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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.
and distal ends. CNN architecture was trained to evaluate the visibility and location
of the piriform aperture’s inferior line, which is located lower than the normal side
in the cleft side. In addition, it evaluated microdontia, un-eruption, missing teeth,
and medial inclination of the affected side lateral incisor. This information was
extracted to classify the input data into the mentioned subcategories. Despite the
errors (Figs.1 and 2), the model can be a potential assistant for clinicians in the
detection of CAs and classifying them in CAs with or without CPs [95].
3.1.5 Salivary Glands’ Pathology
Salivary glands’ inammation happens due to different conditions like salivary
ow obstruction, infection, and diseases such as Sjögren’s syndrome (SjS). SjS
is a chronic inammatory autoimmune disease of salivary glands with unknown
origination [96]. The main cause of obstructive sialadenitis is sialoliths.
Sometimes patients with sialoliths are asymptomatic and stones are accidentally
discovered in panoramic images. Even in asymptomatic patients, early removal
of the stones is necessary because it can increase the possibility of infection and
atrophy. Sialoliths can be misdiagnosed with odontogenic tumors, osteosclerotic
Fig. 1 An equal level of
the inferior piriform
aperture lines on the
affected and healthy sides
and normal alignment of
lateral incisors resulted in a
CA case undetected [95]
Fig. 2 The model detected
a normal case as CA (the
red box). Radiopaque lines
of the hard palate are
superimposed on the
inferior line of the piriform
aperture lines on both
sides. The canine may have
been wrongly recognized
as the congenitally missing
lateral incisor [95]
Application of Articial Intelligence in Diagnosing Oral and Maxillofacial Lesions…
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301
or cemental lesions, especially when it’s located in the mandible and overlaps
with mandibular bony structures [97]; in addition, obstructive sialadenitis and
SjS can have very similar symptoms; therefore, they should be deliberately distinguished from each other. Kise etal. conducted a study for nding sialadenitis
and SjS.They divided the training dataset into three groups: obstructive sialadenitis, SjS, and the control group. Among these three groups, CNN achieved the
highest performance in the identication of SjS with signicantly higher sensitivity than specialist radiologists (83% vs. 72%). Conversely, both DL systems
and radiologists achieved noticeably low total accuracy and sensitivity in detecting obstructive sialadenitis. The main reason is that chronic sialadenitis manifestation dramatically varies based on the disease stage, making the lesion type
prediction more complex and challenging. This study states that obstructive sialadenitis should always be included in the differential diagnosis because it presents a wide range of characteristics from normal to abnormal depending on its
stage [98].
Parotid gland tumors account for a small portion of head and neck tumors and
most of them are benign. Surgical planning of these tumors is signicantly dependent on the tumor histological type. Fine needle aspiration is often used for preoperative diagnosis of tumor type; however, it is expensive and time-consuming.
Also, the sampling process increases the risk of infection and the tumor local
recurrence resulting from tumoral cells’ spreading. Nevertheless, utilizing a technique that is able to detect the tumor nature and location from the imaging modalities would signicantly enhance the clinical practice effectiveness. A CNN model
architecture (modied ResNet model) was trained by MRI images detecting salivary gland tumors and classifying them as benign or malignant lesions. The training dataset was labeled as (1) pleomorphic adenoma, (2) Warthin tumor, (3)
malignant tumor (adenocarcinoma), or (4) free of tumor. The model showed high
performance in distinguishing between benign and malignant tumors; however,
still, among benign tumors, the differentiation accuracy of pleomorphic adenoma
and Warthin tumors was low (misidentication of Warthin tumors as being pleomorphic adenomas). The reason might have been the low number of Warthin
tumors in the dataset and the highly similar manifestations of different benign
tumors. Another weak point of the model was that the tumor segmentation was
done manually, so auto segmentation of the tumoral area can be considered in
future designs [99].
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Outcomes
Accuracy/sensitivity/
specicity
CNN is effective in
the diagnosis of
OA in panoramic
images
Accuracy=0.84,
sensitivity=0.54,
specicity=0.94
CNN has a great
potential to detect
CAs and classify
them
Model 1:
recall=71.1%,
precision=74.5%
F-measure=70.9%
Model 2:
CNN can
effectively show
recall=82.1%,
precision=84.0%
F-measure=81.8%
micro-AUC, 0.93
the type of parotid
gland tumor at the
slice level
CNN can improve
the performance of
Recall=85%,
precision=100%,
P. Motie et al.
detecting
submandibular
gland sialoliths
sensitivity than
specialists in
F-measure=91.9%
Accuracy=70.3% CNN has more
detecting SjS
images
No. of images
for testing Modality
1292 images Panoramic
Type of lesion
detected
Hard tissue/TMJ
osteoarthritis
The goal of the
study
CNN for
classication
TMJ
Deep learning
task
classication
Algorithm
architecture
VGG16 Detection
images
593 images Panoramic
Hard tissue/cleft
alveolus and cleft
osteoarthritis
severity
CNN for
classication of
classication
ResNet AUC=0.82
InceptionV3
DetectNet Detection
palate
alveolar cleft lip
MRI Accuracy, 82.18%;
233 patients,
3791 images
Soft tissue/salivary
gland tumors
(pleomorphic
adenoma, Warthin
CNN for
detection and
classication of
salivary gland
Detection
classication
Modied
ResNet18
images
224 images Panoramic
tumor, and
adenocarcinoma)
Soft tissue/
sialoliths of the
tumors
detection of
DetectNet Detection CNN for
submandibular
gland
salivary gland
stones
images
150 images Ultrasonography
Soft tissue/
inamed salivary
glands
CNN for
identication of
parenchymal
changes of SMGs
classication
VGG16 Detection
Author/
year
Kim
etal./2020
Kuwada
etal./2021
Xia
etal./2021
Ishibashi
etal./2021
Kise
etal./2021
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Outcomes
Deep learning
achieves the same
values of accuracy
and sensitivity
compared to
specialists
automatically
segment sinuses
and evaluate
volumetric
assessments of
sinus inammation
AUC than the
radiologists and
comparable
sensitivity and
specicity to the
radiologists
Accuracy/sensitivity/
specicity
Accuracy=87.5%,
sensitivity=86.7%,
specicity=88.3%,
AUC=0.875
AUC=0.93 CNN has higher
303
images
No. of images
for testing Modality
490 images Panoramic
Type of lesion
detected
Soft tissue/
inamed sinuses
The goal of the
study
Deep learning for
diagnosis of
maxillary
Deep learning
task
classication
Algorithm
architecture
AlexNet Detection
sinusitis
510 patients CT scans Rho=0.82, p<0.001 CNN can
Soft tissue/
inamed sinuses
segmentation of
sinus cavities and
calculation of
Tiramisu Segmentation CNN for
sinus
opacication
score
Waters
radiograph
15,000
patients
Soft tissue/
inamed sinuses
Deep learning for
diagnosis of
maxillary
sinusitis
Detection
classication
Residual
Net
Author/
year
Murata
etal./2018
Humphries
etal./2020
Kim
etal./2019
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P. Motie et al.
3.1.6 Sinusitis
Sinusitis is one of the most prevalent health complaints, with an estimated 20 million physician visits annually in the USA.Sinusitis is mainly caused by recent upper
respiratory viral infection, allergic rhinitis, and also adjacent odontogenic infection.
About 30% of maxillary sinusitis cases can arise from odontogenic factors like periapical or periodontal lesions of the maxillary teeth [100]. CT is the preferred imaging modality for denite assessment of paranasal sinuses in a demanded anatomical
detail. Conventional radiographies such as Waters’ and Caldwell’s view are often
used for initial examination of maxillary sinusitis [101]. Mucosal thickening
exceeding 4mm thickness, uid collection, and cyst-like appearance of mucus are
the characteristic radiographic ndings, indicating sinusitis [75]. In 2019, DL was
applied to diagnose maxillary sinusitis on 80,475 Waters’ view radiographs, and its
function was compared to that of the radiologists. The training and the validation
dataset were labeled into four categories: normal, mucosal thickening (more than
4mm), visible air-uid level, and total opacication. Complicated cases like retention cysts and images that couldn’t be labeled (i.e., the case showing sinus atelectasis) were excluded from the nal analysis. CT ndings were used as the reference
standard in the test sets. In the testing process, most errors occurred when the model
mistook the sinus bony wall with increased mucosal thickness and vice versa.
Nevertheless, as the algorithm showed superior AUC and comparable sensitivity
and specicity to those of radiologists, it might be helpful to be used as the “second
reader” guiding inexperienced clinicians in sinusitis diagnosing and increasing the
radiologists’ diagnostic consistency [102].
As was mentioned earlier, one of the reasons for maxillary sinusitis is the odontogenic and periodontal infection of the maxillary posterior region teeth. On the
other hand, most of the patients note a dental visit a month before the manifestation
of the clinical symptoms, which means that a recent dental procedure might also be
associated with odontogenic maxillary sinusitis (ODMS). Maxillary dental pain is
reported in nearly one-third of patients with true rhinosinusitis. Cheek pain and
discharge of purulent in the oral cavity are also counted as the common symptoms
of ODMS [103]. Therefore, a signicant portion of sinusitis patients primarily visit
a dentist, and panoramic radiography is the rst-order imaging modality for these
patients. Accurate diagnosis of maxillary sinusitis on panoramic radiographies is
challenging because anatomical structures near the sinuses, such as the hard palate
and oor of the nasal cavity, can be overlapped on the sinus image. To support inexperienced observers, a learning model was created using panoramic radiographs of
healthy and inamed sinuses. More than 4mm mucosal thickness, uid accumulation, and/or a cyst-like appearance of mucous extending beyond one-third of the
maxillary sinus were dened as sinusitis. Square regions of interest, including unilateral sinuses, were set and cropped out from the images (Fig. 3). Radiologists
often recognize an inamed sinus by comparing it to the contralateral healthy sinus;
however, the present model can sufciently diagnose the presence of inammation
in single sinuses. This provides additional diagnostic support for inexperienced residents, especially in cases with bilateral inamed sinuses or when both sinuses are
healthy [75].
Application of Articial Intelligence in Diagnosing Oral and Maxillofacial Lesions…
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Fig. 3 Segmentation and
cropping out the ROI
(200*200 pixels) from both
sides. (Reprint with
permission from [75])
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3.2 Implementation ofAI forOral andMaxillofacial
Corrective Surgeries
3.2.1 Orthognathic Surgeries
Orthognathic surgeries are often used to correct hard and soft tissue deformities and
discrepancies that cannot be treated by conventional orthodontic treatments. The
objective of orthognathic treatment is to use a combination of orthodontic and surgical treatments to correct the dentofacial deformities concerning aesthetics and function [104]. Recent advancements in digital radiographic imagery and photographs,
especially the intraoral scanners, and also real-life demands from practitioners such
as reconstruction, tridimensional morphometrics, automated treatment planning,
and customized surgical setup planning in the management of orthognathic cases,
are guiding the trends toward the use of AI in the context of orthognathic surgery
procedures [105].
At the stage of diagnosis and treatment planning, either facial photographs or
posteroanterior (PA) and lateral cephalograms are implemented to analyze the maxillofacial soft tissue and skeletal form, respectively, and thus investigate whether
orthognathic surgery is needed or not. Based on soft tissue judgments, a CNN model
was able to classify frontal and right prole photographs of patients with 89% accuracy based on whether they required orthognathic surgery or not. In this model, after
extracting feature maps from the frontal and lateral photographs by the backbone
network, the global pooling layer vectorizes these high-level feature maps. This
layer is a new replacement for the previous classier layers, sacrices less time for
the calculation process, and doesn’t need xed input dimensions. Finally, the visualization map is created by the VisualBackProp method, highlighting the lips, chin,
and teeth. Considering these sites, CNN will then predict the need for orthognathic
surgery [106]. Another prediction of orthognathic surgery necessity was held by a
DL model using lateral and PA cephalograms. In this study, two CNN models
extracted the features from the posteroanterior and lateral cephalograms. A global
average pooling (GAP) embedded the feature maps of the last convolution layers as
a vector. To trace how the model inferences the images, relevant areas within each
image mainly used for decision-making were visualized by feature visualization.
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Fig. 4 Feature map visualization highlighting the teeth and maxillofacial area [107]
P. Motie et al.
Figure4 shows the color-coded images showing the areas with high or low impact
on this process. Similar to the surgeons, orthodontists, and radiologists, the implemented model determined the need for orthognathic surgery by focusing on the
teeth and the maxillofacial regions with relative accuracy and thus can be used as an
effective screening tool to predict the need for orthognathic surgery for both the
patient and the maxillofacial surgeon/dentist [107].
In conventional surgical planning, a two-dimensional treatment plan is created
after facial photographs and cephalometric analysis; in the next step, model surgery
is performed by mounting the dental casts on an articulator using facebow records,
and a surgical splint is then fabricated to guide the surgeon during the operation.
Recent improvements in three-dimensional imaging methods, such as CBCT, facial
scanning, and digital dental casts, have developed the utilization of three-dimensional virtual surgical planning (VSP) for orthognathic surgeries and the manufacture of surgical splints using CAD/CAM technology [108]. These virtual plannings
are accessible in a web-based workow and the operating room, giving the chance
to conduct the surgical plan with the attendance of the therapist team, including the
maxillofacial surgeons and orthodontists. They also make it possible to run a userdened 3D anthropometric analysis providing quantied information on the deformity. Using virtual surgical planning software enhanced by machine learning,
simulation of the surgical processes such as the osteotomies of the skeletal structures and repositioning of the segments would be possible, thus predicting the surgical results, including the expected dental and skeletal movements. These simulations
are executed through the use of 3D composite models. Such applications like the
Mimics (Materialise N.V., Leuven, Belgium) receive DICOM data from computed
tomography (CT), micro CT, magnetic resonance imaging (MRI), confocal microscopy, X-ray, and ultrasound. After reorienting input images, the anatomical structures (either hard tissue or soft tissue) are then segmented. Finally, all necessary
information from the region of interest (ROI) of the segmented images is gathered
by superimposition or registration to create 3D models [109].We can also use these
simulations to train the residents and educate the patients. In comparison to the
conventional model surgery, this method saves up a signicant amount of time,
increases the surgical procedure accuracy, and makes the process notably simple
and effective [109, 110]. In addition, 3D digital treatment planning software allow
the practitioners to visualize diagrams of each therapeutic approach and the nal
result of each intervention. Clinicians can use the visualized diagrams to explain the
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procedure to the patients and enhance their understanding of the protocol. It also
helps dialogue and planning where there is a multidisciplinary approach (ENT, general dentist, sleep specialist) [105]. The Dolphin “Treat” tool is another application
designed for comprehensive surgical planning. The app receives the patients’ PA or
lateral cephalograms or their submento-vertex (SMV) views as inputs and provides
the users the access to numerous pre-designed plans by surgeons and researchers
worldwide.
1
The outcome of orthognathic surgeries is affected by the long-term orthodontic
treatment phase as well as the short-term surgical procedure. In addition, numerous
interdependent variables such as bony structures, occlusion, periodontal health, orofacial functions, and aesthetics should be noticed when the treatment plan is constructed. A thorough understanding of surface aesthetics changes based on the
planned movements of the bones is also essential; however, the soft tissue analysis
is almost entirely clinical and subjective depending on the clinician’s experience
and artistic sense. The results of these assessments usually tend to be imprecise and
difcult to reproduce. One way to overcome these issues is to record 3D facial
topography. This type of data contains more information than 2D data, and it can be
utilized for creating a posttreatment facial shape predicting system [105, 111, 112].
Landmark-based geometric morphometric methods (GMMs) and DL are two available machine learning methods that are related to 3D prediction. By GMM, we
would be able to systematically map the morphology within a “morphospace” using
homologous landmarks. Using a combination of DL and a GMM, Tanikawa etal.
developed an articial intelligence system that predicts the 3D facial shape after
orthodontic treatment and orthognathic surgery. In orthognathic surgeries, there is a
nonlinear relationship between hard and soft tissue movements along with changes
in different soft tissue sites. For instance, it has been shown that the changes in the
malar-midfacial region after the maxillary advancement in LeFort I osteotomy are
more pronounced than in the upper lip’s [113]. Therefore, the model was designed
based on the nonlinear ratios between soft tissue changes and its underlying hard
tissue. This simulated software not only was accurate enough to be utilized in clinical application but also is applicable in predicting any other 3D shapes after operations, i.e., antiaging surgery, cancer surgery, and cosmetic surgery; however, since
the records were gathered from surgeries performed in two hospitals, the model may
only mimic the treatment outcomes of these facilities, and its generalizability is
unclear [111].
The correction of the skeletal and soft tissue deformities includes manipulating
the present anatomical structures. Putting together every single change made during
surgical and orthodontic procedures results in the nal treatment outcome. To evaluate growth or treatment-related changes taking place between two time points,
superimposition techniques are commonly used. For instance, either lateral cephalograms or dental casts have been used in orthodontics to assess the tooth and jaw
movements during orthodontic treatments [114]. The development of intraoral
scanners and new imaging techniques such as CBCT replaced the dental casts and
1
https://www.dolphinimaging.com/.
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P. Motie et al.
lateral cephalograms with digital 3D models [115]. Since AI has made it possible to
superimpose various digital images, it can be considered as a well-suited tool to
monitor and handle the treatment follow-ups and also gives the opportunity to quantify and visualize the effects of the treatment on maxillofacial structures [105].
3.2.2 Rhinoplasty
Rhinoplasty is one of the most popular cosmetic interventions performed by surgeons. The aim of this operation is usually aesthetic improvements and/or correction of breathing problems by changing the osteocartilaginous architecture [116].
From the aesthetic point of view, the length of the nose should be equivalent to the
height of the forehead and lower face, and the nasal width (from ala to ala) should
be equal to the intercanthal distance. Plastic surgeons strive to achieve these proportions as well as keep a natural-looking appearance. In Borsting etal.’s study, before
and after patient photographs were used to train a DL model to predict whether the
patient has undergone rhinoplasty surgery or not. Compared to traditional machine
learning methods, DL would be a well-adopted tool to assess plastic surgery procedures since it can be trained by real-world photographs taken by nonspecialized
equipment such as mobile phone cameras. The model’s accuracy and precision were
almost similar to that of the experts. This highly accurate DL model was further
deployed as a mobile application making it accessible enough for surgeons, young
residents, and trainers so it can be utilized in telemedicine consultations via electronic communication; it also can be helpful in the management of cases having
uncertain medical histories. Finally, the app would benet surgical trainers for educational purposes as it allows them to observe several rhinoplasties in a large group
of patients [117].
Today antiaging effects of rhinoplasty are not hidden to anyone. Many of the
aging nose characteristic features would be manipulated and guided toward rejuvenation during a successful rhinoplasty surgery. An AI algorithm was used to qualify
these antiaging effects objectively. The model could successfully analyze the photographic inputs taken from the frontal view of patients in a natural position (Fig.5);
it also could evaluate the eyes and lips in a standard position by resizing and cropping images. This would rule out the effect of mood and self-perception on age
evaluation, which is typically visible through facial expressions such as “smiling”
and “frowning” [118].
Osteotomies would be needed in some rhinoplasty cases such as deviated, hunchback noses with nasal bridge and/or a wide base. This technique should be conducted carefully since the osteotomy-related complications can cause permanent
deformities in the patient, however, currently, the technique is entirely dependent on
the surgeon’s tactile sense and hearing capabilities, and there is a lack of the surgeon’s direct vision and access as it is often performed through minimally invasive
subcutaneous approach to limit the remaining scars. A support vector machine
(SVM) classication algorithm was developed and combined with an instrumental
hammer to predict the bone tissue condition around the osteotome tip. SVM is a
machine learning-based algorithm that creates hyperplanes to classify the dataset
into different groups. These hyperplanes are optimized to yield prediction areas so
BF
cd
Application of Articial Intelligence in Diagnosing Oral and Maxillofacial Lesions…
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Fig. 5 Preoperatively, the ranking convolutional neural network software detects the patient as
24years old. Six months postoperatively, this same patient is detected as 22years old. (Reprint
with permission from [118])
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ab
B2F F2 2FB2B
Fig. 6 Schematic illustration of the different groups of impacts on the osteotome (light gray) in
the bone tissue (dark gray) and fractures (white). (a) Bone to bone (B2B), (b) bone to fracture
(B2F), (c) fracture to bone (F2B), (d) fracture to fracture (F2F). The group of each bone-osteotome
system is indicated for each conguration. (Reprint with permission from [116])
that it is possible to separate the points of different classes from a dataset. Here the
impacts were classied into four groups based on the bone-osteotome system (BOS)
state (Fig.6), and the model was applied to predict the BOS status (i.e., the presence
or absence of fracture around the osteotome tip) after each impact. This would help
the surgeon determine the osteotome pathway and force, thus preventing uncontrolled fractures and alerting the arrival to the frontal bone, which should be the end
of the osteotome pathway [116].
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