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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_537_Библиотеки_им_академика_М_И_Перельмана
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a wound’s surgical candidacy by using different imaging modalities [13].
Photographic, infrared, and spectroscopic imaging has been used to classify
burn depth in machine learning applications, with further classification of
wounds as being surgical or nonsurgical. The predicted burn depth by the
ML application is the classifier for a surgical procedure [55–60]. For
instance, superficial and superficial partial thickness wounds are labeled as
nonsurgical, while wounds classified as deep partial thickness to full
thickness are labeled as surgical wounds.
A mobile application called “DL4Burn” using ResNet50 architecture is
developed in [54] to predict surgical burn candidates by using multiple
factors with the training of the model completed on the ImageNet dataset
[61]; ResNet50 architecture is a variant of the ResNet architecture that
contains 50 layers consisting of one MaxPool, one Average Pool, and 48
convolution layers. Expert input is utilized in [54] for the developed
application with visual burn images served as the input of the DL
application for making decisions on burn-related surgical candidacy
evaluation. Comparisons are made with surgeons’ assessment as well as
unimodal ResNet50-based models. In comparison to subjective surgeons’
assessments, burn wound surgical candidacy prediction of the DL method
was highly accurate: the DL method’s average and best accuracy values
were 0.86 and 0.94, respectively, while surgeons’ assessments resulted in an
accuracy range of 0.65 to 0.73, and unimodal ResNet50-based model’s
average and best Accuracy values are determined to be 0.78 and 0.81,
respectively. The area under the receiving operator curve of the DL model
is determined to be high with an average value of 0.91 and best value of
0.98, while the average and best values of unimodal ResNet50-based model
were 0.83 and 0.85, respectively. The DL method and the mobile
application DL4Burn showed its effectiveness in complex decision-making
processes involving burn wound care.
A DL model is developed in [62] with the aim of analyzing eyelid
morphological properties such as eyelid position and contour abnormality
using the associated images based on pre- and post-blepharoptosis surgical
information. A mix of manual and automated distance measurements is
collected. Attention Recurrent Residual Convolutional Neural Network
based on U-Net (Attention R2U-Net) is utilized by the authors for attaining
the DL solution. The automated distance measurements of the DL method
and manual assessment of a surgeon on Margin Reflex Distances MRD 1
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and MRD 2 of the operated eyes are determined to have excellent
agreement by the authors. The attained results suggested automated
measurements’ high repeatability. Great improvements in MRD 1, upper lid
length, and corneal area are found for pre- and postoperative eyelid
morphology. Overall, the DL method was determined to be promising for
automating the assessment of eyelid features to streamline time consuming
manual measurements based on statistical results such as the intraclass
correlation coefficient calculated between manual and automated
measurements of the operated eyes’ marginal reflex distance that was found
to range from 0.934 to 0.971 (P>0.001).
Deep learning is applied to rhinoplasty surgical procedures to predict
rhinoplasty status in [7]. A CNN called “RhinoNET” is developed in the
study to assess model accuracy compared to surgeons’ abilities for similar
applications in plastic surgery. One of the goals of the study was to
discriminate pre- and postsurgical rhinoplasty real-world photographs of
faces by training a predictive model. The developed model is tested using
predictions of attending and resident surgeons, and the model is used for the
development of a mobile application to be used in mobile phones and
tablets. The inputs of the network had pixel structure of the pre- and
postsurgical images with the associated surgical procedure status. A total of
about 22.6 thousand publicly available pre- and post-rhinoplasty surgery
images were collected. Approximately 2.27 thousand test set images were
evaluated by a set of healthcare personnel consisting of residents and
attending surgeons for comparison with the CNN classification. Correct
status prediction of RhinoNet was 85% on the testing images. Specificity
was 0.826, while sensitivity was 0.840. The performance comparison of
RhinoNET and healthcare personnel was determined to be almost
equivalent in accuracy in predicting rhinoplasty status.
The metopic suture’s early fusion is known as metopic craniosynostosis
(MC), and it poses a diagnostic challenge, particularly in moderate cases,
due to the metopic suture’s normal closure well before one year of age [63].
The subjective evaluation of the surgeons’ degree of deformity can cause
variability in measurements and decisions noting the slight differences that
occur in deformity. Interfrontal angles’ gold standards are compared to
those that are developed by using the model on 82 CT images that had
20.3% metopic skull structure while the remaining group was non-affected
controls. A software called ShapeWorks is used by the authors to analyze
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the skull shapes. The authors determined the model to accurately predict the
severity of metopic CS more often than models using interfrontal angles
and identify the level of skull deformity with severity (p<0.1).
CNNs have been used in a variety of dental settings that would also
relate to plastic surgeries.
Such applications included the following:
Prediction of perioperative blood loss in orthognathic surgery [67]
Automated skeletal classification is possible by lateral cephalometry [68]
Radiographic detection of periodontal bone loss [64]
Diagnoses of cystic lesions using panoramic and cone beam computed
tomographic images [65]
Survival prediction of oral cancer patients is also possible [66]
Dentofacial dysmorphisms and/or malocclusion diagnosis using a
developed CNN is attempted in [69]. The main goal of the study was to
assess the effectiveness of the CNN to judge soft tissue profiles requiring
orthognathic surgery using facial photographs. The frontal and right side
images of 822 patients’ dentofacial dysmorphic and/or malocclusion data
were included, with exactly half of these subjects’ needing orthognathic
surgery and the other half not needing the surgery. Well-known measurable
values used in deep learning applications, such as of Accuracy, Precision,
Recall, and F1 scores, are determined to be 0.893, 0.912, 0.867, and 0.889,
respectively, by the authors indicating the ability of the CNN to judge soft
tissue profiles requiring orthognathic surgery relatively accurately with the
photographs alone [69].
5 Gender Classification by Using Deep Learning
Several attempts have been made to use deep learning and machine learning
algorithms to classify gender of a person [91–103]. Gender classification of
a person can be conducted through the iris’ texture; such a classification
using CNN on biometric images is conducted in [15]. One of the ultimate
goals of such research is to select the algorithmic features that allow
optimized accuracy achievement for classification of appearance as male or
female. The authors used the ND-Gender-From-Iris-Dataset [90], a database
that contains the gender information and iris features of the subjects
containing 3000 images, one image per class for 750 males and 750
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females, and the UND_V dataset for attaining the results. The Local Binary
Pattern (LBP) and the Support Vector Machine (SVM) are used prior to
[15] on these datasets for gender classification. The typical binary approach
for the output is followed by the authors. The authors extracted features
from the developed dense convolutional network with the best accuracy
achievement of 96.52%. Additionally, morphometry of eyes is determined
to be a good input for the deep learning network that yielded a good
performance for gender-based classification.
Facial feminization surgery data are collected for gender classification
purposes using ANN in [79]. CNNs of Amazon, IBM, Face++, and
Microsoft are used for gender identification. Confidence analysis is
conducted using IBM and Amazon, with the least confidence classified as
zero and one represented highest confidence. Total 20 patients’ pre- and
postoperative male-to-female transition facial images are used, along with
the 120 facial images of control group images that consisted of unoperated
cisgender men and women. The data is formed into four groups consisting
of postoperative facial feminization surgery, preoperative facial
feminization surgery, male controls, and female controls. Analysis of the
study focused on gender identification improvement from pre- to
postsurgery, femininity confidence of each group, and accuracy of correct
placement within each one of the four groups. The authors concluded
effective correct classification of males’ control group frontal image
classification at 100% and female control groups’ frontal image
classification at 98%. Correct gender classification following facial
feminization surgery was at 98%. The authors concluded that gender
classification using CNN allowed facial recognition and showed improved
gender-typing of transgender women from preoperative facial feminization
surgery to postoperative facial feminization surgery [79].
6 Other Applications of Deep Learning, Machine
Learning, and Artificial Intelligence
Psychological and social factors are important aspects of plastic surgical
outcomes. The ability to restore emotional expressions of an individual after
plastic surgery would be essential in addition to fulfilling physical
expectations of the patient. Facial expressions are translated during social
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interactions for interpreting emotions and reactions to events. Pre- and
postsurgical evaluation of facial reanimation surgery patients’ facial
expressions are analyzed in [71] using a standardized set of images called
Amsterdam Dynamic Facial Expression Set [104], containing seven
different emotional expressions. A commercially available artificial
intelligence system is used to analyze videos in the dataset to test the seven
emotional states, which are happiness, sadness, anger, surprise, fear,
disgust, and neutral; these emotions are considered as principal facial
movement patterns universally, and any impairment in the ability to express
these emotions legibly and appropriately is considered as a significant
social disability [105–108]. Noldus FaceReader software application [110]
is used by the authors in [71] for analyzing the video data of 15 patients
with facial palsy and 8 individuals of the control group without surgical
procedures based on the relative proportions of seven cardinal facial
expressions detected within each video. The authors observed a greater
post-operative happiness detection rate of 42% when compared to the 13%
of the same rate preoperatively, noting that control subjects smiling
detection rate was 53%. The sadness and neutral face detection rates were
reduced from preoperative rates of 15% and 57% to postoperative rates of
9% and 37%, respectively. Hence, objective quantification of facial
expressions is shown in [71] with pre- and postsurgical reanimation.
An ANN that had a feed-forward neural network nature is developed in
[111] for effective surgical site infection prediction model in patients
receiving free-flap reconstruction after surgery for head and neck cancer in
addition to a multivariate logistic regression (LR) developed for the same
purpose to compare the two techniques. A total of about 1.8 thousand freeflap reconstructions collected over the span of nine years starting in 2008
are used for the analysis, with 23.6% of the data used postoperatively for
the analysis. The training set consisted of 70.17% while the testing set was
29.83%. Brier score, the area under the curve (AUC) of the receiver
operator characteristic curves (ROCs), Somers’ Dxy rank correlation
coefficient, c-index, calibration curve, and Brier score are used to measure
the performance of the two different models measured. The authors
concluded that LR’s preoperative AUC value of 0.81 was weaker than
ANN’s postoperative AUC value of 0.892; therefore, prediction of the
surgical site infection after free-flap reconstruction in patients receiving
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surgery for head and neck cancer was much more efficient by using ANN
with the highest overall performance in [111].
ML applications are used as a part of craniofacial surgical procedures to
automate diagnosis of non-syndromic craniosynostosis [112]. A regularized
linear discriminant analysis algorithm is used in [22] by using CT scans of
141 persons for training with approximately 35.71% having one of sagittal,
metopic, or coronal craniosynostoses to diagnose craniosynostosis; the goal
was to distinguish between different types using an index of cranial suture
fusion along with deformation and curvature discrepancy averages across
five cranial bones and six suture regions. Specificity and sensitivity value of
98.9% and 92.3% are attained, respectively, for automatic classification of
differentiating craniosynostosis types based on computed tomographic
scans. As noted previously, declining possible variability of the results
through automated analysis can be helpful in future applications.
Optimization and machine learning methods are used together for face
recognition methods to attain optimal outcomes for a variety of
applications, including but not limited to, security and support to healthcare
providers, in the literature.
Granulation of a face image from a genetic algorithmic standpoint is
outlining fine details of the image at a certain level for the algorithmic
structure to read and act upon for further processing. A multiobjective
evolutionary granular algorithm is introduced for matching face images
before and after plastic surgery in [132]. The algorithm is designed to
simultaneously optimize the selected features for each face granule along
with the weights of individual granules. The authors used a plastic surgery
face database to test the effectiveness of the proposed algorithm that yielded
high identification accuracy when compared to the existing algorithms and
a commercial face recognition system. Another multiobjective differential
algorithm is used in [128] by initially preprocessing the input image and
then applying face detection, face granulation, feature extraction, and
matching. The goal of the researchers is to differentiate images captured
before and after plastic surgery. The results are tested on a limited number
of images with success achieved; therefore, further investigation is needed
on the preliminary outcomes to be able to attain strong results.
The performance of six face recognition algorithms: Principal
Component Analysis, Fisher Discriminant Analysis, Local Feature
Analysis, Circular Local Binary Pattern, Speeded Up Robust Features, and
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Neural Network Architecture, based on 2-D Log Polar Gabor Transform on
plastic surgery database, are experimented in [129], and the authors
concluded that the tested face recognition algorithms during year 2010 had
difficulty in determining nonlinear surgical variations that arise from
surgical procedures on the images. It has been observed over the years that
the algorithms are improving toward addressing the challenges, with room
for further improvement [130, 131]. We refer to [133–135] for some other
techniques generated and used for plastic surgeries in the past.
7 Conclusions and Future Work
The structure of the results presented in this work are similar to [12, 16, 33,
51, 70, 136–166]. Artificial intelligence-based research and the associated
applications in facial plastic surgeries is limited. There are certain areas that
could use machine and deep learning applications including surgical
planning, CT image reconstruction, and bone segmentation that have
potential error sources that can lead to inaccuracies and impair the treatment
outcome. Integrating AI into applications can impact imaging noise,
metallic structures, and patient movements that can heavily affect the CT
image quality after reconstruction. Image segmentation technique can have
a substantial impact on the accuracy of the resulting model. Noting the preand postsurgical data comparison, AI/ML/DL techniques proven
reproducibility can guide the surgeons with surgical planning that heavily
relies on the surgeon’s domain expertise and manual software input.
Psychological and social factors in reconstructive surgery for hemifacial palsy has been studied in the literature [76]. Psychosocial factors that
may be associated with patient satisfaction is analyzed using thematic
analysis in the study; This analysis contained data attained from 106 adults’
post-surgical assessment by using qualitative methods with all participants’
recorded interview transcriptions included. The same surgeon conducted
two-stage reconstructive procedures using vascularized free muscle grafts.
The hospital anxiety and depression scale, demographics’ questionnaire,
and the facial paralysis evaluation measure are used for the assessment of
the patients in the study. One of the research methods we recommend to
conduct is to apply ML/DL techniques to extensively assess similar or same
data to compare with manual data collected. To the best of our knowledge,
the ML/DL techniques by using text recognition and analysis have not been
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conducted extensively in the literature for facial plastic surgery
applications.
ANN is used in [113] for burn depth analysis noting the importance of
early excision and grafting is the treatment of choice for deep dermal burn.
The aim of the researchers was to develop a noninvasive objective method
to predict burn healing time that resulted in predictive accuracy of 96% for
burns that healed in less than two weeks and 75% for more than two weeks.
ANN’s overall predictive accuracy of burn healing was determined to be
86%. Noting that this result was a general result, a potential improvement
can be testing the same results on facial plastic surgery patients that had a
similar experience.
Sequential characteristics of a dataset can be recognized by a recurrent
neural network (RNN), and this sequential characteristic analysis help to
determine a pattern to predict next step in the sequence. One area of RNNs
used in DL is development of models that simulate neuron activity in the
human brain. Researchers have used RNN and Shepp–Logan phantom
developed in [114] as a standard test image for digital reconstruction of a
human head. Image reconstruction algorithms have utilized Shepp–Logan
phantom in [115–126] by using different imaging modalities such as Fan
Beam and Parallel Beam CT scans. We recommend researchers to
investigate along the line of RNN application on facial plastic surgeries and
leave the ways to accomplish such tasks to readers’ imagination. Similarly,
a Generative Adversarial Network (GAN) can be used in facial plastic
surgery applications that was also suggested for using as a part of total hip
arthroplasty applications in [127].
Facial nerve grading system methods are outlined in [77, 78]. The
House-Brackmann grading scale (HBGS), endorsed by the Facial Nerve
Disorders Committee of the American Academy of Otolaryngology, is
successful in providing a standard method for reporting facial nerve
function. HBGS is unable to distinguish between finer grades of facial
nerve dysfunction, the subjective nature of the scale that results in high
interobserver variability, and the ambiguity with which it addresses the
secondary defects of facial nerve function [77, 78]. One of the research
areas that has not been explored is the use of ML techniques, or particularly
DL approach, to reduce variability and identify secondary defects of facial
nerve function.
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Smartphone applications have been developed for certain purposes that
we outlined in this work, and there are other smart phone applications that
are developed for plastic surgical purposes that are not tested on the facial
cosmetic and reconstructive surgeries.
An example of a smartphone for postoperative monitoring of free flap
tissue perfusion application is introduced in [109] that is tested on human
fingers. The goal of such an application is to reduce the cost of monitoring
and simplify the complex nature of the devices used for postoperative
monitoring. This smartphone application and similar developments can be
tested for facial plastic surgeries as an improvement in this area of interest.
Furthermore, one other area of investigation that has not been explored
is using AI/ML/DL methods to determine whether professional
psychological treatment prior to facial cosmetic plastic surgery promotes
the patient to change his/her mind to undertake plastic surgery. These
methods can help to determine attributes that may help to decline the
surgery after such a treatment. Additionally, the impact of religious belief
on the plastic surgery decisions can be investigated with the use of
AI/ML/DL methods to identify the attributes of the factors that impact
going under a plastic surgery.
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