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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_980_Библиотеки_им_академика_М_И_Перельмана
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P. Motie et al.
3.2.3 Facelift Surgeries (Rhytidectomy)
The facial aging process is affected by various factors such as bone loss and remodeling, collagen loss, and skin wrinkling [119]. Current modern societies place a
great deal of importance on youthful appearance by considering old faces as unattractive and undesirable on the one hand and popularizing juvenile looks on the
other hand; as a result, the patients’ desire for facelift surgeries to reach a youthlooking appearance has increased signicantly over the recent years [120]. Several
studies attempted to evaluate the facelift surgery rejuvenating outcome objectively
and quantify the results using a variety of equipment, software, scales, and anthropometric assessments [121]. These studies reported that human estimations of antiaging outcomes of facelift surgeries are a reasonably consistent method, evaluating
the patients’ age reduction with a high degree of accuracy and minimal contribution
from non-important factors. In addition, with recent incredible developments in
image analysis, AI through DL can be a promising assistant in pre- and postoperative aesthetic outcome measurements and also comparing age-reductive effects of
various facelift techniques which can lead to more post-op satisfaction of patients
[122, 123].
3.2.4 Otoplasty
Ear deformities are corrected by surgical (aesthetic surgery) and nonsurgical therapies as ear molding and splinting to achieve proper hearing function and auricle
symmetry on both sides. Compared to surgical approaches, the nonsurgical procedure has fewer postsurgical morbidities, is noninvasive and painless, and has better
outcomes. Since the best time for these treatments is up to 6weeks after birth, time
is an essential factor in nonsurgical interventions. Unfortunately, the ear deformity
diagnosis mostly occurs after the proper window period for nonsurgical approaches
[124–126]. In a recent study, the potential of CNN in recognizing complex patterns
from 2D photographs was used for automatic ear deformity identication. The
results showed that DL was able to distinguish normal and un-normal ear photographs with high accuracy (94.1%) (Fig.7) [127]. Further another DL model was
trained and conrmed to evaluate the outcomes of the ear reconstructive interventions. The model showed up as a robust evaluation tool that was able to resolve the
low interrater reliability problem by its objective, dynamic assessment [126].
3.2.5 Blepharoplasty
The upper eyelid’s downward posture produces visual abnormalities that necessitate
surgical surgery to rectify the eyelid lamellae [128]; blepharoplasty also is used for
the correction of emotional expressions such as happy, sad, angry, and surprised
expressions [129]; similar to other applications of AI in cosmetic surgeries, age
recognition also is used in blepharoplasty [130]; evaluating changes in baseline
emotions after blepharoplasty surgery or 3D evaluation to gain better results is a
known function of AI in blepharoplasty [129, 131]. To enhance the patient-surgeon
relationship and the surgeon’s responsiveness, a machine learning model was used
to analyze patients’ most common pre- and postoperative questions uploaded to
realself.com. The model showed that the patient’s priorities before and after the
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Fig. 7 Top row: ears classied as deformed by CNN which is in agreement with the physician
assistants’ evaluations. Bottom row: ears classied as deformed by CNN but received good scores
from the physician assistants [126]
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operation are primarily about correcting certain features or knowing about post-op
possible complications [130].
3.2.6 Hair Transplant
AI among the development of vision-based system imaging and robotic techniques
improved hair transplant grafting procedures and the accuracy of possible results
[132]. For example, a robotic FU procedure can boost hairs’ numbers in each graft
and attempt for harvesting [133].
3.3 Implementation ofAI forOral andMaxillofacial
Reconstructive Procedures
3.3.1 Dental Implants
The desire for dental implants is increasing at a rapid rate. The results of real-life
examples reveal that not all dental implants are successful. Therefore, it is critical to
segregate patients whose implant does not lead to successful outcomes before surgery. Moayeri etal. proposed a combined prediction model for evaluating dental
implant success. W-J48, support vector machines (SVM), neural network, K-NN,
and naïve Bayes were the classiers used in this model. Each classier’s internal
parameters were tuned. These classiers were integrated in such a manner so that
the maximum possible accuracies were achieved. They considered factors such as
gender, age, systemic conditions, smoking, location, placement, loading, diameter,
length, system, type, platform, connection, parallel or taper, over-denture, and sinus
lift in prediction methods. When comparing the performance of several prediction
algorithms using ve-, seven-, and tenfold cross-validation, the tenfold cross-validation approach outperforms the others. W-J48 had the most accurate outcomes
among single classiers, while the proposed combined method was even more precise [134]. Oliveira et al. conducted a similar approach and found constructive
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Anterior
Mesio-distal
position for
restoration
Fig. 8 The decision tree model for implant survival based on the implants’ mesiodistal position
Inadequate
Adequate
Placement
site
Survival
Posterior
Antero-
posterior
Osseo-
integration
Survival
Survival
radial basis function-dynamic decay adjustment (RBF-DDA) neural networks with
parameter selection were the most benecial method to predict the success of
implant rehabilitation [135]. Ha etal. tried to nd the most important prognostic
factor predicting long-term implant survival. They used machine learning methods
instead of traditional statistical methods because these approaches have analytical
capacities for small sample sizes and may uncover a previously undiscovered component that inuences the outcome [136]. Both of the machine learning methods,
the decision tree model (Fig.8) and SVM, revealed that the implant’s mesiodistal
location in one tooth area and also in a total arc from the midline is the most inuential factor in predicting its prognosis [137].
As investigated, various variables such as applied stresses, the bone quality and
quantity, and prosthetic material and form impact the bone and implant interface,
implant and abutment interface, and abutment-prosthesis interactions, all of which
affect the prosthesis’ success [139]. Considering variations in porosity and size of
the xture, a genetic algorithm was employed by Roy etal. to build patient-specic
dental implant systems. Surrogate models based on articial neural network (ANN)
have been proven to be a suitable way to turn nite element analysis results into an
objective function for the optimization process that is less computationally demanding. In the complicated paradigm of dental implant design, combining the desired
function with the ANN model results in a unique technique to identify the optimal
solution within an authorized range (Fig.9) [138].
3.3.2 Craniofacial Implants
Cranioplasty is the surgical intervention restoring the original morphology of the
skull with cranial implants. Nowadays, cranial implants are produced manually by
third-party companies with the help of computer-assisted design (CAD) software
based on CT images. This process could be time- and money-consuming and
requires temporary coverage of bony defects with an interim restoration. Later, secondary surgery is mandatory to remove the provisional bonnet and replace it with
the nal restoration, including repeated general anesthesia. Hence, an automatic,
instant in-hospital (or even in operation room) manufacturing technique is desired
to dispel the weakness of the classic method. Ji etal. established two databases of
skull CT images to train algorithms. They manually created 240 articial bone
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ab c
Fig. 9 (a, b) The 3D implant model designed by ANN and FEA ndings. (c) The mandibular
model with an inserted implant. (Reprint with permission from [138])
abnormalities on CT images of 24 healthy patients. The other database consisted of
1503 data pairs from 167 healthy skulls. A path-based training strategy with supervised learning was used to train DL models by these databases. Covering the bony
defect of the skull (Fig.10) can be formulated as a high-resolution volumetric shape
completion task well handled by the AI models. This algorithm has three signicant
advantages compared to similar ones: (1) It uses high-quality images for both training and evaluation. (2) Unlike most medical images, the skull images are binary and
sparse, making the conventional DL methods incapable. (3) Injected defects have
various shapes and are not limited to a simple geometric schema to ensure the generalizability of the algorithm to act appropriately in clinical situations. This modern
technique will reduce the wait time. The cranial implant can be manufactured in the
operation room, and the surgeon is allowed to complete bone resection and restoration in a single intervention. It has been said that incongruence between the borders
of two adjacent patches can lead to a bumpy and uneven surface or edge, which is
not desirable in restoring the skull; hence further improvements are required to fulll this incompetency [140, 141].
3.3.3 Craniofacial Surgery
Cleft lip and/or palate is the most common congenital oral and maxillofacial malformation, which involves 3in every 2000 babies in the USA [142]. AI has been
used to predict the risk of developing cleft lip and/or palate and diagnose cleft lip
and/or palate and in pre-surgical orthopedics and most often speech assessment of
babies with this condition. Also, some studies are conducted concerning surgical
procedures like orthognathic surgery or plastic lip surgery in these patients [143].
In surgical correction of these defects, surgical markers and incisions play a
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a
b
Fig. 10 (a and b) Automatically designed 3D printed implants
ab
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Fig. 11 (a) Surgical markers labeled by experienced doctors, (b) specied surgical markers by
CLPNet. (Reprint with permission from [145])
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crucial role in the quality of rectication [144]. CLPNet is a deep CNN marking
correct surgical points and incisions. It has been trained by a dataset composed of
2568 images of complete cleft lip patients. Experienced doctors conducted the
image labeling process, pointing the surgical markers (Fig.11a). It showed much
better performance than CSR-based systems such as the supervised descent
method (SDM) or local binary features (LBF). It means the CNN can appropriately adapt extracted facial features with the localization of surgical markers
(Fig.11b) [145].
3.3.4 Craniosynostosis
Craniosynostosis is a congenital deformity caused by the early fusion of cranial
sutures, causing many aesthetic and developmental problems. The prematurely
fused suture and the accompanying cranial deformation have been used to classify
cranial synostosis. Early detection is critical for the avoidance of severe problems,
effective therapy, and the considerations of surgical repair [146–148]. Mendoza
etal. proposed a fully automatic machine learning-based approach using a statistical shape model to extract diagnostic features of craniosynostosis and detect deformities from CT images (Fig.12). The strengths of this method are:
• Automated detection of cranial areas using graph cuts
• Selection of the most similar morphology to a patient using a multi-atlas of nor-
mal anatomy
• Suture fusion detection
• Registration by masked regions (dening a set of manual landmarks on a healthy
head CT image (the template) to indicate the region of interest for cranial mor-
phology assessment)
• Utilizing quantitative measurements of local shape and deformity to formulate
diagnoses and classications
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Multi-Atlas of
Head CT
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Template CT
with landmarks
RegistrationSegmentation
ROI Delineation
from Landmarks
Normal Shapes
Normal Shapes
Model
Compute SDF
Representation
Bone Segment
Labeling
Compute Malformation
and Curvature
Discrepancy
Compute Fusion Index
P. Motie et al.
Diagnosis via
Classification
Fig. 12 Diagram of the statistical shape model. ROI region of interest, SDF Danielsson’s signed
distance function. (Reprint with permission from [148])
• Acceptable sensitivity (92.7%) and specicity (98.9%) in diagnosing defec-
tive skulls
• Correctly classifying new subjects with 95.7% probability [148]
Metopic craniosynostosis (MC) results from the early fusion of a metopic
suture. Objective assessment of MC severity is benecial in treatment planning,
whether to operate or conservatively manage the condition. It also may be helpful in the prediction of poor operative outcomes, including intracranial hypertension and aesthetic deformities. Plain radiography, physical examination, and CT
imaging are traditional approaches for determining the severity of craniosynostosis; nevertheless, these modalities ultimately rely on the clinician’s subjective
judgment. Bhalodia et al. designed a method combining the statistical shape
analysis results with experts’ severity ranking providing an objective tool for MC
severity measurement. In this method, the experts ranked the severity of deformity in images based on the Likert scale (from 1 to 4). ShapeWorks software
analyzed the geometric relationship of 2048 different points on the 3D skull
shapes. After pattern recognition by shape analysis and the experts’ ranking, a
modernized machine learning algorithm (maximum likelihood estimation) utilized these values, calculating unknown parameters that would complete the
severity prediction model, thus providing more accurate predictions. They compared the accuracy of this 3D model with the interfrontal angle (IFA) method
based on the rankings assigned by 18 craniofacial experts. INF method has
recently been widely used for severity determination in conjunction with clinical
and CT image assessment [149]. The comparison results revealed that the new
model might be more accurate in quantitative MC severity prediction than previous well-adapted methods [150].
Theoretically, machine learning even has the potential to differ synostotic and
deformational plagiocephaly using 3D surface photographs without using ionizing
radiations [151].
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3.3.5 Mandibular Reconstruction
The mandible’s morphology could be altered due to traumatic injuries or radical
surgical procedures to remove malignancies [152]. This alteration may harm the
patient’s aesthetic and functional needs. Determining the detailed original shape
of the bone before the alteration is often challenging because the mandible’s
morphology is unique and varies extensively among the population [153, 154].
Because of this issue, utilizing a universal standard mandibular model to reconstruct the bone is inappropriate [155]. On the other hand, digital surgical
approaches like three-dimensional virtual surgical planning (3D-VSP) frequently
necessitate the use of an anticipated mandibular reference model [156]. Currently,
the most common digital method used in repairing the defective area is to mirror
the intact side (Fig.13) or manually choose a similar entire mandible, locally
fuse the data, and manually smooth the borders. However, bilateral or massive
lesions and lesions crossing the midline are hard to repair with these techniques [157].
Data aligned rigidity constrained exhaustive search (DARCES) algorithm and
the iterative closest point (ICP) algorithm are favored surface registration algorithms, rst used in isolation [158, 159]. A combination of these two techniques
named hybrid DARCES-ICP has been used to reconstruct fractured mandibles
using surface matching. This completion was performed in 2D CT slices in different
spatial axes but was not able to demonstrate a 3D model of reconstructed bone
(Fig.14) [160].
Generative adversarial networks (GANs) give AI the ability to create and consist of a generative neural network for data generation and a discriminating neural
network for identifying whether data are true [161]. This technique learns an
image’s morphological traits and then utilizes them to build entirely new photorealistic pictures [162, 163]. Liang etal. used a deep convolutional generative adversarial network (DCGAN) called CTGAN to create pictures that adhere to the
Fig. 13 Mandibular reconstruction using the mirror technique. Mirror repair or manually searching the similar mandibles for local data fusion and smoothing the borders are the usual technique
in a conventional surgical simulation; the accuracy of this method is typically poor
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Fig. 14 The rst row represents shattered real patients’ mandible pieces on CT slices. The second,
third, and fourth rows illustrate the reconstructions produced by DARCES, ICP, and a hybrid
DARCES-ICP method, respectively. (Reprint with permission from [160])
anatomical characteristics of the mandible. They trained this machine learningbased model by CT scans of typical mandibles. The model could reconstruct mandibular deciencies by generating natural and individualized 3D pictures (Fig.15).
Compared to previous standard approaches such as mirror inversion, CTGAN lowers the manual process, enhances mandibular completion efciency, and avoids
technical discrepancies in classic procedures in diverse medical settings [157].
Even though current medical technology cannot give a patient a mandibular prosthesis straight from a computer design, the design is essential since it serves as a
guide for the clinician.
CASE 4
View 1
Vie
Vie
Vie
Vie
Vie
Vie
View 2
ab
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w 1
w 2
w 3
w 4
w 5
w 6
Original
Pre-Completion
Masked defect
Completed
Original Original
CASE 1
CASE 2
CASE 3
CASE 5
CASE 6
CompletedCompleted
Fig. 15 3D mandibular reconstruction. (a) In case 4, an extensive defect across the mandibular
midline is completed and demonstrated in different views. (b) The function of CTGAN in reconstructing different defects with diverse positions and features [157]
4 Conclusion
AI could be employed in all elds of maxillofacial surgery, including diagnosis,
determination of prognosis, surgical planning, and operation. However, current AI
models are typically one-type data-dependent, i.e., radiographic or cytopathologic
images, while to achieve more accurate diagnostics, more medical information of
patients is required to be integrated into the designed models [2]. On the other hand,
most of the advances in this eld are concentrated on lesions’ diagnosis or prognosis. Treatment planning and pre-operation analysis of patients have also gained
some attention. But clinical application of AI in surgeries is still restricted due to
possible risks and errors, insufcient improvement of systems, and mistrust and
hesitancy of patients about novel medical interventions [164].
Studies concerning the use of AI in maxillofacial surgery have already begun and
have a developing trend, but they are lagging compared to research assigned to
applications of AI in other elds of medicine; thus, there is plenty of room for further investigation and improvements.
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