional review board of Miyake Clinic (institutional review board number 2019-10).…”

Section: Materials S and Me Thodsmentioning

confidence: 99%

“…This retrospective, noninterventional study was approved by the institutional review board of Miyake Clinic (institutional review board number 2019-10). 14,15 We converted each video frame into separated JPG-format images, from which the region of interest was cropped into a square and saved at 100 × 100 pixels. The AI classifier then classified each image and output the seven confidence scores belonging to each expression category.…”

Section: Materials S and Me Thodsmentioning

confidence: 99%

“…Artificial intelligence (AI) has recently advanced and become prominent in obstetrics and gynecology, [14][15][16][17][18] and we have reported on an AI classifier that can classify seven fetal facial expression categories and demonstrate the probabilities of each category as confidence scores. 14,15 The accuracy values of the classifier were 0.996, 0.996, 1.000, 1.000, 1.000, 1.000, 1.000, and 0.996 for eye blinking, neutral, mouthing, scowling, smiling, tongue expulsion, yawning, and all categories, respectively.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Artificial intelligence to understand fluctuation of fetal brain activity by recognizing facial expressions

Miyagi

Hata

Bouno

et al. 2022

Intl J Gynecology & Obste

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Materials S and Me Thodsmentioning

confidence: 99%

Section: Materials S and Me Thodsmentioning

confidence: 99%