REVIEW ARTICLE

Recognition of Fetal Facial Expressions Using Artificial
Intelligence Deep Learning
Yasunari Miyagi1, Toshiyuki Hata2, Saori Bouno3, Aya Koyanagi4, Takahito Miyake5

A b s t r ac t​
Fetal facial expressions are useful parameters for assessing brain function and development in the latter half of pregnancy. Previous investigations
have studied subjective assessment of fetal facial expressions using four-dimensional ultrasound. Artificial intelligence (AI) can enable theobjective assessment of fetal facial expressions. Artificial intelligence recognition of fetal facial expressions may open the door to the newscientific field, such as “AI science of fetal brain”, and fetal neurobehavioral science using AI is at the dawn of a new era. Our knowledge of fetal
neurobehavior and neurodevelopment will be advanced through AI recognition of fetal facial expressions. Artificial intelligence may be an
important modality in current and future research on fetal facial expressions and may assist in the evaluation of fetal brain function.
Keywords: Artificial intelligence, Deep learning, Facial recognition, Fetus, Machine learning, Ultrasonography.
Donald School Journal of Ultrasound in Obstetrics and Gynecology (2021): 10.5005/jp-journals-10009-1710

I n t r o d u c t i o n​
Fetal behaviors such as fetal movements and facial expressions
that have been observed by four- (4D) or three-dimensional (3D)
ultrasound have been deemed to be related to the development offetal central nervous system development.1–11 A scoring system,12
which was originally reported by Kurjak et al. and later modified by
Stanojevic et al.,13 can evaluate fetal neurobehavioral development
by evaluating fetal movements and facial expressions. Fetal facial
movements and expressions such as blinking, a face without any
expression, mouthing, scowling, smiling, sucking, tongue expulsion,and yawning can be evaluated by 4D ultrasound from the beginningof the 2nd trimester of pregnancy.2,14 Eye blinking (blinking) is areflex response possibly related to brain function maturation anddevelopment that occurs with advancing gestation.14–18 Mouthingis the most frequent expression and is recognized as fetal brain
maturation if it occurs together with non-rapid eye movement
after 35 weeks of gestation.19 The frequency of scowling that might
indicate suffering of the fetus in utero pain or stress20 increases
with advancing gestation.21 Smiling might indicate a state of brain
development performing complex facial movements. 22,23 The
correlation of an expressionless face and tongue expulsion withbrain function is unclear.14 Yawning may be utilized as an index offetal development.24,25 Therefore, it is important to investigate fetal
facial expressions. There have been, however, no standard objective
methods to evaluate fetal facial expressions.
Recently, artificial intelligence (AI) has advanced into the field of
medicine. In different fields of obstetrics and gynecology, researchworks relevant to AI have been published.26–35 A well-trained AI
classifier that can evaluate and classify fetal facial expressions
would help investigate the development of the fetal central
nervous system. The AI recognition of adult facial expressions
has been investigated. Kim et al. reported the accuracy of the AI
facial expression recognition was 0.965.36 Adult facial expressions
can state human mental state and behavior and their analysis
is available for marketing, healthcare, safety, environment, andsocial media.37
In this review article, we introduce the updated status of AI
recognition of fetal facial expressions as a significant parameter1

Department of Gynecology, Miyake Ofuku Clinic, Okayama, Japan;
Medical Data Labo, Okayama, Japan; Department of Gynecologic
Oncology, Saitama Medical University International Medical Center,
Hidaka, Japan2
Department of Obstetrics and Gynecology, Miyake Clinic, Okayama,
Japan; Department of Perinatology and Gynecology, Kagawa
University Graduate School of Medicine, Kagawa, Japan
3,4
Department of Obstetrics and Gynecology, Miyake Clinic, Okayama,Japan
5
Department of Gynecology, Miyake Ofuku Clinic, Okayama, Japan;
Department of Obstetrics and Gynecology, Miyake Clinic, Okayama,
Japan; Department of Perinatology and Gynecology, Kagawa
University Graduate School of Medicine, Kagawa, Japan
Corresponding Author: Yasunari Miyagi, Department of Gynecology,
Miyake Ofuku Clinic, Okayama, Japan; Medical Data Labo, Okayama,
Japan; Department of Gynecologic Oncology, Saitama Medical
University International Medical Center, Hidaka, Japan, Phone: +8186-281-2020, e-mail: ymiyagi@mac.com
How to cite this article: Miyagi Y, Hata T, Bouno S, et al. Recognitionof Fetal Facial Expressions Using Artificial Intelligence Deep Learning.
Donald School J Ultrasound Obstet Gynecol 2021;15(3):223–228.
Source of support: Nil
Conflict of interest: Nonefor fetal brain function and suggest recommendations for future
research on fetal brain development and function.

R e co g n i t i o n o f F e ta l F ac ia l E x p r e s s i o n s
U s i n g AI​
All data per fetus are divided into test/training/validation
datasets at random in a ratio that is not fixed but commonly set to
0.20/0.64/0.16. In this way, training datasets, validation datasets,
and non-overlapping test datasets are created.
The AI classifier is then designed. The AI classifier composed of
convolutional neural network (CNN)38–43 for classifying categories
is often used for image recognition. The CNN usually comprises© Jaypee Brothers Medical Publishers. 2021 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and non-commercial reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons
Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Recognition of Fetal Facial Expressions by AI
layers with a combination of convolutional layers such as poolinglayers,44–46 linear layers,47,48 flattened layers,49 batch normalization
layer,50 rectified linear unit layers,51,52 and softmax layer53,54 that
presents the probabilities of each category named confidence
scores. Then, the category with the highest confidence score isdetermined as the AI classification category of each image. The AI
classifier is trained using the training dataset with simultaneous
validation by using the validation dataset. Before the AI training, thetraining and validation datasets are augmented by methods suchas rotating the images. Data augmentation is often used, because
image processing such as rotation, can result in different vector
data in the same category.31
The feasibility of the AI classifier is evaluated using the test
dataset. Then, statistical values of the test dataset are thenobtained, such as sensitivity, specificity, accuracy, receiver operating
characteristic (ROC) curve, etc.

P r e v i o u s S t u d i e s o n AI R e co g n i t i o n o f
F e ta l F ac ia l E x p r e s s i o n s​
The development of the AI classifier with the original neuralnetwork architecture that recognizes and classifies images of fetal
faces captured by sonography was reported by Miyagi et al. 55
This pilot study was the first report on the recognition of fetal
facial expressions by AI, to the best of our knowledge. The CNN
architecture consisted of 13 layers; 2 convolution layers, 3 rectified
linear unit layers, 2 pooling layers, 1 flatten layer, 3 linear layers, 1batch normalization layer, and 1 softmax layer. The classifier could
classify only five categories: blinking, face without any expression(neutral face), mouthing, scowling, and yawning, due to sample
limitations for each category. The number of fetus/images were
93/922 and the number of test/validation/training datasets forcreating the AI was 222/1,648/7,168. The accuracy for the test
dataset was 0.985. The values of accuracy/sensitivity/specificity
were 0.996/0.993/1.000, 0.992/0.986/1.000, 0.985/1.000/0.979,
0.996/0.888/1.000, and 1.000/1.000/1.000 for blinking, mouthing,neutral face, scowling, and yawning, respectively. Though theconfidence score of the blinking category in the rated categories
was 0.51 ± 0.35 (mean ± SD), the maximum values of the averageof the confidence scores of other categories were over 0.85.

F u r t h e r A c h i e v e m e n ts i n AI R e co g n i t i o n
o f F e ta l F ac ia l E x p r e s s i o n s​
We introduce the improved AI classifier composed of thesame neural network architecture by collecting more data forrecognizing seven categories in this review. The number offetus/images were 237/1,457 and the number of test/validation/
training dataset for creating the AI was 251/1,536/11,248. The
accuracy, the confidence scores, and the ROC curve of the AI
fetal facial expression analysis were 0.996 as shown Figures 1
to 3, respectively. The accuracy/sensitivity/specificity values
were 0.996/0.964/1.000, 1.000/1.000/1.000, 0.996/1.000/0.994,
1.000/1.000/1.000, 1.000/1.000/1.000, 1.000/1.000/1.000, and
1.000/1.000/1.000 for blinking, mouthing, neutral face, scowling,smiling, tongue expulsion, and yawning, respectively (Table 1).
Other statistical values such as negative predictive value, positive
predictive value, informedness, the area under the ROC curve, F1score, markedness, and Matthews correlation coefficient were over
0.96 in all categories. Sample images classified by AI are shown in
Figure 4.
224

Fig. 1: The confusion matrix plot for the test dataset. The correct
predictions were located in a diagonal matrix position. The accuracy
for classifying fetal facial expression for seven categories was 0.996

L i m i tat i o n s​
The following limitations of AI fetal facial expression recognitionneed to be considered. First, the AI cannot properly classify
unknown images. Though seven facial categories were used,
there would be other fetal facial expressions that are significant ininvestigating the development of the fetal central nervous system.
The perfect classification of fetal facial expressions has not yet been
established, possibly due to the long time needed to observe thefetal face and lack of consensus image classification by examiners.
Such undefined and undiscovered images and categories would
be needed to train AI for clinical practice and research in the
future. Second, the feasibility of fetal facial expression recognition
by AI depends on the supervised data by experienced examiners.
Moreover, the anthropometric differences affected in fetal facial
expression could strongly affect AI creation, this AI would not
be feasibly used for different anthropometric fetuses directly.
We believe, however, that similar algorithms would be available
for other anthropometric fetuses. Third and last, although the AI
showed quite good accuracy, there are still defective datasets suchas for sucking that was rarely seen during the examination. The
incidence of sucking was approximately 1% in all cases.55
More datasets are required, as in general, AI deep learning forthe neural network is better with more datasets. The recognition
frequencies and accuracies of each fetal facial expression related
to gestational weeks by AI should also be analyzed.

F u r t h e r P e r s p e c t i v e​
The advantage of multi-modalities for AI has been presented in theclassification of the uterine cervical squamous epithelial lesion from
colposcopy images combined with HPV types27 and the predicting
live birth from blastocyst images combined with the conventional
clinical embryo evaluation parameters. 29 Therefore, fetal facial
expressions can be classified by image and by incorporating images
with gestational age and other associated parameters.

Donald School Journal of Ultrasound in Obstetrics and Gynecology, Volume 15 Issue 3 (July–September 2021)

Recognition of Fetal Facial Expressions by AI

Fig. 2: The profiles of the confidence scores for each category per image (mean ± standard deviation) for the test dataset

Established AI has no intrinsic bias for classifying images. Thus,
AI can show objective findings regarding fetal facial expression
recognition, which could advance research on the fetal central
nervous system and brain development. The establishment of
AI classification of fetal facial expressions could enable objective
fetal neurodevelopment investigation by applying tests such asmini KANET that is used for predicting postnatal developmental
disabilities.56 Further establishment of an advanced AI recognition
for fetal facial expression using incorporated images with associatedparameters will be able to reveal correlations between facial
expressions and parameters such as fetal physical development,
multiple pregnancies, parity, siblings, maternal personality,

maternal disease, mental and physical development after birth,
personality formation, score in school, intelligence, etc. Subsequent
observation data of medical and social factors obtained in cohort
studies or retrospective studies may aid mothers in the next
generation in providing optimal treatment for their fetuses.

C o n c lu s i o n​
As the fetal facial expression is considered to be important for
non-invasively investigating the fetal central nervous system andbrain development, AI may be useful in this aspect with the adventof 4D ultrasound.

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Recognition of Fetal Facial Expressions by AI
Table 1: Statistic results of AI for classifying fetal facial expressions
Fetal facial expression
Statistic results
True-positive number
True-negative number
False-positive number
False-negative number
Accuracy
Area under ROC curve
F1 score
False discovery rate
False-negative rate
False-positive rate
Informedness
Markedness
Matthews correlation coefficient
Negative predictive value
Positive predictive value (precision)
Sensitivity (recall)Specificity

Eye blinking
27
223
0
1
0.996
1.000
0.982
0.000
0.036
0.000
0.964
0.996
0.980
0.996
1.000
0.964
1.000

Neutral face
88
162
1
0
0.996
1.000
0.994
0.011
0.000
0.006
0.994
0.989
0.991
1.000
0.989
1.000
0.994

Mouthing
90
161
0
0
1.000
1.000
1.000
0.000
0.000
0.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000

Scowling
9
242
0
0
1.000
1.000
1.000
0.000
0.000
0.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000

Smiling
4
247
0
0
1.000
1.000
1.000
0.000
0.000
0.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000

Tongue expulsion
7
244
0
0
1.000
1.000
1.000
0.000
0.000
0.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000

Yawning
25
226
0
0
1.000
1.000
1.000
0.000
0.000
0.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000

ROC, receiver operating characteristic

Fig. 3: The receiver operating characteristic (ROC) curve of AI for the test
dataset. The area under the ROC curve was 1.0

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