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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_537_Библиотеки_им_академика_М_И_Перельмана
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all neck levels [130]. A skin flap is elevated in this technique with the
placement of a self-retaining retractor, creating a workspace for the robotic
neck dissection. We refer to [24] the article for the details.
Additional research literature and the associated areas of TORS
applications and observations included, but were not limited to, the
following:
Tong neoplasms [137]
Glottic microsurgery [138]
Supraglottic partial laryngectomy [139]
TORS of the oropharyngeal cancer [136, 140, 141, 143–147]
4 Technology Integration into Robotics Use in
Facial Plastic Surgery
Technologies integrated into robotics use in facial plastic surgery come
along a long way within the last 10years. As is the case for dentistry and
medicine, technology, when used prudently, can add the accuracy and speed
in which oral and maxillofacial surgeons care for patients [15].
Technologies incorporate into robotics for plastic surgery procedures
included (but are not limited to) CT scanning technologies, machine
learning, deep learning, AR/VR, 3D simulation, computerized analysis, and
natural language processing [105]. Even though these technologies can be
beneficial to estimate an outcome from facial plastic surgery as well as offer
assistance to the surgeon, the results shadow the actual outcomes noting that
the difference between the predicted and actual outcomes can result in
patient dissatisfaction [106, 107]. Machine learning in combination with
perioperative photos can be advantageous over the robotic predictions [16].
Augmented reality has been incorporated into robotic-assisted
craniomaxillofacial surgeries allowing ease of 3D simulation of the
outcomes in [8] for the first time. A rapid prototype is introduced by the
authors to position landmarks and robot-assisted arms to utilize an operative
method. For comparative purposes, both traditional and robot-assisted
procedures are tested. Toolkits of AR are used for system tracking and
display with the operative plans and measured error carried out. Positioning
and angle accuracy and stability results attained for the RAS method when
compared to the traditional method indicated success of the combination of
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AR and RAS use; Therefore, AR navigation use for mandibular angle split
osteotomy with specialized robot-assisted arms was determined to be a
success [8]. Figure 2 displays an example of an optical tracking system
integrated into a Kuka robotic surgical system with a tool developed and
used with the robot for dental applications [119].
Fig. 2 An integrated technological system that incorporates a robot and an optical tracker [119]
Another AR application to increase the effectiveness of mandible plastic
surgery and reduce the burden of surgeons is developed in [9] for attaining
comparative results of robotic-assisted and conventional plastic surgeries.
As a part of this application, motor control followed fuzzy logic and AR
assists surgeons with positioning. The concern associated with AR’s
detection and position estimation of the outcomes of drilling is handled with
the use of a force sensor. Experiments are conducted on animals for
identifying the effectiveness of the robotic system. Errors associated with
positioning and angles as well as sensor feedback and automatic drilling
calculations for the designed system determined to result in accuracy of
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positioning and automatic drilling that can result in improving the
traditional plastic surgical procedures.
The robotics applications require training of surgeons and their skill
assessments [31, 148].
Patient safety and operative performance can be improved by the
assessment of objective metrics that relate to dissection and anastomosis
[149]. Analysis of the data collected on visual and kinematic data captured
during robotic surgeries provides opportunities to build effective tools;
however, it comes with the associated challenges as the nature of the
surgical motion data is complicated, hand motion generates variability in
motion detection, and the data has a nonlinear nature [150]. Analysis of
such data sets requires advanced analysis methods, such as machine
learning.
Orbital bone positioning and visualization of orbital fractures is
accomplished by using mixed reality in [16]. Noting that accurate
localization of the orbital bone is critical for achieving orbital fracture
reduction surgery (OFRS), a specific marker is designed and adopted by the
authors to perform the registration step and the outcomes to be displayed in
a platform of the navigation system. The precision is determined to be an
advantage of the developed system while the need for localization accuracy
improvement is mentioned by marker detection and registration methods,
along with evaluation of subjective feedback on acceptance and usability
[16].
Deep learning (DL), a subset of machine learning, serves to simplify the
analysis of data sets with complicated nature that can be attained during
facial plastic surgeries [13]. An application of DL that works in localized
settings is region-based convolutional neural networks that help identify
regions of interest with selective search to help localize objects of interest
that resolve the recognition challenges [151]. One successful application of
this method is the surgical tool and hand detection from surgical videos for
extraction of more data [152]. Another useful method is attained by
combining deep learning with reinforcement learning (called deep
reinforcement learning) through enhancement of the machine’s knowledge
by using a feedback mechanism to improve the reactions of the machine by
using well-defined reward and punishment with the associated
consequences applied. A combination of computer vision and deep
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reinforcement learning is practically one application to enhance roboticassisted surgeries [153].
In this application, entities within the intraoperative environment, such
as organs and their movements, are read by a computer vision system while
automatic motion recognition and pattern detection are analyzed by the
deep reinforcement method [154].
The augmented and virtual reality applications require several steps to
be taken during a reconstructive surgery as the surgery requires these steps.
Initially, CT scan angiography data is used for loading segmented bone and
vessels with resection of the bone to prepare the recipient site for
reconstruction. Fibula segment positions, orientations, and angulations need
to be defined and pedicle reach to anastomosis sites on the recipient vessels
needs to be tested. Possible skin paddle configurations need to be figured
out as well. Several iterations of the Design and Test stage would help to
find a suitable configuration for the fibula, vessels, and skin paddle [108].
Figure 3 [109] displays the use of VR simulation using a haptic device.
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Fig. 3 Simulation by using VR [110]
The ability to provide additional information without diminishing the
camera’s information or giving a wrong perception is one of the challenges
faced in AR applications. This limitation has been partially eliminated with
the advancement of machine learning by using object subtraction method
that had strong performance for the detection of instruments in laparoscopy
videos, allowing the surgeon to view only the relevant information [112].
Figure 4 displays depth perception improvement methods for AR based on
curvature-dependent transparency [111].
Fig. 4 Points chosen on a person for AR application with the view of how AR would demonstrate
the skull area [111]
As far as the planning of VR application is concerned, the marking of
the location is accomplished first, as shown on the left of Fig. 5. What
follows next is the virtual cut of the marked location. The use of robotics
for these two steps would be an advanced method of robotics and VR
integration.
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Fig. 5 The VR application developed in [109] for skull marking is displayed on the left image, and
the image on the right side displays the next step with the removal of the marked region
The next step of the application requires raising skin flap and lifting
anteriorly, as shown in the left image in Fig. 6. The haptic device stylus
serves as the aneurysm clip holder in this image. The image on the right
shows the clip holder and the clip that gives the 3D depth [109].
Fig. 6 The steps following Fig. 4 with a clip and clip holder displayed for the associated operation
Integration of robotics into VR in the abovementioned setting can be
one of the new initiatives that have not been explored in the literature.
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Augmented Reality is used for testing marker-free robotic surgical
placement of two iliac crest transplants in [26]. The purpose of the AR and
robotics integration was to determine the benefit of combining the two
technologies for easing complex vascularized graft reconstructions and
identifying their effectiveness in reducing facial skeleton–related defects.
Figure 7 demonstrates a robot with a 3D camera that has a mini projector
and a 3D camera attached to a mechanical system that allows the robot to
move freely to operate on the projected iliac crest implant [113]. The key
elements of the AR application and determining its effectiveness in [26]
included random CT scans of two commonly used iliac crest transplantation
model configurations, cutting guidelines following the surgical protocols,
and the measurements of duration, accuracies of the distances, angulation,
and the volume between the planned and executed osteotomies. The authors
determined the time and accurate execution of preoperatively planned
geometries to be the beneficial aspects of using, AR while anatomical
complexities and vertical osteotomy were the weaknesses faced. It is
concluded that the use of AR has benefits in applications with the
shortcomings outlined above.
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Fig. 7 A robot with a projector and 3D camera attached to a mechanical unit for easing the robot’s
move to operate on an iliac crest model
Postoperative skeletal change prediction for orthognathic surgical
planning by using machine learning is investigated in [29] and the authors
showed its potential for reducing the workload of surgeons. Noting this
success, the authors also pointed out its potential beneficial use with the
robotic surgical hardware [114, 115].
Other technologies that are used alongside of robotics included
computer-aided design and computer-aided manufacturing, which allowed
symmetrical reconstruction of unilateral defects by mirroring the unaffected
side [10, 160–162], and simulation is used for demonstrating the surgical
process with the associated lengths and angles calculated for fibular
segments with marking of curves/lines [157, 158]; it is also possible to
incorporate robotics as a part of these technologies themselves.
5 Free Flap Reconstruction
Free fibula flap is the typical application used for mandibular defect
treatment. The application of robotics is seen in both the preoperative
planning and during surgeries. In this section, we will cover a review of the
literature that contains such applications.
Free fibula flap’s preoperative planning protocols are costly to learn and
have execution challenges; these protocols are not robotic-assisted surgery
friendly for cutting fibular osteotomy plane. Redesign of such preoperative
surgical planning system is essential, which allows robotics assistance.
Such a system is proposed in [10] with the associated adaptation of artificial
intelligence with the incorporation of information attained from the CT scan
images, as shown in Fig. 8 [10]. This figure displays the data read from the
deformed mandibular structure with the associated CT scan images used for
AI application.
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Fig. 8 The progression starting with CT scanned images to demonstration of mandibular deformity
within simulation and AI application
A part of the preplanning process is excision of defected mandible along
with mirroring and repositioning of the planned mandible. This requires
determination of an appropriate and accurate planar structure for the
mandible, as shown in Fig. 9.
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Fig. 9 The planar structure formed for mandibular reconstruction and the associated three
dimensions
The planned mandibular structure is then incorporated into the
simulation with the associated planned cutting of the bone selected for
mandibular reconstruction displayed in Fig. 10 [10]. Path planning is then
designed for robotic execution as a part of the operational planning using
the landmarks determined for bone cutting and placement.
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