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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.
“Look, Dave, I can see you’re upset about this,” said HAL 9000 a ctional computer with a human personality. In 1968 when Kubrick produced “2001: A Space
Odyssey” movie, our daily life was not involved with articial intelligence (AI), and
it was just a fancy idea. The movie “her” smartly attempted to illustrate the future of
AI in our lives. In the movie, Samantha is a self-aware virtual assistant who could
undertake complex tasks, is more human-like, and has the power of understanding
human behavior, which makes it an ideal girlfriend and satisfying companion out of
an AI operation system.
Our modern world has recently been embraced by the fourth industrial revolution, which is described by Klaus Schwab as “a fusion of technologies that has
blurred the lines between the physical, digital, and biological spheres.” It is fast and
vast and will cover whole aspects of our lives sooner than expected. If you ask what
this revolution is precisely doing with our world, the answer would be that it is
injecting technology into all products and services of the real world. A clear example of such integration is the entrance of AI into the eld of medicine and dentistry.
The My Invisalign app is an AI-based patient-supporting application, guiding the
patients in every step of their Invisalign journey, from deciding to start treatment to
switching to their last aligner in treatment. Using this app, patients would be able to
nd nearby Invisalign practitioners and arrange an appointment. The Invisalign
SmileView technology allows the patients to visualize how their smile could look
after Invisalign treatment just by uploading or taking a sele. The app also provides
patients with educative care tips to keep them using their clear Invisalign aligners
properly. The da Vinci robot (Intuitive Surgical, Sunnyvale, CA) was the pioneer
surgical robot introduced and became commercially available in 2000; however, it’s
still too pricey to be used in routine daily surgeries worldwide. It is estimated that
clinically feasible surgical robots will be realized by the end of the twenty-rst century. Integration of AI with surgical robotics will augment their surgical capabilities
so that future fully automated surgical robots would be able to “see,” “think,” and
“act” independently. Lesser active human intervention results in more precise movements and elimination of human errors such as tremors, enhancing the surgeon’s
capabilities until those even unskilled surgeons would be able to perform maxillofacial operations [1, 2]. Since such innovations could potentially shape the industry
over the next decade, we aim to encourage the new generation of oral surgeons to
join in and develop these new approaches in their operations.
Machine learning (ML), a subgroup of AI, has shown up as a powerful assistant
in resolving clinical issues in oral and maxillofacial surgery. Neural networks (NNs)
are popular ML models. These models outperform more classical ML algorithms by
a wide margin, especially when the data structure is complex such as visual or verbal data. A neural network is created by mathematical operations, linking the articial neurons and then making connections between the neuron layers. With a
sufcient amount of data, these networks can be trained to learn the images’ statistical patterns, thus predicting the outcome of unseen data. The prediction error would
be minimized by optimizing the model weights (the connections between the neurons) [3]. Radiology and pathology research has yielded impressive diagnostic and
predictive results using NN; based on these studies, these networks can be applied
in the diagnosis of cancer lesions in the oral cavity and recognition of canal
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involvement in the dentoalveolar surgery process [4]. Furthermore, they have the
potential to meet dental implant recognition, system classication requirements,
and assessment of the osteointegration quality as well [5, 6]. They were also used to
create a clinically applicable postsurgical video image simulation out of the patients’
presurgical lateral cephalometric radiographs and prole photographs [7]. Articial
neural networks (ANN) and convolutional neural networks (CNN) are the most
commonly used types of neural networks [6]. CNN (also known as deep neural
network) is a customized format of ANN where convolutional functions are applied
to the input data by intermediate nodes. It automatically extracts the images’ important features and isolates the images’ patterns from the raw data without the need for
human supervision [8]. This makes CNN a faster and more accurate tool for image
classication compared to ANN, which needs manual feature extraction [9].
Considering all progressions, most of the designed models for dental and medical use remained at the research level so far, and only a limited number of models
were qualied enough to be used in clinical situations. Three main reasons have
been listed for this [3, 4, 10]:
1. Compared to other types of data, medical and dental data is signicantly small
and less accessible because of concerns about the patients’ privacy. This data is
sensitive, multidimensional, and often incompletely recorded. Different data
banks, such as Kaggle, DataHub, and the UCI Machine Learning Repository, are
available. However, in order to train the model without bias, it would be more
suitable to use pure big data, including digital radiographs, electronic health
records, and longitudinal follow-up data. On the other hand, during the sampling
process, the selection bias often disrupts the data balance (e.g., toward the sick
in hospital data or toward the health in data collected from wearable devices).
2. Replicability and robustness of the training, testing, and validating results are
often insufcient. Using similar data as training and testing sets results in “data
snooping bias.” The performance of a model trained and tested by similar retrospective data wouldn’t be optimal when encountering new prospective data.
There is no agreement in dening an acceptable ground truth and determining
how many experts would be sufcient for the labeling and annotation process.
3. Generally, the information provided by dental AI applications only partially
reects a patient’s complex decision-making process. Responsibility and
transparency- related issues are still under question because the AI systems cannot clearly explain their decision-making steps. In contrast to the research stage,
where accuracy and other functional results are more important than transparency, it is an essential issue in the clinical setting. Accurate but not explainable
systems are not trustable for patients and clinicians.
As a result of these incompetencies, most AI-related articles are not published in
high-quality peer-reviewed journals. Future AI models for clinical use must be clinical and patient-centered rather than theoretical and functional-centered. For
instance, they should publish information regarding positive and negative predictive
values instead of the area under the curve of a receiver operating characteristic
curve. These values are relevant to changes in patient outcomes and are more
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interpretable for clinicians, especially for those who are not familiar with AI basics.
On the other hand, related regulatory systems such as the US Food and Drug
Administration must dene monitoring guidelines to ensure the models’ safety and
efciency and continue developing these rules as the models keep improving day by
day. Also, it will be sensible that oral-maxillofacial surgeons and residents should
develop their understanding of basic AI concepts and metrics. They need to integrate AI courses into their educational curriculums and invite experts to teach
AI-related concepts at the clinically foundational level [4]. The accuracy of annotations and labeling of a training dataset signicantly impact the accuracy of AI systems’ predictions. The inconsistent quality of clinic-labeled datasets, which results
from poor knowledge about AI, limits the effectiveness of the nal AI systems.
Consequently, to decrease the errors and increase the speed of applying AI in dentistry routinely, clinicians and dental students have to improve their comprehension
of the process [11].
P. Motie et al.
2 Possible Future Applications
2.1 Precision Medicine
Precision medicine is a customized medical procedure of personalizing medicine by
patient’s lifestyle, gut microbiome, genetics, geography, habits, sleep, stress, medical history, underlying biology, environment, and other characteristics [12–14]. In
precision medicine, the healthcare providers focus on each patient’s unique genetic
algorithm, environment, and lifestyle to predict their susceptibility to disease and
treatment response. Biological diversity such as functional genomics results in differences in health condition, disease risk, prognosis, and treatment response [14].
Accurate and practical analysis of this enormous database wouldn’t be possible but
with the help of machine intelligence [15].
2.1.1 AI inPrecision Medicine
Cost reductions in prevention and treatment, low cost-effectiveness, overutilization,
insufcient patient care, and high readmission and death rates are all contemporary
issues in many clinical cares. To allow clinically useful automated and predictive
data analysis, productive collaborations between physicians and data scientists are
required. Storage, analysis, and exploitation of the current large and heterogeneous
big data content of “omics” data and social media are impracticable [16, 17].
Datasets that capture such variation can be analyzed using articial intelligence
algorithms to identify cryptic structures of the phenotypic and genotypic data. These
analyses’ results could further be used to predict the risk of disease, treatment
response, prognosis, and other individual patient outcomes based on their own
unique characteristics [13]. AI can be utilized in identifying high-risk patients in the
eld of opioid addiction or the patients who may experience higher postoperative
pain by analyzing patient-related information such as familial and medical history [18].
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Today, there is no anthropometric technique that is universally applicable for
surgical planning; all available techniques have their advantages and disadvantages.
Machine learning techniques, in conjunction with geometric morphometrics, have
enabled the production of very detailed and accurate facial forms and statistical
models. Despite their infancy, these models promise to be highly effective tools for
surgical planning and assessment [19].
Moreover, in the eld of plastic surgery, evaluating wound properties such as size
and shape and patient-related factors including skin type, lifestyle, and genotype by
the use of machine intelligence results in suitable individual-based surgical plans
and faster decision-making, which help prevent wound infection and improve
patient care. The technology could also be used in the prediction of the affected tissue percentage as well as the healing time. Currently, as a presurgical evaluation of
anatomical structures to design a suitable surgical ap, a surgeon needs to analyze
the three-dimensional (3D) CT or CBCT images into slices which is a timeconsuming task with a human-dependent accuracy. AI could assist the surgeons in
designing a faster and more precise customized ap for each patient [18].
Future AI systems would also be able to guide clinicians with every single step
of implant therapy. They will merge the information from CBCTs and intraoral
scans to automatically design the proper implant and its restoration and use the
information from the patients’ specic medical history and clinical features (i.e., the
tissue thickness, anatomical considerations, bone quality and quantity, emergence
prole, occlusion, etc.) to provide a surgical guide [10].
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2.2 Tissue Engineering
Tissue engineering is a branch of regenerative medicine that aims to provide solutions using biomaterials and stem cell research. It employs the body’s natural healing responses to regenerate tissue and organs. There remain many unanswered
questions in tissue engineering, with the best biomaterial designs still to be developed and a lack of stem cell knowledge posing signicant limitations to successful
applications. Advances in articial intelligence and machine learning allow us to
push scientic understanding and improve clinical outcomes [20].
2.2.1 Degradation oftheScaffold
Entekhabi and colleagues developed a machine learning model, which offers an
excellent prediction accuracy to estimate the degradation rate of the scaffold [21].
This will signicantly help reduce the experimental costs of scaffolds’ design. In the
rst stage of the study, biodegradable porous scaffolds with different weight percentages of gelatin and genipin were prepared for tissue regeneration, and their various
properties, including physical appearance, mechanical properties, molecular weight
distribution, swelling, degree of cross-linking, and degradation rate, were measured.
The experiment results showed that a higher percentage of genipin had the most
signicant amount of cross-linking and, when genipin was increased from 0.125% to
0.5%, ultimate tensile strength (UTS) increased by 113% and 92% when the gelatin
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percentages are 2.5 and 10, respectively. For these samples, increasing the percentage of genipin reduced their degradation rate signicantly, with an average value of
124%. Furthermore, in the next stage, these experimental data were used to develop
a machine learning model comparing articial neural networks (ANN) and kernel
ridge regression (KRR) to predict the degradation rate of genipin cross-linked gelatin
scaffolds. The degradation rate is predicted by six parameters that are differentially
correlated to the degradation. The variables involved are X1, gelatin percentage
(%W); X2, genipin percentage (%W); X3, swelling (%); X4, porous size (μm); X5,
UTS (kPa); X6, elongation (%); and X7, degree of cross- linking (%). The predicted
degradation rate demonstrates that the ANN, with a mean squared error (MSE) of
2.68%, outperforms the KRR with MSE=4.78% in terms of accuracy.
P. Motie et al.
2.2.2 Bone Growth
Ghosh and colleagues present near-optimal neural network architectures for predicting long-term secondary stability of uncemented prostheses at commercially
reasonable bone-implant interfaces [22]. A mechanobiology algorithm has been
used to study progressive bone growth. Eighty nite element (FE)-based mechanoregulatory analyses of bone growth have been used to train the neural network (NN),
and another 12 such results have been used to validate the network. The levels of
NN-predicted bone growth were found to have a pronounced correlation with the
FE-predicted results. Results show that reduction in the groove dimension promotes
higher bone growth levels. Moreover, periodic patterns of grooves with higher and
lower groove dimensions caused a uniform stress regime in the interfacial region,
resulting in enhanced bone growth. The present NN-based scheme was able to speculate the amount of bone growth based on geometric parameters of the macrotextures and, therefore, may be used as a preclinical tool to discover favorable
implant designs. It is worth noting that the presented scheme is exible enough to
incorporate variable loading and boundary conditions in a future investigation.
2.2.3 In Vivo Bone Regeneration
Wu and colleagues presented an ML-based multiscale modeling and remodeling
approach for predicting bone regeneration in tissue scaffolds at both macroscopic
and microscopic levels [23]. As a representative case study, a cylindrically shaped
porous scaffold was implanted into a 3-cm-long segmental defect in the mid diaphyseal tibia of a merino sheep. The bone ingrowth results predicted by this procedure
were compared with those obtained using the conventional nite element (FE)
model to scrutinize its credibility and efciency. Inverse identication was also
being made by correlating the predicted in silico results with a longitudinal invivo
sheep study. A bone remodeling algorithm was built following Wolff’s law, allowing it to predict the bone regeneration outcome. Mechanical stimuli from the strain
energy density (SED) were then input as a way of testing it. To nd when Wolff’s
law is applicable and if the remodeling parameters relate, invivo X-ray images were
taken (0, 3, 6, and 9 months). A third neural network (NN3) was developed to predict Pearson correlation coefcients to measure the correlation of net changes in the
signal densities of regions of interest (ROIs). Figure1 compares the macroscopic
bone density distribution within the scaffold, showing that the proposed ML-based
Strain ener
density (J)
Bone density
FE-based
ML-based
a
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gy
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1.36 x 10
1.22 x 10
1.07 x 10
9.22 x 10
7.75 x 10
6.27 x 10
4.80 x 10
3.33 x 10
1.86 x 10
3.84 x 10
b
(g cm-3)
1.34
1.21
1.08
9.47 x 10
8.16 x 10
6.85 x 10
5.54 x 10
4.24 x 10
2.93 x 10
1.62 x 10
-3
-3
-3
-4
-4
-4
-4
-4
-4
-5
-1
-1
-1
-1
-1
-1
-1
FE-based
ML-based
Fig. 1 Comparing macroscopic bone density distribution within the scaffold. The ML-based
approach agrees well with its FE counterpart. (a) the distribution of strain energy density (SED)
inside the representative volume elements (RVEs), (b) The map of macroscopic bone distribution
based on the RVEs at mouth (Reprint with permission from [23])
approach agrees well with its FE counterpart. It’s evident that the ML approach
converges with the traditional FE method, both in distribution and magnitude. In
this study, degradation of the scaffold has not been considered, and only one representative sheep is exemplied using X-ray images. However, the ML-based modeling approach demonstrates satisfactory accuracy and efciency for predicting
invivo bone tissue regeneration.
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P. Motie et al.
2.3 3D Printing
Recent studies have shown that an articial intelligence approach based on machine
learning (ML) can improve the 3D printing of materials in three common ways [24,
25]: assessing the quality of the prints [26, 27], optimizing printing parameters to
maximize the nal structure’s properties [28, 29], and monitoring the process
[30–32].
2.3.1 Assessing Quality
Machine learning has been extensively used to optimize the performance of 3D printing, but few studies have examined the application of machine learning in different 3D
bioprinting processes [33]. Most current 3D bioprinting applications are designed to
fabricate a tissue replica, which is difcult due to technological limits [34]. ML models are typically used in more complex situations because they can account for factors
or conditions that traditional mathematical models couldn’t [35]. Jin and colleagues
implemented an anomaly detection system to identify and distinguish anomalies in
bio-printed materials [36]. Three different types of anomalies (discontinuity, nonuniformity, and irregularity) were characterized by this system. The ability to predict
anomalies in various patterns is demonstrated using different colors on an image of a
sampling of the testing dataset (Fig.2). The feature extractor may fail if the anomaly
is at the edge of an image. However, thick irregularity anomalies are all correctly
predicted. This may be caused by the imbalanced training dataset, as a thicker irregularity is more common due to the suboptimal printing temperature settings.
2.3.2 Optimizing Printing Parameters
Ruberu and colleagues investigated the prospects of combining machine learning
with 3D bioprinting to improve printability [37]. Bayesian optimization (BO), a
Fig. 2 Feature extractor using different colors on an image of a sampling of the testing dataset.
(Reprint with permission from [36])
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commonly used optimization algorithm, was applied to the experimental process to
nd the best printing parameters with minimal testing. The BO algorithm was
accompanied by a scoring system, assessing the printability of gelatin methacryloyl
(GelMA) and hyaluronic acid methacrylate (HAMA) bio-inks. Two fundamental
printing criteria, the lament formation of the bio-ink and the layer stacking of the
3D scaffold, have been incorporated into the scoring metric. The framework was
initially developed based on a set of randomly designed and scored experiments.
These experimental results were made of pairs of printer settings and their associate
printing score. The optimizer builds a probabilistic system model used to recommend the next batch of printer settings experiments. An experimenter then conducted printer tests following the recommended settings and scored the performance.
By feeding back these results to the optimizer, this loop is continued to reach an
optimal print. The optimal print parameters for GelMA containing inks with ranging concentrations (10%, 7.5%, and 5% (w/v)) were obtained in 19, 4, and 47 experiments, whereas for GelMA-HAMA (10:2%, 7.5:2%, and 5:2% (w/v)), 32, 25, and
32 experiments were required, respectively. As compared to the possible combinations counted by Bayesian algorithm (6000–10,000), this number of experiments is
drastically reduced. In contrast to the tedious and time-consuming traditional trial
and error process, the BO team accelerated the experimentation process.
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2.4 4D Printing
Four-dimensional printing combines the advantages of 3D printing and innovative
materials, which enables printed objects to change their shape or other properties
when stimulated externally. The effect of printing parameters, such as angle and
temperature, is being investigated to see how accurate AI can predict the shape morphing behavior. Intending to push 4D printing to a new paradigm of autonomy, Ji
and colleagues demonstrated how data-driven approaches can be employed to predict and understand the shape morphing behaviors during printing four-dimensional
(4D) objects based on experimental data [38]. The paper proposes a scheme that
aims to improve the precision and actuation speed of 4D printed smart materials. A
Q-learning-based control policy for 4D shape morphing is developed by reinforcement learning. Figure3 shows the cascade control structure. The inner loop controls
the temperature on the controller board. This happens at a sampling time of 0.05s,
with reference temperature coming from the outer loop controller. The outer loop
runs the reinforcement learning (RL) controller and decides the target temperature
for a subsequent control period based on the angle and temperature feedback.
Angles can be controlled precisely to the reference with less than 5% deviation.
Strips of 1D shape memory polymer (SMP) are fabricated and precisely transformed into different 2D structures. These 2D structures are then put together to
make complex 3D structures. This method is shown to have high precision and
consistency in these 3D assembly procedures.
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Temperature
Feedback
10Ts
–
Temperature
+
Angle
reference
Fig. 3 The schematic structure of the cascade control
RL
Controller
Controller
Heatign Unit
10Ts
Angle Feedback
P. Motie et al.
SMP target
2.5 Robotic Surgery
Articial intelligence is gradually altering surgery practices with imaging, navigation, and robotic intervention advancements [2]. The initial purpose of developing
the surgical robots was full automation of the procedure [39]. Surgery is a dynamic
process requiring a great deal of precision and expertise from the surgeon. AI especially has been advantageous in the eld of robotics as it optimizes the autonomy of
a robot’s tasks and ability to interact with their environment, which is contrary to
conventional robots that could only apply predened actions [40, 41]. The use of
articial intelligence in robotic-assisted surgeries is substantiated and can be a lifesaving measure, especially in the eld of oncology, in which the size of the incisions
is crucial for success rates. The performance standards for evaluating autonomous
robotic surgery should be dened before clinical practice. Important factors include
its ability to adapt to unforeseen events, the accuracy of surgical gestures, and
repeatability [3].
Ma and colleagues proposed an autonomous surgical system developed for oral
and maxillofacial surgery, driven to work with the assistance and surveillance of the
surgeon [42]. This system mainly consists of three modules: a markerless navigation module, a compact robot module, and a position correlation module. The overall view and workow are shown in Fig.4. For the experimental part, 16 drilling
holes with an interval of 5mm were planned in a 4×4 layout in the mandible. The
system has some limitations and needs an update in the hardware and software
structure. The rst issue is whether the xed camera could cover a desired eld of
interest. Another potential problem is the system delay due to image processing.
However, the proposed system has great potential in clinics in the future because it
may change the priorities of surgeons.
As osteotomy is the most commonly used technique for various oral and maxillofacial surgery operations, Kwon and colleagues created an autonomous robot
Camera
system
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Monitor
Device
marker
Robot
marker
Camera
Polaris
marker
PhantomRobot
Workstation
osteotomy
device
Robot
adapter
Device
controller
Fig. 4 The overall view and workow. (Reprint with permission from [42])
osteotomy scheme, which relies on the three points on teeth using direct, coordinate
determination [43]. They compared a robotic versus manual surgical procedure to see
the differences between accuracy and precision. The robotic surgery showed better
accuracy and precision in positioning but lower accuracy in controlling the depth of
the disc sawing, with overall more accuracy and precision than manual surgery.
3 Challenges
Despite the great hope and promising aspects of articial intelligence, there are still
some challenges and limitations, which can be categorized into three main groups.
First, the methodological and epistemological misconceptions about articial intelligence; second, the restrictions on the social context in which machine learning
applications are developed; and third, the consequence of current technical limitations in the development and use of articial intelligence [44]. The following are
some of the most critical challenges AI is struggling with.
3.1 Black Box Nature
In recent years, deep learning has shown acceptable accuracy in image analysis for
diagnostic and predictive purposes. However, these algorithms are still in a “black
box.” This problem leaves deep machine learning methods without explaining why
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