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Use of Artificial Intelligence in Dentistry: Current Clinical Trends and Research Advances

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Abstract

The field of artificial intelligence (AI) has experienced spectacular development and growth over the past two decades. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be reserved for human experts. When applied to medicine and dentistry, AI has tremendous potential to improve patient care and revolutionize the health care field. In dentistry, AI is being investigated for a variety of purposes, specifically identification of normal and abnormal structures, diagnosis of diseases and prediction of treatment outcomes. This review describes some current and future applications of AI in dentistry.
ISSN: 1488-2159 1 of 7
Published: May 3, 2021
Use of Articial Intelligence in Dentistry:
Current Clinical Trends and Research Advances
Thomas T. Nguyen, DMD, MSc, FRCD(C); Naomie Larrivée; Alicia Lee; Olexa Bilaniuk, BASc MSc;
Robert Durand, DMD, MSc, FRCD(C)
Cite this as: J Can Dent Assoc 2021;87:l7
ABSTRACT
The eld of articial intelligence (AI) has experienced spectacular
development and growth over the past two decades. With
recent progress in digitized data acquisition, machine learning
and computing infrastructure, AI applications are expanding
into areas that were previously thought to be reserved for
human experts. When applied to medicine and dentistry, AI has
tremendous potential to improve patient care and revolutionize
the health care eld. In dentistry, AI is being investigated for a
variety of purposes, specically identication of normal and
abnormal structures, diagnosis of diseases and prediction of
treatment outcomes. This review describes some current and
future applications of AI in dentistry.
ISSN: 1488-2159 1 of 7 J Can Dent Assoc 2021;87:l7
What once seemed like science ction is now
becoming reality in health care. Articial intelligence
(AI) is a fast-moving technology that enables machines
to perform tasks previously exclusive to humans.1 Advances in
AI offer a glimpse of such health care benets as decreasing
postoperative complications, increasing quality of life, improving
decision-making and decreasing the number of unnecessary
procedures.2 When applied to the elds of medicine and dentistry,
AI can play a crucial role in improving diagnosis accuracy and
revolutionizing care. AI is currently used for a variety of purposes
in dentistry: identication of normal and abnormal structures,
diagnosis of diseases and prediction of treatment outcomes.
Furthermore, AI is used extensively in dental laboratories and is
playing a growing role in dental education. The following review
describes current and future applications of AI in the clinical
practice of dentistry.
What Is Articial Intelligence?
AI is a branch of computer science that aims to understand and build
intelligent entities, often instantiated as software programs.3 It can be
dened as a sequence of operations designed to perform a specic
task.4 Historically, articially intelligent systems applied hand-
crafted rules to the specic tasks they were meant to solve. Each
task required domain-specic knowledge, engineering and manual
ne-tuning of the system by subject-matter experts. For instance, a
system designed to detect lesions in medical imaging might look for
abnormally coloured lumps of a given shape. The ne-tunable parts
of the system might be a range of healthy tissue colours or minimum
lengths and widths for a potential lump. Nowadays, medicine most
commonly uses a branch of AI called machine learning5 and, more
recently, deep learning.6
Machine learning (ML) is a branch of AI in which systems learn to
perform intelligent tasks without a priori knowledge or hand-crafted
rules. Instead, the systems identify patterns in examples from a
large dataset, without human assistance. This is accomplished by
dening an objective and optimizing the system’s tunable functions
to reach it. In this process, known as training, an ML algorithm gains
experience through exposure to random examples and gradual
adjustments of the “tunables” toward the correct answer. As a result,
the algorithm identies patterns that it can then apply to new images.
This technique is analogous to an adult showing several photos of
cats to a child. The child eventually learns the patterns involved in
recognizing a cat and identifying one in new images.
Deep learning (DL) is a sub-branch of ML wherein systems attempt
to learn, not only a pattern, but also a hierarchy of composable
patterns that build on each other. The combination and stacking
of patterns create a “deep” system far more powerful than a plain,
“shallow” one. For instance, a child does not recognize a cat in a
single, indivisible step of pattern-matching; rather, the child rst sees
the edges of the object, a particular grouping of which denes a
textured outline with simple shapes, such as eyes and ears. Among
these components, larger groups such as heads and legs arise, and a
particular grouping of these denes the whole cat.
An extremely popular class of DL algorithms is the articial neural
network (ANN), a structure composed of many small communicating
units called neurons organized in layers. A neural network is
composed of an input layer, an output layer and hidden layers in
between.7 It is possible to have 1 or a few hidden layers (shallow
neural network) or multiple/many hidden layers (deep neural
network, DNN) (Figure 1, a and b). These layers are called hidden
because their values are not pre-specied or visible to the outside.
Their aim is to make it possible to build hierarchically on information
retrieved from the visible input layer to compute the correct value of
the visible output layer. The pattern of connections between neurons
denes the particular neural network’s architecture, and the ne-
tunable strengths of those connections are called the weights of the
neural network.
In medicine and dentistry, one of the most commonly used subclasses
of ANN is the convolutional neural network (CNN) (Figure 1c).
A CNN uses a special neuron connection architecture and the
mathematical operation, convolution, to process digital signals such
as sound, image and video. CNNs use a sliding window to scan a
small neighbourhood of inputs at a time, from left to right and top to
bottom, to analyze a wider image or signal. They are extremely well
adapted to the task of image classication and are the most-used
algorithm for image recognition.7
Clinical Application of AI in Dentistry
Radiology
CNNs have shown promising ability to detect and identify anatomical
structures. For example, some have been trained to identify and
label teeth from periapical radiographs. CNNs have demonstrated
a precision rate of 95.8–99.45% in detecting and identifying teeth,
almost rivaling the work of clinical experts, whose precision rate was
99.98%.8,9
CNNs have also been used for the detection and diagnosis of dental
caries.10 In 3000 periapical radiographs of posterior teeth, a deep
CNN algorithm was able to detect carious lesions with an accuracy
of 75.5–93.3% and a sensitivity of 74.5–97.1%. This is a considerable
improvement over diagnosis by clinicians using radiographs alone,
with sensitivity varying from 19% to 94%.11 Deep CNNs have great
potential for improving the sensitivity of dental caries diagnosis
and this, combined with their speed, makes them one of the most
efcient tools used in this domain.
Use of Articial Intelligence in Dentistry:
Current Clinical Trends and Research Advances
J Can Dent Assoc 2021;87:l7
May 3, 2021
J Can Dent Assoc 2021;87:l7 ISSN: 1488-2159 2 of 7
Figure 1: Schematic representation of the architecture of neural networks. Articial neural networks are structures used in machine
learning. They contain many small communicating units called neurons, which are organized in layers. a. Shallow neural networks
are composed of an input layer, a few hidden layers and an output layer. b. Deep neural networks have an input layer, multiple
hidden layers and an output layer. c. Convolutional neural networks use lters to scan a small neighbourhood of inputs.
Orthodontics
ANNs have immense potential to aid in the clinical decision-making
process. In orthodontic treatments, it is essential to plan treatments
carefully to achieve predictable outcomes for patients. However, it is
not uncommon to see teeth extractions included in the orthodontic
treatment plan. Therefore, it is essential to ensure that the best clinical
decision is made before initiating irreversible procedures. An ANN
was used to help determine the need for tooth extraction before
orthodontic therapy in patients with malocclusion.12,13 The four
constructed ANNs, taking into consideration several clinical indices,
showed an accuracy of 80–93% in determining whether extractions
were needed to treat patients’ malocclusions.12,13
Periodontics
According to the 1999 American Academy of Periodontology
classication of periodontal disease, 2 clinical types of periodontitis
are recognized: aggressive (AgP) and chronic (CP) forms.14 Because
of the complex pathogenesis of the disease, no single clinical,
microbiological, histopathological or genetic test or combination
of them can discriminate AgP from CP patients.15 Papantanopoulos
and colleagues16 used an ANN to distinguish between AgP and CP
in patients by using immunologic parameters, such as leukocytes,
interleukins and IgG antibody titers. The one ANN was 90–98%
accurate in classifying patients as AgP or CP. The best overall
prediction was made by an ANN that included monocyte, eosinophil,
Use of Articial Intelligence in Dentistry:
Current Clinical Trends and Research Advances
J Can Dent Assoc 2021;87:l7
May 3, 2021
J Can Dent Assoc 2021;87:l7 ISSN: 1488-2159 3 of 7
neutrophil counts and CD4+/CD8+ T-cell ratio as inputs. The study
concluded that ANNs can be employed for accurate diagnosis of AgP
or CP using relatively simple and conveniently obtained parameters,
such as leukocyte counts in peripheral blood.
Various non-surgical and surgical methods have been devised for the
treatment of periodontally compromised teeth (PCT) and supporting
structures.17 Despite advances in treatment modalities, no signicant
improvement has been made in the method for diagnosing and
predicting the prognosis of PCT. Clinical diagnostic and prognostic
judgement depends heavily on empirical evidence.18 Lee and
coworkers19 evaluated the potential utility and accuracy of deep
CNN algorithms for diagnosing and predicting PCT. Using the CNN
algorithm, the accuracy of PCT diagnosis proved to be 76.7–81.0%,
while the accuracy of predicting the need for extraction was
73.4–82.8%. The noted difference in accuracy seemed to occur
between different types of teeth, with premolars more accurately
diagnosed as PCTs than molars (accuracies were 82.8% and 73.4%,
respectively). This could be explained by the fact that premolars
normally have a single root, whereas molars have 2 or 3 roots, thus
exhibiting a more complex anatomy for a CNN to interpret.
Endodontics
Although mandibular molars tend to have similar root canal
congurations, several atypical variations may occur.20 To minimize
treatment failures related to morphological differences and to
optimize the clinical outcomes of endodontic therapy, cone-beam
computed tomography (CBCT) has become the gold standard.
However, because of its higher dose of radiation compared with
conventional radiographs,21 CBCT is not used systematically. To
overcome such challenges, AI has been introduced to classify
the given data using a CNN22 to determine whether the distal
root of the rst mandibular molar has 1 or more extra canals.
Radiographs of 760 mandibular rst molars taken with dental
CBCT were analyzed. Once the presence or absence of the
atypia was determined, image patches of the roots obtained from
corresponding panoramic radiographs were processed by a deep-
learning algorithm to classify morphology.
Although the CNN had a relatively high accuracy of 86.9%,20 several
limitations exist regarding its clinical integration. The images must be
segmented manually,23 which consumes a considerable amount of
time. Furthermore, the obtained images must be of adequate size and
should focus on a small region to allow the system to concentrate
on the object being studied, while covering enough area to include
pertinent information.24
Oral Pathology
Detection and diagnosis of oral lesions is of crucial importance
in dental practices because early detection signicantly improves
prognosis. As some oral lesions can be precancerous or cancerous
in nature, it is important to make an accurate diagnosis and prescribe
appropriate treatment of the patient. CNN has been shown to be
a promising aid throughout the process of diagnosis of head and
neck cancer lesions. With specicity and accuracy at 78–81.8% and
80–83.3%, respectively (compared with those of specialists, which
were 83.2% and 82.9% respectively), CNN shows great potential for
detecting tumoural tissues in tissue samples or on radiographs.25,26
One study used a CNN algorithm to distinguish between 2 important
maxillary tumours with similar radiologic appearance but different
clinical properties: ameloblastomas and keratocystic odontogenic
tumours.26 The specicity and the accuracy of diagnosis by the
algorithm were 81.8% and 83.3%, respectively, comparable with
those of clinical specialists at 81.1% and 83.2%. However, a more
signicant difference was observed in terms of diagnostic time:
specialists took an average of 23.1 minutes to reach a diagnosis,
while the CNN achieved similar results in 38 s.26
Challenges of AI
The management and sharing of clinical data are major challenges
in the implementation of AI systems in health care. Personal data
from patients are necessary for initial training of AI algorithms, as
well as ongoing training, validation and improvement. Furthermore,
the development of AI will prompt data sharing among different
institutions and, in some cases, across national boundaries. To
integrate AI into clinical operations, systems must be adapted to
protect patient condentiality and privacy.27 Thus, before considering
broader distribution, personal data will have to be anonymized.28
Even with the ability to take these precautions, there is skepticism
in the health care community about secure data sharing.
AI systems are also associated with safety issues. Mechanisms
must be created to control the quality of the algorithms used in
AI. To remedy this situation, the United States Food and Drug
Administration has created a new drug category, “Software as
Medical Device,” through which it regulates safe innovation and
patient safety.29 Ambiguous accountability in the use of AI systems
is another concern. Who will be held responsible for a patient who
faces unintentional consequences resulting from an error or adverse
event caused by the AI technology? Is it the professional’s fault, or
is it the fault of the developer who built the algorithm? Given that
our legal system is based on the fundamental assumption that fault
and crime are ultimately attributable to humans, substituting humans
with autonomous agents raises numerous questions of legal and
ethical order. These issues will continue to represent a considerable
challenge to our legal system for the foreseeable future.
Finally, the transparency of AI algorithms and data is a substantial
issue. The quality of predictions performed by AI systems relies
Use of Articial Intelligence in Dentistry:
Current Clinical Trends and Research Advances
J Can Dent Assoc 2021;87:l7
May 3, 2021
J Can Dent Assoc 2021;87:l7 ISSN: 1488-2159 4 of 7
heavily on the accuracy of annotations and labeling of the
dataset used in training. Poorly labeled data can lead to poor
results.30 Clinic-labeled datasets may be of inconsistent quality,
thus limiting the efcacy of the resultant AI systems. Furthermore,
health care professionals should possess a full understanding of
the decisions and predictions made by an AI system, as well as
the capability to defend them.31 Interpretability of AI technology
is a known problem, and major advances are required before
certain classes of algorithms, such as neural networks, can
make clinical diagnoses or treatment recommendations with full
transparency.29
Conclusions
Although multiple studies have shown potential applications of
AI in dentistry, these systems are far from being able to replace
Dr. Nguyen
is an Assistant Professor, Faculty of Dentistry,
McGill University, Montreal, Quebec.
dental professionals. Rather, the use of AI should be viewed as a
complementary asset, to assist dentists and specialists. It is crucial to
ensure that AI is integrated in a safe and controlled manner to assure
that humans retain the ability to direct treatment and make informed
decisions in dentistry.
The road to successful integration of AI into dentistry will necessitate
training in dental and continuing education, a challenge that most
institutions are not currently prepared for. In addition, AI plays a
critical role in virtual reality (VR) and augmented reality (AR). A
new term, mixed reality, incorporates aspects of generative AI,
VR and AR into computer-superimposed information overlays to
enhance learning and surgical planning.32 As various AI systems for
diverse dental disciplines are being developed and have produced
encouraging preliminary results, a future for AI in the health care
system cannot be discounted. AI systems show promise as a great aid
to oral health professionals.
THE AUTHORS
Ms. Larrivée
is a 4th year dental student, Faculty of
Dental Medicine, Université de Montréal,
Montreal, Quebec.
Ms. Lee
is a 4th year dental student, Faculty of
Dental Medicine, Université de Montréal,
Montreal, Quebec.
Mr. Bilaniuk
is a Research Software Developer, Mila –
Quebec AI Institute, Montreal, Quebec.
Dr. Durand
is an Associate Professor, Faculty of
Dental Medicine, Université de Montréal,
Montreal, Quebec.
Correspondence to: Dr. Thomas T. Nguyen, Assistant Professor,
Division of Periodontics, McGill Faculty of Dentistry, 2001
McGill College Avenue, Montreal, QC, H3A 1G1.
Email: thomas.nguyen@mcgill.ca
Acknowledgement: The authors thank Dr. John Syrbu and Dr.Borys
Bilaniuk for their expertise and contribution to this review.
The authors have no declared nancial interests.
This article has been peer reviewed.
Use of Articial Intelligence in Dentistry:
Current Clinical Trends and Research Advances
J Can Dent Assoc 2021;87:l7
May 3, 2021
J Can Dent Assoc 2021;87:l7 ISSN: 1488-2159 5 of 7
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Use of Articial Intelligence in Dentistry:
Current Clinical Trends and Research Advances
J Can Dent Assoc 2021;87:l7
May 3, 2021
J Can Dent Assoc 2021;87:l7 ISSN: 1488-2159 7 of 7
... Eğitim olarak bilinen bu süreçte, bir makine öğrenimi algoritması, rastgele örneklere maruz kalma ve "ayarlanabilirlerin" doğru cevaba doğru kademeli olarak ayarlanması yoluyla deneyim kazanır. Sonuç olarak, algoritma daha sonra yeni görüntülere uygulayabileceği kalıpları tanımlar (Nguyen et al., 2021). Bilgisayarlar, bir hastaya teşhis koyma sanatını iki geniş teknikle öğrenirakış şemaları ve veri tabanı yaklaşımı. ...
... Derin öğrenme, sistemlerin yalnızca bir kalıp değil, aynı zamanda birbiri üzerine inşa edilen bir oluşturulabilir kalıplar hiyerarşisini de öğrenmeye çalıştığı algoritmalara dayalı makine öğrenmesinin bir alt dalıdır. Desenlerin kombinasyonu ve istiflenmesi, düz, "sığ" bir sistemden çok daha güçlü bir "derin" sistem yaratır (Nguyen et al., 2021). Derin öğrenme sinir ağlarının en az üç gizli katmanı vardır Bunlar, görüntüler gibi karmaşık veri yapılarındaki köşeler, şekiller, kenarlar ve makroskopik desenler gibi özellikleri tanımlamak için özellikle faydalıdır . ...
... Robotik destekli, makine öğrenimi ile oral implant cerrahisi, deneyimli cerrahlar tarafından yapılsa bile manuel serbest prosedüre kıyasla önemli ölçüde daha fazla doğruluk, daha kısa operasyon süresi, hassas yapılar etrafında daha güvenli manipülasyon bildirilmiştir (Nguyen et al., 2021). ...
Book
Full-text available
INSAC WORLD HEALTH SCIENCES
... To understand the current and future clinical AI utilizations in dentistry, we must first comprehend the elemental AI technologies. Figure 2 represents the relationship between the various technologies [6][7][8][9]. AI connotes fundamental technologies including machine learning, artificial neural networks (ANN), and deep learning. AIs are commonly categorized into three types: artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI). ...
... All 22 qualitative variables included five interdisciplinary groups of papers (# 4,5, 6,8,9). ...
... Five interdisciplinary "focus groups" for papers with focus balanced between two classifications (# 4,5,6,8,9).For a better overview of the topic,Table 5presents the ten most impactful AI publications in dentistry according to their total and average citations per year. ...
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Full-text available
This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.
... Eğitim olarak bilinen bu süreçte, bir makine öğrenimi algoritması, rastgele örneklere maruz kalma ve "ayarlanabilirlerin" doğru cevaba doğru kademeli olarak ayarlanması yoluyla deneyim kazanır. Sonuç olarak, algoritma daha sonra yeni görüntülere uygulayabileceği kalıpları tanımlar (Nguyen et al., 2021). Bilgisayarlar, bir hastaya teşhis koyma sanatını iki geniş teknikle öğrenirakış şemaları ve veri tabanı yaklaşımı. ...
... Derin öğrenme, sistemlerin yalnızca bir kalıp değil, aynı zamanda birbiri üzerine inşa edilen bir oluşturulabilir kalıplar hiyerarşisini de öğrenmeye çalıştığı algoritmalara dayalı makine öğrenmesinin bir alt dalıdır. Desenlerin kombinasyonu ve istiflenmesi, düz, "sığ" bir sistemden çok daha güçlü bir "derin" sistem yaratır (Nguyen et al., 2021). Derin öğrenme sinir ağlarının en az üç gizli katmanı vardır Bunlar, görüntüler gibi karmaşık veri yapılarındaki köşeler, şekiller, kenarlar ve makroskopik desenler gibi özellikleri tanımlamak için özellikle faydalıdır . ...
... Robotik destekli, makine öğrenimi ile oral implant cerrahisi, deneyimli cerrahlar tarafından yapılsa bile manuel serbest prosedüre kıyasla önemli ölçüde daha fazla doğruluk, daha kısa operasyon süresi, hassas yapılar etrafında daha güvenli manipülasyon bildirilmiştir (Nguyen et al., 2021). ...
... Eğitim olarak bilinen bu süreçte, bir makine öğrenimi algoritması, rastgele örneklere maruz kalma ve "ayarlanabilirlerin" doğru cevaba doğru kademeli olarak ayarlanması yoluyla deneyim kazanır. Sonuç olarak, algoritma daha sonra yeni görüntülere uygulayabileceği kalıpları tanımlar (Nguyen et al., 2021). Bilgisayarlar, bir hastaya teşhis koyma sanatını iki geniş teknikle öğrenirakış şemaları ve veri tabanı yaklaşımı. ...
... Derin öğrenme, sistemlerin yalnızca bir kalıp değil, aynı zamanda birbiri üzerine inşa edilen bir oluşturulabilir kalıplar hiyerarşisini de öğrenmeye çalıştığı algoritmalara dayalı makine öğrenmesinin bir alt dalıdır. Desenlerin kombinasyonu ve istiflenmesi, düz, "sığ" bir sistemden çok daha güçlü bir "derin" sistem yaratır (Nguyen et al., 2021). Derin öğrenme sinir ağlarının en az üç gizli katmanı vardır Bunlar, görüntüler gibi karmaşık veri yapılarındaki köşeler, şekiller, kenarlar ve makroskopik desenler gibi özellikleri tanımlamak için özellikle faydalıdır . ...
... Robotik destekli, makine öğrenimi ile oral implant cerrahisi, deneyimli cerrahlar tarafından yapılsa bile manuel serbest prosedüre kıyasla önemli ölçüde daha fazla doğruluk, daha kısa operasyon süresi, hassas yapılar etrafında daha güvenli manipülasyon bildirilmiştir (Nguyen et al., 2021). ...
... This technique is analogous to an adult showing several photos of cats to a child. The child eventually learns the patterns involved in recognizing a cat and identifying one in new images 13 . ...
... For instance, a child does not recognize a cat in a single, indivisible step of pattern-matching; rather, the child first sees the edges of the object, a particular grouping of which defines a textured outline with simple shapes, such as eyes and ears. Among these components, larger groups such as heads and legs arise, and a particular grouping of these defines the whole cat 13 . ...
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Training Tomorrows Dentists with Artificial Intelligence
... They have an increased potential for enhancing the sensitivity of dental caries diagnosis, which, combined with their speed, makes them one of the most effective tool in this domain. [13][14][15][16] Machine Learning (ML) algorithms can detect an abnormal or normal lymph node in a head and neck image if interpreted by a trained Radiologist who has analysed thousands of such images labelled as normal or abnormal. 17 ...
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Today artificial intelligence (AI) is becoming increasingly important in the healthcare industry. It can be useful in many areas where humans can be aided by new technologies. The concept of "artificial intelligence" (AI) refers to machines being capable of performing human tasks. Machine learning (ML) is an AI subdomain that "learns" intrinsic statistical patterns in data to ultimately make predictions on unseen data. Deep learning is a machine learning technique that employs multi-layer mathematical operations to learn and infer on complex data such as imagery. With the substantial growth in documented information and patient data, intelligent data computation software has become a requirement. Artificial intelligence has a wide range of applications in medicine and dentistry, from data processing and information retrieval to the use of neural networks for diagnosis and the incorporation of augmented reality and virtual reality into dental education. Artificial intelligence (AI) is being studied in dentistry for a variety of purposes, including the identification of normal and anomalous structures, disease diagnosis, and treatment outcome prediction. This review looks at some current and future applications of AI in dentistry. We are ushering in a modern age, and AI is undeniably the future of dental practise management.
... For example, AI has been used to discriminate benign from malignant skin lesions in photographs [32] and detect gastric cancer lesions in endoscopic images [33]. AI has various potential applications in dentistry [34]. Furthermore, AI has already been shown to be capable of identifying teeth and detecting carious lesions in radiographs [35,36]. ...
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Purpose of Review Natural disasters occur frequently in Japan. A disaster medical system was rapidly developed in Japan following the Great Hanshin-Awaji Earthquake in 1995. Dentistry has become increasingly important in disaster medicine. This review summarizes the roles of dental professionals in disaster medicine, highlights relevant issues, and identifies new directions for research to improve disaster relief activities based on our previous experiences as dental professionals supporting the victims of major disasters. Recent Findings Many preventable deaths after a disaster are caused by aspiration pneumonia, which occurs against a background of factors that are compounded by a harsh living environment. An important aim of dental care in disaster medicine is to prevent these disaster-related deaths in vulnerable persons such as the elderly. This can be achieved through interventions to maintain oral hygiene, preserve and enhance oral function (i.e., chewing and swallowing), and improve the diet, since these interventions help to prevent the development of malnutrition and frailty in vulnerable people. Dental identification of disaster victims could be improved through the use of intraoral three-dimensional scanners and artificial intelligence to automate the acquisition of dental findings and through the construction of a national database of digitized dental records. Advances in personal identification methods will be needed given the prediction that a catastrophic earthquake will occur on the Nankai Trough during the next 30 years and claim more victims than the 2011 Great East Japan Earthquake. Summary Disaster-related deaths due to aspiration pneumonia can be prevented by providing appropriate dental care to those in need. The process of identifying victims could be made more efficient through the use of intraoral three-dimensional scanning, artificial intelligence, and a digital database of dental records. Establishing and strengthening relationships between professionals in different regions will help to optimize the multidisciplinary response to future large-scale disasters.
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Artificial intelligence (AI) has remarkably increased its presence and significance in a wide range of sectors, including dentistry. It can mimic the intelligence of humans to undertake complex predictions and decision-making in the healthcare sector, particularly in endodontics. The models of AI, such as convolutional neural networks and/or artificial neural networks, have shown a variety of applications in endodontics, including studying the anatomy of the root canal system, forecasting the viability of stem cells of the dental pulp, measuring working lengths, pinpointing root fractures and periapical lesions and forecasting the success of retreatment procedures. Future applications of this technology were considered in relation to scheduling, patient care, drug-drug interactions, prognostic diagnosis, and robotic endodontic surgery. In endodontics, in terms of disease detection, evaluation, and prediction, AI has demonstrated accuracy and precision. AI can aid in the advancement of endodontic diagnosis and therapy, which can enhance endodontic treatment results. However, before incorporating AI models into routine clinical operations, it is still important to further certify the cost-effectiveness, dependability, and applicability of these models.
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This study intended to evaluate the effects of inorganic trace elements such as magnesium (Mg), strontium (Sr), and zinc (Zn) on root canal dentin using an Artificial Neural Network (ANN). The authors obtained three hundred extracted human premolars from type II diabetic individuals and divided them into three groups according to the solutions used (Mg, Sr, or Zn). The authors subdivided the specimens for each experimental group into five subgroups according to the duration for which the authors soaked the teeth in the solution: 0 (control group), 1, 2, 5, and 10 min (n = 20). The authors then tested the specimens for root fracture resistance (RFR), surface microhardness (SμH), and tubular density (TD). The authors used the data obtained from half of the specimens in each subgroup (10 specimens) for the training of ANN. The authors then used the trained ANN to evaluate the remaining data. The authors analyzed the data by Kolmogorov–Smirnov, one-way ANOVA, post hoc Tukey, and linear regression analysis (P < 0.05). Treatment with Mg, Sr, and Zn significantly increased the values of RFR and SμH (P < 0.05), and decreased the values of TD in dentin specimens (P < 0.05). The authors did not notice any significant differences between evaluations by manual or ANN methods (P > 0.05). The authors concluded that Mg, Sr, and Zn may improve the RFR and SμH, and decrease the TD of root canal dentin in diabetic individuals. ANN may be used as a reliable method to evaluate the physical properties of dentin.
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A review of over 4000+ articles published in 2021 related to artificial intelligence in healthcare.A BrainX Community exclusive, annual publication which has trends, specialist editorials and categorized references readily available to provide insights into related 2021 publications. Cite as: Mathur P, Mishra S, Awasthi R, Cywinski J, et al. (2022). Artificial Intelligence in Healthcare: 2021 Year in Review. DOI: 10.13140/RG.2.2.25350.24645/1
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Neural Networks or Artificial Neural Networks is a nonlinear machine learning algorithm which is inspired by the most advanced learning machine, the human brain. Unlike Conventional computers, Neural networks find out how to solve the problem by itself. It tries to solve the problems the same way a human brain does. Its basic architecture is similar to that of biological neurons. Neural Networks are trained by example data.
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Background: The field of implant dentistry education is rapidly evolving as new technologies permit innovative methods to teach the fundamentals of implant dentistry. Methods: Literature from the fields of active learning, blended learning, augmented reality, artificial intelligence, haptics, and mixed reality were reviewed and combined with the experience and opinions of expert authors. Both positive and negative aspects of the learning methods are presented. Results and conclusion: The fundamental objectives of teaching and learning remain unchanged, yet the opportunities to reach larger audiences and integrate their learning into active experiences are evolving due to the introduction of new teaching and learning methodologies. The ability to reach a global audience has never been more apparent. Nevertheless, as much as new technology can be alluring, each new method comes with unique limitations.
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Objectives Ameloblastomas and keratocystic odontogenic tumors (KCOTs) are important odontogenic tumors of the jaw. While their radiological findings are similar, the behaviors of these two types of tumors are different. Precise preoperative diagnosis of these tumors can help oral and maxillofacial surgeons plan appropriate treatment. In this study, we created a convolutional neural network (CNN) for the detection of ameloblastomas and KCOTs. Methods Five hundred digital panoramic images of ameloblastomas and KCOTs were retrospectively collected from a hospital information system, whose patient information could not be identified, and preprocessed by inverse logarithm and histogram equalization. To overcome the imbalance of data entry, we focused our study on 2 tumors with equal distributions of input data. We implemented a transfer learning strategy to overcome the problem of limited patient data. Transfer learning used a 16-layer CNN (VGG-16) of the large sample dataset and was refined with our secondary training dataset comprising 400 images. A separate test dataset comprising 100 images was evaluated to compare the performance of CNN with diagnosis results produced by oral and maxillofacial specialists. Results The sensitivity, specificity, accuracy, and diagnostic time were 81.8%, 83.3%, 83.0%, and 38 seconds, respectively, for the CNN. These values for the oral and maxillofacial specialist were 81.1%, 83.2%, 82.9%, and 23.1 minutes, respectively. Conclusions Ameloblastomas and KCOTs could be detected based on digital panoramic radiographic images using CNN with accuracy comparable to that of manual diagnosis by oral maxillofacial specialists. These results demonstrate that CNN may aid in screening for ameloblastomas and KCOTs in a substantially shorter time.
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Objectives: Deep convolutional neural networks (CNNs) are a rapidly emerging new area of medical research, and have yielded impressive results in diagnosis and prediction in the fields of radiology and pathology. The aim of the current study was to evaluate the efficacy of deep CNN algorithms for detection and diagnosis of dental caries on periapical radiographs. Materials and methods: A total of 3000 periapical radiographic images were divided into a training and validation dataset (n = 2400 [80%]) and a test dataset (n = 600 [20%]). A pre-trained GoogLeNet Inception v3 CNN network was used for preprocessing and transfer learning. The diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, and area under the curve (AUC) were calculated for detection and diagnostic performance of the deep CNN algorithm. Results: The diagnostic accuracies of premolar, molar, and both premolar and molar models were 89.0% (80.4-93.3), 88.0% (79.2-93.1), and 82.0% (75.5-87.1), respectively. The deep CNN algorithm achieved an AUC of 0.917 (95% CI 0.860-0.975) on premolar, an AUC of 0.890 (95% CI 0.819-0.961) on molar, and an AUC of 0.845 (95% CI 0.790-0.901) on both premolar and molar models. The premolar model provided the best AUC, which was significantly greater than those for other models (P < 0.001). Conclusions: This study highlighted the potential utility of deep CNN architecture for the detection and diagnosis of dental caries. A deep CNN algorithm provided considerably good performance in detecting dental caries in periapical radiographs. CLINICAL SIGNIfiCANCE: Deep CNN algorithms are expected to be among the most effective and efficient methods for diagnosing dental caries.
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We need to consider the ethical challenges inherent in implementing machine learning in health care if its benefits are to be realized. Some of these challenges are straightforward, whereas others have less obvious risks but raise broader ethical concerns.