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5.2.6 Lack ofSpecific Instruments forMaxillofacial Surgery
For example, electric bone saws and drills are unwieldy. This issue needs to be
addressed in the near future.
6 Prospective ofRobotics intheHead andNeck
andMaxillofacial Region
The robotic surgical system is a new and minimally invasive approach with potential benets but is still in its early stages of development. There are various challenges and obstacles to wider adoption of this technique. Further improvements are
necessary before it can be widely used for maxillofacial surgery for head and neck
tumors and non-cancerous conditions [5].
From a clinical viewpoint, the widespread adoption of robots in head and neck
surgeries is unavoidable. Research shows great results in terms of surgery outcomes,
cancer control, and functional recovery for head and neck tumor patients treated
with robots. But there are still some challenges and uncertainties linked to robotic
surgery. As mentioned earlier, the frequency of capsule damage or tumor breakage
during robotic surgery is relatively high.
Robotic surgery for head and neck tumors requires longer surgical time or drainage, particularly in retroauricular or face-lift approaches due to extended aps. The
improvement of prognosis for HPV-negative patients with robotic surgery is unclear.
Robotic surgery for head and neck tumors has a variable rate of regional/distant
metastasis and requires further study for long-term effects and cost-effectiveness.
Specialized instruments, miniaturization, haptic feedback, multi-surgeon capability,
and exible access devices are future developments desired for the improvement of
robotic surgery.
Cleft palate surgery can be performed successfully with robotic-assisted system,
and initial challenges with accessing the soft palate through the oral cavity have
been addressed. However, advances in technology with smaller and more exible
instruments may improve opportunities for palatal surgeries. Robotic surgery for
cleft lip and palate is limited in available studies, with only one clinical research
demonstrating benets of shorter hospital stays and improved functional recovery
compared to traditional surgery. Further studies with larger sample sizes are needed
to ensure safety and feasibility.
Virtual surgical planning (VSP) enhances guidance in robotic surgery, potentially improving accuracy and efciency. Combining VSP with robotic surgery is
expected to lead to shorter surgical duration and superior reconstruction, a trend in
future robotic surgery.
Robotic surgery has been used in OSAS patients and is promising for those intolerants of CPAP, but success rate remains unsatisfactory due to multiple risk factors
for OSAS sufferers.
Robotic surgery for OSAS should only be used after careful patient selection.
Factors such as severity, underlying dento-skeletal deciencies, age, BMI, and soft
tissue structures should be considered. Currently, the use of robotic surgical systems
Denition, History, andIndications ofRobotic Surgery inOral andMaxillofacial Surgery
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259
in maxillofacial fractures and orthognathic surgery is limited due to the lack of tactile and haptic feedback.
Current robotic surgical technology lacks the appropriate resistance to prevent
additional damage when treating maxillofacial fractures and orthognathic surgery.
More work is needed to advance the technology and make it suitable for these procedures. Theoretical feasibility and clinical application of robotic surgical systems
in craniofacial and orthognathic surgery need further development.
The long-term effects and safety of robotic surgery in conditions like ectopic
lingual thyroid and ptyalolithiasis need more research. Choosing the right surgical
procedures for the system is also a challenge and requires well-designed studies.
Standardizing maxillofacial surgical procedures is necessary for the successful
application of surgical robots in this eld. This will help overcome the challenges
posed by the diversity of maxillofacial surgery and promote wider adoption of
robotic surgery. To advance robotic surgery in maxillofacial surgery, instrument
specialization, improved intraoperative navigation, and more extensive studies with
larger samples of various maxillofacial procedures are needed. The application of
oscillating and surgical drills in robotic arthroplasty has not yet been applied to
maxillofacial surgery. From a technical point of view, the extended time for robot
docking, changing tools, and inserting supplies is one of the main deciencies of
robotic surgery. To address this issue, two technical projects have been recently
proposed [9]. One is “robotic systems” that refer to the integration of multiple surgical robots into a single operating unit for enhanced efciency and precision during
surgeries. The use of robotic tool changers or supply dispensers can reduce the need
for human intervention, leading to faster and more accurate procedures.
“Autonomous or automatic surgery” is the other one, which refers to the use of
technology to perform a pre-programmed surgical task without direct human intervention. The challenge in implementing this technology lies in the variability and
unpredictability of the living system, but in theory by collecting enough data on
previously performed procedures, it can be made possible.
The lack of haptic feedback can limit a surgeon’s ability to feel tissue and structures, leading to a higher risk of unintended tissue damage, such as palatal muscles
in robotic-assisted cleft palate surgery. Haptic feedback can also provide important
information about tissue stiffness, resistance, and other important features that are
essential for precise surgical procedures.
Currently, most robotic surgical instruments are simple mechanical devices that
rely on visual information and the surgeon’s subjective sense of touch to guide their
movements. This can limit the surgeon’s ability to feel important tissue features and
can increase the risk of unintended tissue damage.
Despite the potential benets of haptic feedback in robotic surgery, there is currently no widely adopted haptic sensor technology that is incorporated into commercial robotic surgical systems. Some early attempts to address this issue, such as
the VerroTouch system introduced by Tsang [116], have been developed but have
not seen widespread adoption. Some specialized robotic systems, like ACROBOT
and MAKO RIO, used in orthopedic surgery, have incorporated haptic feedback
through the use of predened safe regions, but these systems are limited in their
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application and do not provide full haptic feedback to the surgeon. The resistance
applied by the control and drive systems in a surgical manipulator (such as MAKO
RIO) keeps the surgeon from operating outside the predened surgical plan. The use
of similar techniques in head and neck surgery could be possible with the advancement of computer-aided manufacturing and computer-aided design. Further there
are other engineering barriers to the wider adoption of robotic surgical systems: (1)
ease of use, as current systems are complex and require advanced training, and (2)
the reliability of telecommunication, as low packet loss and limited latency are crucial for safe remote operations.
Currently, the high cost of robotic surgical equipment is a limitation to its widespread use in operating rooms worldwide. The author suggests the need for the
development of smaller, less expensive, and more user-friendly robotic platforms,
along with specic instruments for head and neck surgery, to overcome this limitation and believes that the development of smaller instruments and further advances
and modications will make it easier to incorporate robotic equipment into TORCS
(transoral robotic cleft palate surgery). They also mention the potential advantage of
the robot’s imaging capabilities for training and teaching purposes. The development of formal training courses is needed to improve safety of robotic surgery, similar to the introduction of endoscopic surgery in the 1990s.
Recent advances in telecommunication and 3D imaging provided by the surgical
cart can make it possible to transmit the surgical view to large 3D screens in any
location around the world.
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Brief Introduction toArtificial
https://t.me/medicina_free
Intelligence andMachine Learning
SaeedRezaMotamedian, SahelHassanzadeh-Samani,
MohadesehNadimi, ParnianShobeiri, ParisaMotie,
MohammadHosseinRohban, ErfanMahmoudinia,
andHosseinMohammad-Rahimi
S. R. Motamedian
Department of Orthodontics, Dentofacial Deformities Research Center, Research Institute of
Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences,
Tehran, Iran
Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health,
Berlin, Germany
S. Hassanzadeh-Samani
Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health,
Berlin, Germany
M. Nadimi
Department of Cardiology, Cardiovascular Diseases Research Center, Heshmat Hospital,
School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
P. Shobeiri
School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
Universal Scientic Education and Research Network (USERN), Tehran, Iran
Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population
Sciences Institute, Tehran University of Medical Sciences, Isfahan, Iran
P. Motie
Medical Image and Signal Processing Research Center, Isfahan University of Medical
Sciences, Isfahan, Iran
M. H. Rohban · E. Mahmoudinia
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
H. Mohammad-Rahimi (*)
Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health,
Berlin, Germany
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023
A. Khojasteh et al. (eds.), Emerging Technologies in Oral and Maxillofacial
Surgery, https://doi.org/10.1007/978-981-19-8602-4_14
267
268
https://t.me/medicina_free
Fig. 1 Articial
intelligence vs. machine
learning vs. deep learning.
Deep learning is a subset
of machine learning, which
itself is a subset of articial
intelligence
Artificial Intelligence
Machine Learning
Deep Learning
S. R. Motamedian et al.
1 Introduction
Articial intelligence (AI) is dened as intelligence displayed by machines to
interpret and learn from data to ultimately solve tasks, including those typically
carried out by humans [1]. AI is growing more and more due to the availability of
large datasets and the advent of powerful parallel computing hardware [2]. As a
subset of AI, machine learning is dened as giving computers the capability to
learn from data without being specically programmed [3]. Using machine learning, algorithms are created that can learn and predict. Because it is based on no
explicit rules, machine learning algorithms are empowered as they are used in
more novel datasets; therefore, more experiences result in their automatic improvement [4, 5].
Deep learning, which is a subset of machine learning itself1, is dened as learning representations of data that incorporate multiple levels of abstraction by using
computational models (Fig.1). Using the backpropagation algorithm, deep learning
identies intricate structures in large datasets by indicating how to change the
parameters of the machine that compute the representation in each layer from the
representation at the previous layer [6]. Over the past decade, diverse deep learning
techniques have been applied to the medical images with different modalities to
assist clinicians by automating the various time-consuming processes in medical
imaging applications [7], including Alzheimer’s disease detection [8], urinary
stones detection [9], coronary calcium score quantication on CT images [10],
1
In the following sections of this chapter, everywhere we used the term “machine learning”; it also
includes “deep learning” approaches.
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