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hypertrophy, biventricular hypertrophy, anterior myocardial infarction, and inferior
myocardial infarction [49]. A fully CNN algorithm was applied directly on ECG
data without preprocessing to detect myocardial infarction and investigate its decision criteria. The trained algorithm showed a human-level performance by 93%
sensitivity and 90% specicity. The same signs that human cardiologists used as MI
indicators were recognized in the network’s decisions. It was shown with this,
despite the need for continued testing and renement of such programs, the ability
to read ECGs using standard computer programs can facilitate obtaining more consistent and uniform interpretations [50]. In 2020, as the next step in algorithmic
ECG interpretation, BeatLogic platform provides beat detection, beat classication,
and rhythm detection/classication with much more accuracy than current
approaches, whereas the previously published algorithms could process just one of
these three [51]. Also, it was shown that AI-enabled ECG acquired during sinus
rhythm assists in identifying patients with atrial brillation bedside testing [52].
AI might also be helpful in early diagnosis of cardiac rhythm disorders; it was
proved that DL long short-term memory (LSTM) model with more than 0.90 accuracy and ultrafast rhythm classication is more effective in interpreting 12 common
heart rhythms in comparison to emergency physicians, internists, and cardiologists [53].
P. Motie et al.
2.5 AI inDermatology
The application of AI in clinical dermatologic visits can enhance the clinician’s
diagnostic ability. This technology has the potential to help the diagnosis of skin
disorders out of photographs [54]. The DL algorithm can be used for malignancy
diagnosis, treatment suggestion, and classication of 134 diseases based on clinical
photographs of skin lesions with a performance comparable to that of the experts
[55]. In detections of skin cancers such as melanoma and non-melanoma skin cancers in which early diagnosis plays a critical role in treatment and prognosis, AI can
perform as well as or even better than trained dermatologists [56]. AI also has the
potential in diagnosis and treatment suggestion of psoriasis [57–59], atopic dermatitis [60], and onychomycosis [61, 62], distinguishing between benign nevi vs. melanoma [63, 64], ulcer assessment [65, 66], and acne severity evaluation [67].
Although AI is still far from human-level capabilities in judgment, interpretation,
and face-to-face communications, its use in clinical situations is ready to be tested
since AI can lever up the clinician’s judgment and diagnostic abilities. It is fast and
can supply additional services such as lesion comparison or differential diagnosis
suggestions. It also has the potential of automatic diagnosis of simple skin lesions,
so the referrals to dermatologists could be limited to the complex and advanced
cases [68].
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3 AI inDentistry andOral andMaxillofacial Surgery
Dental clinicians can also take advantage of AI potentials in various elds. In orthodontics, the BN (Bayesian network) was compared with expert orthodontists and it
was highly accurate in determining whether a patient needed orthodontic treatment
or not [69]. AI also showed promising cephalometry landmark detection, skeletal
classication, and tooth extraction decision [70]. In periodontics, support vector
machine (SVM) classiers were able to distinguish between periodontal health,
generalized aggressive periodontitis, and generalized chronic periodontitis in young
individuals using a panel of 40 bacterial species [71]. DL algorithms could also
identify the strongest tooth mobility predictors, such as aging, general health, soda
intake, ossing behavior, and nancial stress [72]. An increasing body of literature
shows that machine learning models perform well for diagnostic and prognostic
analyses in studies of oral cancer [73]. And in endodontics, an articial neural network determined the apical foramen location more accurately than endodontists [74].
3.1 Implementation ofAI fortheDiagnosis ofMaxillofacial
Pathologic Conditions
Accurate differential diagnoses of disease are crucial because these rst diagnoses
gure out the treatment plan. Today, we see outstanding improvements in AI performance in diagnosing and classifying pathologic conditions. It has shown a diagnostic accuracy level equal to or greater than specialists [75] and could aid the residents
or inexperienced dentists in formulating more precise diagnoses. Using AI also
brings the opportunity of detecting malignant neoplasms and other pathologic
lesions in early stages before even they become detectible by experienced specialists. This early detection provides a better prognosis and decreases patients’ treatment costs and loads, since dramatically less time is sacriced [76].
3.1.1 Cystic Lesions
Cystic lesions are pathological cavities having luminal uid or semiuid components. Cysts can be seen in images as clearly delineated areas with different contrast
from the surrounding tissues. These cavities could be covered fully or partially by
epithelium [77]. As the type and the biologic nature of the epithelium determine the
prognosis and the potential for recurrence of cysts, the classication of the cystic
lesions is based on the origin of this epithelium; odontogenic cysts develop from the
odontogenic process, and non-odontogenic cysts arise from head and neck embryogenesis [78].
Radiologically, the characteristics of odontogenic cysts and tumors appear only
once they reach a certain volume. Also, initial radiological appearances of cysts and
tumors are so indistinguishable from each other that even by experienced specialists
their proper differentiation is hard to reach. They are also asymptomatic during their
early stages of progression, and most of them are found accidentally in panoramic
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P. Motie et al.
images [76]. Automatic lesion detection based on either plain radiographs or threedimensional image modalities using deep CNN has snowballed in recent years,
attracting substantial public attention [76, 77, 79].
Yang etal. did a comparative study among three groups of YOLO architecture,
oral and maxillofacial surgeons, and general clinicians to detect and diagnose odontogenic cysts and tumors, including dentigerous cysts, odontogenic keratocyst, and
ameloblastoma on panoramic images. YOLO achieved at least similar correct results
than oral and maxillofacial specialists and signicantly higher than general practitioners. They showed the usefulness of CNN in specic pathology detection and
showed that CNN could be a great assistant for inexperienced dentists and other
specialists who are not oral and maxillofacial surgeons. Also, in cases with low
clinical signs and symptoms, especially at early stages, double-checking diagnoses
with YOLO would prevent the surgeon from misdetection or unnecessary surgical
intervention [76].
Odontogenic keratocysts (OKC) and ameloblastoma are both clinically prevalent
benign radiolucent lesions of the jaws. Due to their different behavior, the correct
differential diagnosis of these lesions is crucial because it has a direct impact on
surgical planning. Accurately distinguishing between these two lesions by panoramic radiography is usually difcult; therefore histopathological ndings are now
the gold standard for differential diagnosis [80]. A trained deep CNN model to differentiate ameloblastoma and OKCs on panoramic radiographs achieved high accuracy, sensitivity, and specicity, demonstrating that the CNN algorithm can provide
a reliable recommendation for oral and maxillofacial specialists without the need
for incisional or excisional biopsy [81].
Comparing various lesions, detection and classication of radicular cysts by
deep CNN architecture showed the highest recall and accuracy (than OKCs, dentigerous cysts, etc.). The reason may be that radicular cysts are more frequent among
other cysts; they form the main percentage of datasets used for the learning process
that might have favored their detection, and also radicular cysts mainly occur around
the root apexes, which makes it easier to differentiate them from nasopalatine duct
cysts and dentigerous cysts [77, 82]. In contrast, OKCs had the lowest recall and
accuracy. It might happen because rather mandibular OKCs tend to grow in posterior regions; maxillary OKCs mainly extend in the maxillary sinus area, so their
proper detection is usually hard to reach. Ariji etal. conducted a study to detect
radiolucent lesions in the mandible and classify them into four groups (ameloblastomas, OKCs, dentigerous cysts, and radicular cysts). The model could correctly
detect OKCs (100%), but their classication sensitivity was very low (13%) [79]. In
this study, the performance of CNN in the detection of cyst-like lesions in the maxilla and mandible was also compared. Mandible cyst-like lesions were better
detected with higher accuracy and sensitivity due to the high contrast between the
radiolucent lesions and the surrounding bony structures of the mandible, while in
the maxilla, radiolucency of air-containing structures and the hard palate and inferior nasal concha superimpositions decreases which makes the identication of
lesions more difcult [79, 82].
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CNN-based
system has high
performance in the
identication of
maxillary radicular
cysts
Classication
sensitivity: test
1=0.949, test
2=1.00
Accuracy/sensitivity/
No. of
images/
Deep CNN
architecture
achieved better
results when it’s
Outcomes
For panoramic images
(AUC=0.847,
specicity
CBCT and
panoramic
2126 images
(1140
photographs Modality
trained with CBCT
sensitivity=0.882,
specicity=0.77)
For CBCT images
images
panoramic
+986
CBCT)
images than that
trained with
(AUC=0.914,
sensitivity=0.961,
high accuracy in
panoramic image
Sensitivity=0.88 Deep CNN has
specicity=0.771)
radiographs
210 Panoramic
detecting
mandibular lesions
Detection sensitivity:
test 1=0.746, test
2=0.771
Panoramic
radiographs
1000 epochs
(410
patients)
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(continued)
Hard tissue/
odontogenic
cystic lesions—
odontogenic
keratocysts,
Deep neural
network for
detecting and
classifying cystic
The goal of the
study Detected lesion
Deep
Algorithm
Author,
and
learning task
Google Net Detection
architecture
J.Lee
etal.
year
lesions
classication
2019
cysts
Hard tissue/
Deep CNN
DetectNet Detection
Ariji etal.
dentigerous
ameloblastoma,
odontogenic
keratocysts,
dentigerous
cysts, radicular
algorithm for
automatic
detection and
classication of
radiolucent
and
classication
2019
cysts, and
lesions of the
Hard tissue/
simple bone
cysts
Deep neural
mandible
DetectNet Detection
Watanabe
cyst-like lesions
(cyst or benign
tumors arising
network for
detecting and
showing
and
classication
etal.
2020
in the maxilla)
especially
radicular cysts
maxillary
cyst-like lesions
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Outcomes
Accuracy/sensitivity/
specicity
CNN shows
excellent
performance in
differentiation of
ameloblastoma
Accuracy=90.36%,
sensitivity=92.88%,
specicity=87.80%,
AUC=0.946
and odontogenic
keratocyst
performance in
detecting and
Precision=0.707 YOLO has a great
Recall=0.680
classifying lesions
greater than or
equal to OMS
specialist
P. Motie et al.
No. of
images/
photographs Modality
420 Panoramic
Hard tissue/
The goal of the
study Detected lesion
CNN for
Deep
learning task
Detection
Algorithm
architecture
VGG19,
radiographs
ameloblastoma
and odontogenic
keratocyst
differentiation of
the
ameloblastoma
and odontogenic
keratocyst
and
classication
ResNet-50
radiographs
1603 Panoramic
Hard tissue/
dentigerous
cysts,
odontogenic
keratocyst,
A deep learning
algorithm for
detecting and
classifying
lesions at the
and
classication
YOLO Detection
ameloblastoma
same time and
comparative
study between
machine and
clinician
performance
Author,
year
Liu etal.
2021
Yang
etal.
2020
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3.1.2 Cancerous Lesions
Oral cancer is a general term covering cancers of the tongue, gingiva, palate, buccal
mucosa, mouth oor, and lips. Opportunistic screening, particularly for high-risk
groups, might be benecial in detecting oral cancers at an earlier stage. However,
conventional visual and tactile oral examinations are unable to distinguish benign
from malignant or potentially malignant lesions. Jubair etal. designed a study in
which a deep CNN model was trained by clinical photographs to classify the oral
lesions into either benign, malignant, or potentially malignant. The clinical images
were gathered from various digital cameras and smartphones, resulting in variable
sizes, zoom magnications, angles, light conditions, and picture orientations. So,
they manually cropped and resized the images into a similar format. They indicated
that CNN could be successfully used to detect cancerous or potentially malignant
lesions. Its detection accuracy is (85%) near to specialist practitioners (90%) and
signicantly higher than general dental practitioners (75%) [83].
The most common malignancy in oral and maxillofacial regions is squamous cell
carcinoma (SCC), a form of cancer that arises from epithelial tissues of the skin or
mucous membranes. Early diagnosis of SCC can reduce its mortality and morbidity.
Still, most cases are diagnosed at an end stage which crucially decreases the survival
rate after curative therapies. This late detection of SCC could happen due to a lack of
familiarity among healthcare professionals and effective and reliable early diagnostic
methods [83]. Today, the gold standard for diagnosing SCC is biopsy and histopathological conrmation. The unsupervised learning ability of CNN models brings an
opportunity to learn from label-free histopathology images. It can extract image patterns that are not tangible to the human eye. Digital pathology is a new imaging
technique to improve pictures’ robustness through certain operations and make tissue
analysis more efcient. Zhang H etal. maneuvered CNN models trained with these
digitally enhanced histopathology images. The study’s primary attention was to signify the contrast between the nucleus and cytoplasm to make the morphological features of the cell much bolder; therefore, segmentation of the nucleus and malignancy
recognition became more efcient. They also omitted the images’ noise by setting a
threshold through mathematical calculations. The trained CNN model by these
images had a higher sensitivity score than pathologists. Therefore, the CNN model
has the potential to aid the specialist in screening at-risk population groups [84].
Confocal laser endomicroscopy (CLE) is another imaging modality that recently has
been used for noninvasive SCC diagnosis. Previously, it has been widely used to
diagnose gastrointestinal tract pathologic conditions. In comparison to bright light
endomicroscopy, CLE provides a better depth (around 100μm below the surface).
Because CLE is unlikely to other imaging techniques, it requires an additional training program for the pathologist or surgeons for reliable analyzing; AI can reduce this
burden load, help pathologists, and save the energy and time needed to train specialists. Utilizing a CNN model trained by CLE images can bring the opportunity of
automatic real-time classication, make accurate diagnoses directly on the high-risk
sites, and determine the outline of the cancerous lesion synchronous with the operation treatment [85]. Stimulated Raman scattering (SRS) microscopy is also a recent
imaging modality providing a chemical map of the tissue section. This microscopy
technique does not need any tissue processing, xation, or H&E staining and can be
used to analyze fresh samples directly. It takes molecular specicity data from
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P. Motie et al.
samples and can diversify proteins, lipids, and nucleic acids. The contribution of SRS
histology images and deep learning-based architectures can provide immediate, reliable diagnostic data used in tumor surgeries. Zhang L etal. trained a CNN model
with SRS images from normal and neoplastic specimens. The model yielded outstanding performance with 100% accuracy in SCC identication [86].
Nasopharyngeal carcinoma (NPC) is a head and neck malignant tumor originating
from the nasopharynx epithelium. It’s been more frequently reported in the south of
China and southern Asia [87]. The tumor stage of NPC is gured out by its anatomical
extension. A large body of research has reported that tumor volume and extension are
also a dominant prognostic factor. In the currently available methods of volume estimation margins for treatment planning and follow-up evaluations, tumor borders in a
CT or MR image must be manually drawn by a radiologist in a “slice-by-slice”
approach [88]. NPC has large variations in size and shape and its growth pattern is
inltrative. These tumors have inhomogeneous intensities and similar intensity to the
surrounding tissues. Also, they are usually found in complex anatomical areas, which
makes their segmentation a challenging, time- consuming task. This complexity can
increase trustless and inaccurate segmentation. Although semiautomated segmentation techniques have been developed, they still take time to process large datasets. To
reduce time, workload, and human errors, automatic image segmentation is an ideal
solution [89]. Until now, several methods of automatic NPC segmentation have been
introduced, which can generally be categorized into three main groups: intensitybased, shape-based, and statistical approaches [88]. Tatanun etal. conducted a study
that used a framework based on a region-growing method for NPC segmentation in
CT images. In this technique, automatic seed generation is done using a probabilistic
map that denes a tumor cell probability. Prior knowledge of tumor location, intensity,
and non-tumor regions is used to make these maps; pixel with the highest probability
is set as the potential seed. Afterward, the nearby seeds are examined to see if they are
tumor cells. This process will be continued until no new seed is found [89].
As was mentioned previously, NPCs have inhomogeneous structures, and their
inltrative growth pattern makes an indistinguishable tumor-normal tissue interface
on medical images. Consequently, linear and shape-based image segmentation is
not usually an effective technique in these cases.
Regarding statistical approaches, Zhou etal. used two-class support vector machines
(SVM) to segment NPCs on MRI images. In this approach, rst, tumor and non-tumor
areas (the classes) are dened in the model’s training process. After feature extraction,
the SVM-based method can “learn” and map the actual data distribution and nally
generate an optimal exible separation plane across the two pre-dened classes [90].
Ma etal. created a deep CNN and graph cut method to classify the patches for
NPC segmentation. They used 3D structural information and trained the CNN individually for each sagittal, coronal, and axial view. The model then used this 3D
context information to perform the segmentation in the rst stage. Afterward the
graph cut framework was applied to rene the segmentation results. It was concluded
that the multi-view CNN method has an acceptable segmentation performance and
excellent stability and reliability [88]; however, segmentation by patches requires
extensive training time and only uses the information of small local regions. A U-net
model was then improved to overcome this problem, which is an end-to-end network
mainly used for image segmentation. It used the whole image as NPC segmentation
input instead of extracting patches increasing the segmentation efciency [91, 92].
Application of Articial Intelligence in Diagnosing Oral and Maxillofacial Lesions…
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with CNN has
great
performance in
SCC
identication
CNN is an
effective tool for
OSCC diagnosis
CNN can
Specicity: 77%;
sensitivity: 80%
images
enhance the
quality and
specicity: 84.5%,
sensitivity: 86.7%,
Accuracy/
Outcomes
sensitivity/
specicity
high accuracy in
detecting and
differentiating
ameloblastoma
VGG19 Deep CNN has
Accuracy:87.50,
VGG16
Accuracy: 83.33
radiographs
and complex
odontoma
CNN has a high
AUC: 0.96,
Laser
performance in
detection of
OSCC from
CLE images
accuracy: 88.3%,
sensitivity: 86.6%,
specicity: 90%
endomicroscopy
images
AUC: 0.95 SRS afliated
Accuracy: 100%
scattering
microscopy
images
of oral
cancer
screening and
early detection
AUC=0.928
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(continued)
No. of
images/
photographs
The goal of
Deep learning
Algorithm
116 images Panoramic
for testing Modality
Hard tissue/
ameloblastoma,
complex
odontoma
CNN for
showing jaw
tumors
the study Detected lesion
classication
task
VGG16, VGG19 Segmentation
architecture
12 patients,
11,000
images
(extracted
from 116
Hard tissue/oral
squamous cell
carcinoma
(OSCC)
detection of
OSCC
LeNet-5 Classication CNN for
78 patients Stimulated Raman
video
sequences)
Laryngeal
squamous cell
CNN based
SRC
classication
ResNet Detection
carcinoma
microscopy for
automatic
detection of
175 images Histopathological
Hard tissue/oral
squamous cell
carcinoma
SCC
CNN for
recognition
OSCC cancer
Segmentation
classication
VGG16, VGG19,
InceptionV3,
InceptionResNetV2,
716 images Clinical images Accuracy: 85.0%,
(OSCC)
Soft tissue/
Malignant or
potentially
CNN for early
detection of
cells
and Xception
suspicious oral
Detection
classication
EfcientNet-B0,
VGG19, ResNet101
malignant tongue
lesions, SCC,
oral epithelial
dysplasia, benign
tumors
lesions
Ismael
etal./2020
Author/year
Aubreville
etal./2017
Zhang
etal./2019
Zhang
etal./2021
Jubair
etal./2020
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Accuracy/
Outcomes
sensitivity/
specicity
DA-DSU net
has an exact
performance in
segmentation of
0.8026
CR=0.7065,
ASSD=0.8021
NPC tumors of
head and neck
graph cut
method in a
CR: 0.77
two-stage
manner are
effective in
fully-automated
NPC
segmentation
DEU has
correct and
sensitivity:0.642,
stable
performance in
NPC automatic
segmentation
precision:0.654,
T2W: DSC:0.642,
sensitivity:0.654,
precision:0.688,
T1W+T2W:
DSC:0.721,
sensitivity:0.712,
precision:0.768
P. Motie et al.
No. of
images/
95 patients MRI DSC: 0.8050, PM:
photographs
for testing Modality
Soft tissue/
nasopharyngeal
The goal of
Deep learning
Algorithm
automatic
the study Detected lesion
task
DA-DSUnet Segmentation CNN for
architecture
carcinoma
NPC
segmentation
30 patients MRI PM: 85.93 CNN and a 3D
Soft tissue /
nasopharyngeal
carcinoma
segmentation
of NPC tumors
Segmentation CNN for
AlexNet like CNN's
architectures
44 patients MRI T1W: DSC:0.620,
Soft tissue /
nasopharyngeal
carcinoma
automated
NPC
segmentation
Segmentation CNN for fully
Developed DEU
architecture
Tang
etal./2021
Author/year
Ma
etal./2018
Ye
etal./2020
DSC Dice similarity coefcient equals twice the number of pixels common to both sets divided by the sum of the number of pixels in each set, which shows
the mutual overlap between ground truth and predicted segmentation
PM percent match, the ratio of true positive (TP) to the number of tumor pixels in the ground truth
CR correspondence ratio
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3.1.3 Bone andJoint Pathology
Ameloblastoma and complex odontoma both occur in similar maxillofacial areas.
They are benign tumors that typically have painless signs and symptoms and are
accidentally found in panoramic images. Unlike complex odontoma, ameloblastoma is locally aggressive; therefore, their correct differentiation is essential. Ismael
etal. trained two models of CNN architecture (VGG19, VGG16) to classify ameloblastoma and complex odontoma. Both models achieved high and accurate performance in detection and classication tasks but VGG19 shows better results in cases
with limited training data [93].
Osteoarthritis (OA) is the most common arthritis in temporomandibular joints.
Temporomandibular joint osteoarthritis is a condition in which internal surfaces of
the joint undergo pathological degenerative changes, and the subchondral cortical
layer leads to loss. These changes occur in response to mechanical overloads and
cause pain and noise in TMJ.These structural bone changes are viewable in panoramic radiographs; therefore, these radiographic images can be used as an additional examining tool besides clinical evaluation [94]. Kim et al. trained CNN
architectures to diagnose TMJ OA.They train three CNN models: Model 1 detects
the TMJ, joint fossa, and condyle. Model 2 classies the selected anatomical region
into normal or abnormal. Model 3 determines whether abnormal TMJ detected by
model 2 is OA or not [94]. The purpose of multi-models was to divide pictures into
left side and right side because the symmetry of condyles is a vital criterion guring
out TMJ conditions. At last, they achieved high performance in detecting TMJ
osteoarthritis and implemented the CNN model in an expert system program for
pre-screening panoramic radiographs from 20 local dental clinics in South
Korea [94].
3.1.4 Developmental Abnormalities
Cleft lip and palate (CLP) are one of the most common types of congenital maxillofacial lesions [95]. The primary palate originates from the median palatine process in the weeks of 4–7 of the gestational periods. The lip, alveolus, and hard palate
anterior to the incisive foramen have the same developmental origin. Any interference in development of palates can result in primary palate cleft. Alveolar clefts
(CAs) are categorized as primary palate clefts (CPs). There is a close relation
between cleft lip, cleft palate, and alveolar clefts. The DL systems’ detection function was used to design a model to detect and classify cleft alveolus into CA only
and CA with CP groups based on panoramic radiographies. The dataset was categorized as lip cleft with or without palate cleft and normal group. Rectangular regions
of interest were manually annotated. The inferior line of the piriform aperture span
at the level of the healthy side was selected as the superior margin, and the lower
margin was located at the alveolar ridge. The alveolar ridge between the central
incisors and the most distal portion of the piriform aperture were set as the medial
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