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5.2.6 Lack ofSpecific Instruments forMaxillofacial Surgery
For example, electric bone saws and drills are unwieldy. This issue needs to be addressed in the near future.
6 Prospective ofRobotics intheHead andNeck
andMaxillofacial Region
The robotic surgical system is a new and minimally invasive approach with poten­tial benets 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 benets 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 efciency. 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 deciencies, age, BMI, and soft tissue structures should be considered. Currently, the use of robotic surgical systems
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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 deciencies 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 efciency 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 benets 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 predened 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 predened 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 specic instruments for head and neck surgery, to overcome this limita­tion and believes that the development of smaller instruments and further advances and modications 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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Denition, History, andIndications ofRobotic Surgery inOral andMaxillofacial Surgery
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Brief Introduction toArtificial
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Intelligence andMachine Learning
SaeedRezaMotamedian, SahelHassanzadeh-Samani, MohadesehNadimi, ParnianShobeiri, ParisaMotie, MohammadHosseinRohban, ErfanMahmoudinia, andHosseinMohammad-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 Scientic 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
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Fig. 1 Articial intelligence vs. machine learning vs. deep learning. Deep learning is a subset of machine learning, which itself is a subset of articial intelligence
Artificial Intelligence
Machine Learning
Deep Learning
S. R. Motamedian et al.
1 Introduction
Articial intelligence (AI) is dened 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 dened as giving computers the capability to learn from data without being specically 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 dened as learn­ing representations of data that incorporate multiple levels of abstraction by using computational models (Fig.1). Using the backpropagation algorithm, deep learning identies 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 quantication 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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