Computer-assisted surgery: Evolution and basic concepts
As the field of robotics evolves, so too does our willingness to welcome technology into traditionally human-dominated realms. Computer-assisted surgery (CAS) is available for the modern operating room and is constantly being refined to enhance functionality, precision, and cost-effectiveness. Part 1 chronicles some of the evolution of CAS, looking at terminology and basic concepts.
Through the ages, humans have been fascinated with the idea of automated devices that serve our needs. Consider Hephaestus’ mechanical servants in Greek mythology . We have been entranced by the potential of machines for millennia [2,3]. Now, our capabilities are catching up to our imaginings: take a look at robotics lab Boston Dynamics’ newest commercial robot, Spot® .
Advancements in technology, combined with interdisciplinary team collaboration, is propelling the integration of innovations in materials, computing, and medicine to bring a technological revolution to the operating room (OR) across all surgical disciplines.
What is computer-assisted surgery?
Computer-assisted surgery (CAS) is a umbrella term that encompasses different kinds of technologies that are used to : perform surgical procedures, in part or in their entirety ; guide or navigate during surgery ; plan surgeries ; train less experienced surgeons ; and create patient-specific instruments (PSI) [5–7]. It is sometimes referred to as computer-aided surgery, computer-assisted intervention, surgical robots, image-guided surgery, or surgical navigation, depending on what the technology does [5, 7, 8].
CAS helps orthopedic surgeons perform surgery with increased precision and reproducibility, which is believed to have a positive impact on clinical and functional outcomes. There are two categories of CAS: robotic-assisted surgery, where a motor moves the technology, and general CAS (usually navigation systems), where the surgeon physically moves the technology .
Ahmed Magan, Trauma and Orthopedic Surgeon with University College London Hospital NHS Foundation Trust, UK, notes that robotic surgery in particular “has revolutionized surgical practice—from planning through to the execution of the operation. It is easy to learn and the results are reproducible.”
It all started with the brain
The pioneering work of CAS was in the field of neurosurgery in the early 1900s. Two British academics working at University College London Hospital, Sir Victory Horsley (professor of neurosurgery and a neuroscientist) and British physiologist, Robert Clarke, collaborated on the development of a stereotactic apparatus for locating lesions in the brain in 1908.
It essentially involved attaching the cranium to the “Horsley-Clarke Apparatus” and inserting a probe into an area of interest with some degree of accuracy. Their work was based on three-dimensional (3D) Cartesian geometry of a monkey brain . It is no wonder that their invention lacked precision and required further work.
Applications of CAS
Over time, CAS systems have been developed for use in a wide range of surgical disciplines. An indicator of the growth of the field is that in 1999, only 14 articles indexed in PubMed included “computer-assisted surgery” or “robotic surgery” in their titles or abstracts, while by 2019, 1,027 articles were published.
Neurosurgery was the first field to employ CAS [8, 11, 12], and the technology has expanded to support myriad surgical interventions. Here are a few examples:
- Total hip arthroplasty (THA): Robotic THA was found to improve acetabular implant positioning and reduce dislocations compared to manual THA [13, 14].
- Partial and total knee arthroplasty (TKA): Using navigation in TKA was associated with higher clinical accuracy in implant placement  and robot-assisted TKAs also improved implant positioning .
- Osteotomies: Using 3D-planned patient-specific instrumentation (PSI) and navigation in high tibial open wedge valgus-producing osteotomies resulted in accurately corrected mechanical leg axis .
- Tumors: Minimally invasive robotic hepatectomy for liver tumors has been shown to be “safe and feasible” . Intraoperative computer-assisted navigation and 3D PSI printing facilitated a successful surgical resection of metastatic acetabular osteosarcoma, ultimately preserving the patient’s hip stability and providing better quality of life for two palliative years .
- Neurosurgery: Robot-assisted drainage of thalamic hemorrhages improved patients’ prognoses and was associated with reduced cases of pneumonia and renal dysfunction .
- Spine surgery: A study of 18 patients that received navigation-assisted surgery for a primary spine tumor indicated that it was beneficial in the resection of tumors due to more accurate screw placement and fewer complications .
- Dental implants: Compared with a novice freehand implant placement group, novices using navigation were able to achieve implant placement in mandible models with an accuracy similar to that of experienced professionals .
In relation to orthopedics, Ahmed Magan highlights that, “In terms of contributions, computer-assisted surgery has been a real game-changer in partial and TKA. With this procedure, the outcome is related to the alignment, balance, and soft tissue preservation and there is technology available to help surgeons achieve these factors in more reliable, repeatable ways.
“However, there are barriers to this technology being widely adopted and available in all ORs such as, added operative time, being limited to implants specific to the system, resistance to new technology training for theater staff and cost implications.”
Classification of CAS systems
As CAS technology has expanded, how these technologies are classified has also evolved. There is a wide range of applications for CAS and since the 1990s numerous classification schemes have been suggested, with some common unifying elements [23, 24].
What has emerged is a general classification by functionality and surgeon operation (active, semi-active, passive) with a secondary consideration within navigation systems of how the reference system and surgical plan is defined (image-based, imageless) [5, 23, 25] [See Table 1].
In 2000, Picard et al proposed broadly classifying computer-assisted surgical technologies as either robotic assistive systems, where patient-specific models are used and actual machine-controlled contact with a patient occurs (active), or surgical navigation systems, where surgeons use optical or magnetic markers to track their tools/anatomy and receive intraoperative guidance compared with their surgical plan (passive) . As time has passed, some CAS technologies have been introduced that incorporate elements of both.
Table 1. Different types of computer-assisted surgical (CAS) systems
Active CAS systems execute preprogrammed tasks autonomously, such as drilling, while a surgeon oversees the procedure. They offer an interface between the surgeon and the patient, often with the intent to decrease human error [27, 28]. A motorized system moves the surgical tools and is capable of tirelessly performing preoperatively programmed, repetitive motions. This category aligns with what one would be most likely to label as a “robot”.
Semi-active/Haptic systems combine surgeon-controlled elements with preprogrammed elements in a complementary manner. Such a system might involve a robotic arm controlled by the surgeon that holds tools but will not move outside a predetermined milling path boundary .
What is haptic feedback?
Haptic feedback is the touch (force and tactile) information surgeons intuitively receive during their physical interaction with tissues and bone during surgery. Subconscious evaluation of this information allows them to adjust their movements in order to exert appropriate force to the tissues. Surgeons and engineers are working together to increase the haptic capabilities of surgical robots as the lack of effective feedback is a limitation for CAS .
Another example of a semi-active CAS system is a teleoperated master-slave system . In this case, a surgeon’s hand movements are translated into surgical actions performed by a robot with the surgeon at a distance, whether in the same room or further apart .
The 2001 “Lindberg Operation”: the first true telemedicine surgery [32–36]
Named after the first solo transatlantic flight in 1927 by pilot Charles Lindbergh, the world’s first true and completed teleoperated surgery took place on September 7, 2001, and required 54 minutes of operating time over a round-trip distance of nearly 14,000 km.
With the 68-year-old female patient located in Strasbourg, France, and the surgeon (Professor Jacques Marescaux) in New York, US, a laparoscopic cholecystectomy was performed using Computer Motion’s ZEUS surgical robot. The surgeon and robot were connected via asynchronous transfer mode (ATM) technology, a high-speed dedicated fiberoptic network for data transmission that runs across the Atlantic Ocean. The average transmission lag time was determined to be 155 ms (milliseconds) and the telecommunications costs estimated at over 1 million USD. The patient recovered well and within two weeks had returned to normal activities.
Image used with permission: IRCAD, France.
Passive CAS as a category is comprised of navigation systems, which is further broken down into image-based or imageless platforms, depending on if they use images and how those images are acquired [29, 37] [See Table 2]. They are used in preoperative planning as well as intraoperatively to monitor and compare a procedure to the plan in real-time; some systems combine these functions.
Navigation equipment can help: “1) to assess joint irregularities and joint biomechanics; 2) to make recommendations on how to continue with the procedure, when assessing ligament balancing, for instance; and 3) to monitor the accuracy of the bone cuts.”  Surgeons can override any recommendation made by these systems. The majority of CAS systems used in ORs today are tracking/navigation systems [27, 28, 39].
Navigation systems can be imagined as a global positioning system (GPS) for surgeons and are used in many surgical disciplines . In orthopedics, navigation systems are used to plan optimized, patient-specific implant placements as well as guide a surgeon through achieving their plan. Table 2 compares the two kinds of navigation systems (image-based and imageless). Keep in mind that many navigation systems combine elements of both types, for example, to facilitate optimized preoperative planning based on images, then real-time registration and intraoperative monitoring to compare progress against the plan.
In TKA and THA, Ahmed Magan prefers using image-based navigation. “It is also fantastic for planning complex cases with abnormal anatomy. However, for the few patients who may have reservations about radiation exposure, imageless navigation system would be suitable.
“In cases where urgent intervention is required, imageless navigation may be easier to use as there is no need to wait for a turnaround for computed tomography (CT) scans to be scheduled and performed. There is always an economic consideration on which type to use as well: imageless navigation systems tend to be less expensive than image-based ones.”
Table 2. Features of image-based and imageless navigation systems
One task that is common to both types of navigation is registration. The surgeon goes through a set sequence of actions unique to each system to manually identify key anatomical landmarks for the computer . Paired with software that helps surgeons digitally plan an optimized procedure, accurate registration is critical to the success of a navigated procedure.
In image-based navigation, a computer creates a 3D volumetric model of the patient’s anatomy using information from preoperative images taken with either x-rays, CT, or magnetic resonance imaging (MRI). Ultrasound images can be obtained before and during a procedure . Fluoroscopy is used to gather 2D or 3D images intraoperatively.
Navigation systems can integrate a combination of these, for example preoperative CT scans with intraoperative fluoroscopy, or use just one whereby the preoperative images are registered against positioning determined by a static marker fixed to a patient. Then, images either taken pre- or intraoperatively are correlated with the patient’s anatomy and/or what tracking cameras “see” during the operation—the process referred to as registration .
Types of imaging:
- Computed tomography (CT): Commonly used in navigation because of its high-resolution images that offer good contrast between bone and soft tissue allowing precise modeling with short scanning times . It is expensive , exposes patients and healthcare workers to ionizing radiation, and some may have adverse reactions to the contrast dyes used . Requires drilling and “rigidly” mounting a tracking device/marker on the patient’s anatomy to infer their position, which is impossible to do for small bones and could prolong recovery .
- Magnetic resonance imaging (MRI): Although it is also expensive , MRI results in higher-resolution images with better soft tissue contrast than CT. No ionizing radiation exposure but the strong magnetic field generated could heat or move existing implants within patients . See Figure 1.
- Ultrasound: With no ionizing radiation exposure for patients and healthcare workers , this 3D image gathering modality showed promise in producing good registration for bone when paired with preoperative CT scans and used in real-time, intraoperatively [49, 50]. However, several technical challenges exist including imaging artifacts and bone surfaces appearing on screen with same intensity as soft tissue interfaces . Does not require mounting a tracking device .
- Fluoroscopy 2D and 3D: has a lower cost than the previously described imaging modalities .
- 2D: C-arms needed to produce these images are readily available for many surgeons and there is less intraoperative radiation exposure than CT scans [47, 51]. However, drawbacks include inferior delineation of anatomical structures , overlapping images of bone , and distorted images .
- 3D: Software constructs 3D volumetric images from up to a hundred 2D C-arm acquired images that are somewhat comparable to CT scans . Some authors have reported this results in higher levels of ionizing radiation exposure when compared to 2D fluoroscopy  but others have determined the exposure to be less . Better image quality than 2D fluoroscopy.
When compared with image-based systems, imageless navigation systems are touted for their reduced time and cost requirements, and some have referred to them as the “gold standard”  for navigation. Less personnel are needed to take intraoperative images, radiation exposure for the surgical team and patient is avoided, and no time needs to be allocated for scanning .
These systems were originally developed for THA and TKA surgery [44, 55]. No images are taken before or during the surgery, instead, the surgeon defines the anatomy by sliding the tip of a specialized instrument over its surface .
Markers (dynamic reference frame [DRF]) that emit infrared light are attached to target a bone and instruments (rigid bodies), and an optoelectrical tracker (camera) monitors their positioning in relation to each other with rapid and precise measurements [44, 53]. Markers are usually grouped in “constellations” of three to six to allow triangulation between them; more markers placed on each rigid body (up to six markers) increases accuracy .
The many data points are synthesized by computer software to generate a virtual model. Joint kinetic information and bone morphology is also collected; some readings do not require surgical access, such as the center of the femoral head landmark which is calculated via the legs’ passive rotation around the acetabulum .
Patient-specific instrumentation (PSI)
Another application of CAS is the creation of single-use PSI or implant components. This was introduced in the 1990s for pedicle screw placement, TKA, decompression of the cervical spine, and triple osteotomy of the pelvis .
In the field of orthopedics, CT or MRI scans are used together with specialized software to develop and manufacture (or 3D print) cutting guides or “jigs” customized to a patient’s anatomy . The idea is that these will help a surgeon perform exact resections that match the surgical plan generated by the software. A 2020 meta-analysis of PSI (cutting blocks) in TKA highlighted reduced blood loss and improved Knee Society Scores (KSS); compared with CT-based PSI, MRI-based PSI was associated with reduced operating time and fewer mechanical axis malalignments .
In discussing his personal experience using PSI in TKA, Ahmed Magan notes that he “found it relatively straight forward to use”, but cautions that “PSI is based on preoperative imaging and cannot be amended intraoperatively to accommodate soft tissue balance. Obtaining correct fit of the cutting guides can be a challenge, and if not applied correctly can cause implant malpositioning, which is sometimes difficult to ascertain intraoperatively.”
In this 4-minute video, Adolph Lombardi Jr, MD, FACS, discusses how patient-specific instrumentation has been used in orthopedic surgery (from healio.com).
However, there have been numerous criticisms of PSI, such as the increased costs related to obtaining adequate imaging and the manufacturing— it can only be used once and is then discarded. One study showed that using PSI can add an additional CAD 1,787 per case . Surgeons are also advised to keep a set of standard instruments available as back-up if the PSI do not work for any reason .
Optimized positioning system™
Lower limb arthroplasty research and development continues at an impressive rate. The improved longevity of implants has led to an interest in further improvement in implant position to match patients’ own anatomy. The Optimized Positioning SystemTM (OPSTM, Corin, UK) merges biomechanical modelling of patients’ movement with PSI, adding a new layer of consideration for arthroplasties—pre-existing functionality.
In recent years, THA implants within the “normal safe zone” have been shown to not be safe for everyone and that spinopelvic motion does not always influence outcome. Stiff spines result in loss of normal spinopelvic mobility that may result in impingement and dislocation . Based on preoperative x-ray and CT images, OPSTM software models/simulates the biomechanical loading of a hip joint as it moves during different daily activities. The consideration of how the femur, spine, and pelvis work together offers unique insight into dynamic relationships that influence optimized placement of implants. All this information is then synthesized to design a 3D-printed patient-specific guide for component placement [63–65]. Although OPSTM is in its infancy, the early data looks promising . OPSTM utilizes PSI and patient-specific dynamic analysis to allow the surgeon to plan and insert acetabular cups with more accuracy.
Virtual and augmented reality
Virtual reality (VR) involves a computer generated 3D environment that a person can view on a screen and interact with in a seemingly realistic or physical manner . It is increasingly being used in training for surgical procedures that can be deconstructed into steps, which a learner can rehearse and be assessed on without exposing a patient to undue risk [68, 69]. Using VR for training has been shown to reduce the operative time and complication rate for specific procedures performed by surgical trainees [66, 70].
Augmented reality (AR) enhances the perception of reality by layering a digital object on top of reality . Called a “disruptive” technology in the medical sphere, AR as a CAS system overlays a projected 3D virtual model over the surgical field , or can show a surgeon images through special lenses or monitors . For example, it can direct the safe corridors of screw placement to the surgeon or guide the surgeon in what instruments they need next .
In 2006, an early AR device was designed that took a near infrared image of veins in a patient then projected it on to the skin in a green light. It identified veins too shallow to be found with ultrasound and those invisible to the naked eye . To illustrate how this technology has evolved, a 2020 publication describes participant surgeons using a head-mounted display called “System for Telementoring with Augmented Reality (STAR)” that connected them in VR to expert guidance to conduct leg fasciotomies on cadavers. Fewer errors, better performance scores, and self-reported higher confidence levels were reported when compared to participants who independently reviewed the procedure with a mentor, indicating the technology could be used to train and support surgeons in remote areas or low-volume centers .
In this video update on the capabilities and features of System for Telementoring with Augmented Reality (STAR), ultrasound, vital sign monitoring, and image stabilization are highlighted for their ability to communicate critical onsite patient information to remote mentors.
While AR has been used in neuro- and visceral surgical fields for some time, a systematic review of studies involving AR in orthopedics, published in 2020, found that AR showed promising results in implant placement, osteotomies, tumor surgery, trauma, and surgeon training/education. The authors of the review concluded: “AR has the potential to be a timesaving, risk and radiation reducing, and accuracy enhancing technology in orthopedic surgery.” 
Reasoning, perception, planning, and learning are characteristics we generally associate with humans. However, as computer science evolves, the field of artificial intelligence (AI) is pushing our understanding and acceptance of what machines can do.
Often called “machine learning”, AI refers to human-like intelligence that machines can mimic. AI techniques are being used in healthcare to solve a wide range of problems such as generating equations to calculate more precise medication dosing , prescreening images and flagging irregularities for radiologists to review , and identifying and optimizing drug combinations to more effectively treat antibiotic resistant conditions . In terms of surgery, experiments with autonomous robot surgeons that make their own decision are being conducted . Another study reported that with a dataset of 129,450 clinical images consisting of 2,032 different skin diseases, a deep neural network learned to diagnose/classify skin cancer with the same diagnostic competence of board-certified dermatologists .
The ability to make millions of calculations in a timeframe almost unimaginable to humans translates into computers being able to identify patterns we might not be easily able to see within huge data sets. This holds interesting potential for huge banks of information, such as joint registries.
Open vs closed CAS robotic systems
In an open platform robotic CAS system, the technology is cross-compatible with more than one manufacturer’s implants. This has the added benefit of surgeons not being limited to a single brand of implant. In contrast, a closed platform robotic CAS system restricts the surgeon to a specific implant brand .
“There needs to be a balance between the two,” says Ahmed Magan. “As technology advances and implant costs come down, it would make sense for surgeons to be able to use their implant of choice for the best outcome for the patient.” See Part 3 of this article series for further discussion of open and closed platforms.
Path of development
Over the last 50 years, computers inside digital medical-grade equipment have increasingly been involved in patient care and monitoring . Some of the first CAS systems to be developed are the descendants of the surgical robots we know today .
Robotics in surgery
The first use of the word “robot” as we understand it is attributed to Czech playwright Karel Čapek and his 1920 play R.U.R. (Rossum’s Universal Robots) [3, 84]. In 1942, science-fiction author Isaac Asimov first used the term “robotics” to describe the study of robots. Perhaps it is the surgical robot that most captures our collective imagination, a machine capable of autonomously performing every medical treatment we need, tirelessly and with a precision not attainable by imperfect humans. While this imagined future of surgical robots is currently out of reach, one report estimates that 5,000 robotic orthopedic surgery units are in use around the world , indicating that we are slowly moving closer to realizing this fantasy.
In the 1950s, the idea of robotic arms that could be controlled remotely and delivered a feeling of being somewhere else was conceptualized by NASA and called “telepresence”. These robotic manipulators were first developed for use in hazardous environments (space, deep sea, contamination) and industrial spaces .
Figure 2. DARPA's original concept for MEDFAST surgical unit, linked by mobile 2-way microwave communication link. © 2018 by JSLS, Journal of the Society of Laparoendoscopic Surgeons. Used with permission under CC BY-NC-ND 3.0 US license. Source: George EI, et al. Origins of Robotic Surgery: From Skepticism to Standard of Care. JSLS. 2018;22(4):e2018.00039
One can easily see how the ability to assess patients, perform surgeries, and monitor recoveries from afar would be of interest to the military. The US-based "Defense Advanced Research Projects Agency" (DARPA), in partnership with several private companies, is credited with developing and testing prototypes of a multipurpose teleoperated robotic system that brought the OR to the patient. See Figure 2.
The "Medical Forward Advanced Surgical Treatment" (MEDFAST) aimed to connect a mobile, remote surgical unit with off-site surgeons who would control robotic surgical equipment via telepresence, assisted by a bedside medic, while the patient was being transported. Despite promising demonstrations in 1993 and 1994, the MEDFAST system was never fully realized due to “political considerations” .
Early surgical robots
The discipline of orthopedics was the first to trial robotic technology in a real OR . A team from Vancouver, Canada, is attributed with the first use of a surgical robot. In 1983, “Arthrobot” responded to “simple voice commands” from a surgeon and would assist with moving a patient’s limbs during orthopedic surgery; the surgeon would perform all aspects of the actual surgical procedure [3, 86, 87].
Watch original 1980s video footage of Arthrobot in action. ©Dr Brian Day.
In 1985, the PUMA 560 integrated CT to insert a needle into a specific part of the brain in a stereotaxic operation to collect a tissue sample for biopsy. The idea was that the smooth movement of a robot would eliminate the slight tremor of a human hand and make needle placement more precise .
Degrees of freedom
When designing surgical robots to replicate procedures, a robot’s range of motion is compared to that of a human arm; the human arm is the most versatile actuator known to man. The three joints in a human arm (shoulder, elbow, wrist) provide “seven degrees of freedom” (DoF)—the different axes about which an object is able to move in 3D space. Terminology, originating in the nautical world, is used to describe DoF :
- Pitch: tilting in the vertical vector (shoulder, elbow, wrist)
- Yaw: turning to the left or right (shoulder, wrist)
- Roll: tilting from side to side (shoulder, wrist)
In 1992, Computer Motion developed the Automated Endoscopic System for Optimal Positioning (AESOP). It was the world’s first surgical robot to be approved by the US Food and Drug Administration (FDA) in 1994. AESOP was controlled by a foot pedal, and later by voice commands, and intraoperatively maneuvered an endoscope, with a laparoscope being added to the ZEUS unit in 1996, which was based on the AESOP platform. Interestingly, ZEUS had the potential to be used in telesurgery and was the robot used in the 2001 transatlantic “Lindbergh Operation”, although it was discontinued in 2003. AESOP was adopted in over 1,000 hospitals .
Compared with a human surgeon, Robodoc was able to more quickly and precisely prepare a femoral cavity to accept a hip replacement in 1993 [12, 87, 89]. A 1998 study reported that in over 900 cementless total hip replacement procedures using the robot, not a single intraoperative femoral fracture occurred . See Figure 3.
Figure 3. The ROBODOC system was comprised of ORTHODOC, a 3D preoperative planning workstation, and ROBODOC surgical assistant, a 5-axis SACARA type surgical robot. Used with permission under CC BY-NC 3.0 license. Source: Sugano N. Computer-assisted orthopaedic surgery and robotic surgery in total hip arthroplasty. Clin Orthop Surg. 2013;5(1):1–9.
Renamed to TSolution-One®, this autonomous robot continues to be used to this day. It incorporates image-based preoperative planning software and is used in ORs around the globe for both hip and knee replacements . It has also been shown to effectively remove all bone cement, without causing femoral fracture during hip revision surgery .
Although it did not receive FDA approval until 2000 , the da Vinci Surgical System [See Figure 4] from Intuitive Surgical also has its roots in the 1990s. It is still in use today, purportedly having performed over 5 million surgeries over 20 years .
Figure 4. DaVinci Xi surgical robot. Image in public domain. Source: Marcy Sanchez.
It is mostly used in prostatectomies, cardiac valve repair, and gynecologic surgical procedures. The current version of the system includes a surgeon console in the same room as the patient and a side cart with four robotic arms (one arm is a 3D camera, the other three are for tools). See Part 2 of this article series for further discussion of CAS currently in use in orthopedics. Figure 5 provides an overview of the history of robots in surgery.
Figure 5. History of robotic surgery. An overview. Used with permission. Source: IDTechEx Report Innovations in Robotic Surgery 2020-2030.
CAS systems encompass a wide range of technology that has been successfully incorporated into many surgical fields, including orthopedics. From planning software and navigation, to PSI and robots, the potential of CAS will continue to evolve as the technology that drives surgery advances. Part 2 of this article series narrows the CAS focus to just orthopedics and takes a closer look at how CAS is currently being used to improve patient outcomes.
This series of articles was created with the support of the following specialists (in alphabetical order):
This issue was created by Word+Vision Media Productions, Switzerland
Additional AO resources on this topic
Access videos, tools, and other assets to learn more about this topic.
- Video: Robotics and Other Smart Tools for Hip and Knee Replacement
- Further reading: The development and validation of a robotic system for sacroiliac luxation/fracture reduction and fixation
- Upcoming events: AO Recon Course finder
- Marino MV, Shabat G, Gulotta G, et al. From Illusion to Reality: A Brief History of Robotic Surgery. Surg Innov. 2018 Jun;25(3):291–296.
- Needham J. Science and Civilisation in China: Volume 2, History of Scientific Thought. Cambridge University Press; 1991.
- Yates DR, Vaessen C, Roupret M. From Leonardo to da Vinci: the history of robot-assisted surgery in urology. BJU Int. 2011 Dec;108(11):1708–1713; discussion 1714.
- Boston Dynamics. SPOT®. https://www.bostondynamics.com/spot. Published 2019. Updated November 5, 2019. Accessed April 10, 2020.
- Kazanzides P. Robots for Orthopaedic Joint Reconstruction. In: Faust R, ed. Robotics in Surgery: History, Current and Future Applications. New York: Nova Science Publishers Inc.; 2006:61–94.
- Jackson DW, Simon TM. History of computer-assisted orthopedic surgery (CAOS) in sports medicine. Sports Med Arthrosc Rev. 2008 Jun;16(2):62–66.
- Picard F, Deakin AH, Riches PE, et al. Computer assisted orthopaedic surgery: Past, present and future. Med Eng Phys. 2019 Oct;72:55–65.
- Wikipedia. Computer-assisted surgery. https://en.wikipedia.org/wiki/Computer-assisted_surgery. Published 2020. Updated March 24, 2020. Accessed April 10, 2020.
- Davies B. A review of robotics in surgery. Proc Inst Mech Eng H. 2000 214(1):129–140.
- Horsley V, Clarke R. The structure and functions of the cerebellum examined by a new method. Brain. 1908 31(1):45–124.
- Jenny JY. [The history and development of computer assisted orthopaedic surgery]. Orthopade. 2006 Oct;35(10):1038–1042.
- Moore E. Robotic surgery. Encyclopædia Britannica, inc. https://www.britannica.com/science/robotic-surgery. Published November 23, 2018. Accessed April 10, 2020.
- Illgen R, Bukowski B, Abiola R, et al. Robotic-Assisted Total Hip Arthroplasty: Outcomes at Minimum Two-Year Follow-Up. Surg Technol Int. 2017 July;25(30):365–372.
- Kayani B, Konan S, Ayuob A, et al. The current role of robotics in total hip arthroplasty. EFORT Open Rev. 2019 Nov;4(11):618–625.
- Selvanayagam R, Kumar V, Malhotra R, et al. A prospective randomized study comparing navigation versus conventional total knee arthroplasty. J Orthop Surg (Hong Kong). 2019 May–Aug;27(2):2309499019848079.
- Bell SW, Anthony I, Jones B, et al. Improved Accuracy of Component Positioning with Robotic-Assisted Unicompartmental Knee Arthroplasty: Data from a Prospective, Randomized Controlled Study. J Bone Joint Surg Am. 2016 Apr 20;98(8):627–635.
- Fucentese SF, Meier P, Jud L, et al. Accuracy of 3D-planned patient specific instrumentation in high tibial open wedge valgisation osteotomy. J Exp Orthop. 2020 Feb 27;7(1):7.
- Sucandy I, Schlosser S, Bourdeau T, et al. Robotic hepatectomy for benign and malignant liver tumors. J Robot Surg. 2020 Feb;14(1):75–80.
- Heunis JC, Cheah JW, Sabnis AJ, et al. Use of three-dimensional printing and intraoperative navigation in the surgical resection of metastatic acetabular osteosarcoma. BMJ Case Rep. 2019 Sep 30;12(9).
- Wang Y, Jin H, Gong S, et al. Efficacy Analysis of Robot-Assisted Minimally Invasive Surgery for Small-Volume Spontaneous Thalamic Hemorrhage. World Neurosurg. 2019 Nov;131:e543–e549.
- Ando K, Kobayashi K, Machino M, et al. Computed tomography-based navigation system-assisted surgery for primary spine tumor. J Clin Neurosci. 2019 May;63:22–26.
- Jorba-García A, Figueiredo R, González-Barnadas A, et al. Accuracy and the role of experience in dynamic computer guided dental implant surgery: An in-vitro study. Med Oral Patol Oral Cir Bucal. 2019 Jan 1;24(1):e76–e83.
- Picard F, Moody J, DiGioia A, et al. Clinical Classification of CAOS Systems. In: DiGioia A, Jaramaz B, Picard F, et al, eds. Computer and Robotic Assisted Hip and Knee Surgery. Oxford: Oxford University Press; 2004:43–48.
- Taylor R. Robotics in Orthopedic Surgery. In: Nolte R, Ganz R, eds. Computer assisted orthopedic Surgery (CAOS). Boston: Hogrefe & Huber Publishers; 1998:35–41.
- Lane T. A short history of robotic surgery. Ann R Coll Surg Engl. 2018 May;100(6_sup):5–7.
- Picard F, Moody J, Jaramaz B, et al. A Classification Proposal for Computer-Assisted Knee Systems. 2000; Berlin, Heidelberg.
- Davies B. A brief review of robotics in surgery. Proc Instn Mech Engrs. 1999 24:129–140.
- Levinson K. Robotic Assisted Surgery. Electrical and Computer Engineering Design Handbook. 2015. https://sites.tufts.edu/eeseniordesignhandbook/. Accessed April 14, 2020.
- Chang JD, Kim IS, Bhardwaj AM, et al. The Evolution of Computer-Assisted Total Hip Arthroplasty and Relevant Applications. Hip Pelvis. 2017 Mar;29(1):1–14.
- Okamura AM. Haptic feedback in robot-assisted minimally invasive surgery. Curr Opin Urol. 2009 19(1):102–107.
- Zamorano L, Li Q, Rhiew R. Robotic Applications in Neurosurgery. In: Faust R, ed. Robotics in Surgery: History, Current and Future Applications. New York: Nova Science Publishers Inc; 2006:147–172.
- Choi PJ, Oskouian RJ, Tubbs RS. Telesurgery: Past, Present, and Future. Cureus. 2018 May 31;10(5):e2716.
- Marescaux J, Leroy J, Gagner M, et al. Transatlantic robot-assisted telesurgery. Nature. 2001 Sept;413(6854):379–380.
- George EI, Brand TC, LaPorta A, et al. Origins of Robotic Surgery: From Skepticism to Standard of Care. JSLS. 2018 Oct–Dec;22(4).
- Wikipedia. Remote surgery. Wikipedia. https://en.wikipedia.org/wiki/Remote_surgery. Published 2019. Updated November 25, 2019. Accessed April 18, 2020.
- Marescaux J, Leroy J, Rubino F, et al. Transcontinental robot-assisted remote telesurgery: feasibility and potential applications. Ann Surg. 2002 Apr;235(4):487–492.
- Victor J, Hoste D. Image-based computer-assisted total knee arthroplasty leads to lower variability in coronal alignment. Clin Orthop Relat Res. 2004 Nov;(428):131–139.
- Lang JE, Mannava S, Floyd AJ, et al. Robotic systems in orthopaedic surgery. J Bone Joint Surg Br. 2011 Oct;93(10):1296–1299.
- Sugano N. Computer-assisted orthopaedic surgery and robotic surgery in total hip arthroplasty. Clin Orthop Surg. 2013 Mar;5(1):1–9.
- Venkatesan M, Mahadevan D, Ashford RU. Computer-assisted navigation in knee arthroplasty: a critical appraisal. J Knee Surg. 2013 Oct;26(5):357–361.
- Sikorski J, Chauhan S. Computer-assisted orthopaedic surgery: Do we need CAOS? The Journal of bone and joint surgery British volume. 2003 05/01;85:319–323.
- Mezger U, Jendrewski C, Bartels M. Navigation in surgery. Langenbecks Arch Surg. 2013 Apr;398(4):501–514.
- Zheng G, Nolte LP. Computer-Assisted Orthopedic Surgery: Current State and Future Perspective. Front Surg. 2015 2:66.
- Kubicek J, Tomanec F, Cerny M, et al. Recent Trends, Technical Concepts and Components of Computer-Assisted Orthopedic Surgery Systems: A Comprehensive Review. Sensors (Basel). 2019 Nov 27;19(23).
- Hacihaliloglu I. Ultrasound imaging and segmentation of bone surfaces: A review. Technology (Singap World Sci). 2017 Jun;5(2):74–80.
- US Food & Drug Administration. Computed Tomography (CT): Risks and Benefits. https://www.fda.gov/radiation-emitting-products/medical-x-ray-imaging/computed-tomography-ct#3. Published 2019. Updated June 14, 2019. Accessed April 18, 2020.
- Chen TK, Abolmaesumi P, Pichora DR, et al. A system for ultrasound-guided computer-assisted orthopaedic surgery. Comput Aided Surg. 2005 Sep-Nov;10(5-6):281–292.
- US Food & Drug Administration. MRI (Magnetic Resonance Imaging) Benefits and Risks. https://www.fda.gov/radiation-emitting-products/mri-magnetic-resonance-imaging/benefits-and-risks. Published 2017. Updated September 12, 2017. Accessed April 18, 2020.
- Beitzel J, Ahmadi SA, Karamalis A, et al. Ultrasound bone detection using patient-specific CT prior. Conf Proc IEEE Eng Med Biol Soc. 2012 2012:2664–2667.
- Wein W, Karamalis A, Baumgartner A, et al. Automatic bone detection and soft tissue aware ultrasound-CT registration for computer-aided orthopedic surgery. Int J Comput Assist Radiol Surg. 2015 Jun;10(6):971–979.
- Akins R, Abdelgawad AA, Kanlic EM. Computer navigation in orthopedic trauma: safer surgeries with less irradiation and more precision. J Surg Orthop Adv. 2012 Winter;21(4):187–197.
- Thakkar SC, Thakkar RS, Sirisreetreerux N, et al. 2D versus 3D fluoroscopy-based navigation in posterior pelvic fixation: review of the literature on current technology. Int J Comput Assist Radiol Surg. 2017 Jan;12(1):69–76.
- Saragaglia D, Rubens-Duval B, Gaillot J, et al. Total knee arthroplasties from the origin to navigation: history, rationale, indications. Int Orthop. 2019 Mar;43(3):597–604.
- Brin YS, Nikolaou VS, Joseph L, et al. Imageless computer assisted versus conventional total knee replacement. A Bayesian meta-analysis of 23 comparative studies. Int Orthop. 2011 Mar;35(3):331–339.
- Liu Z, Gao Y, Cai L. Imageless navigation versus traditional method in total hip arthroplasty: A meta-analysis. Int J Surg. 2015 Sep;21:122–127.
- Kowal J, Langlotz F, Nolte L. Basics of Computer-Assisted Orthopaedic Surgery. In: Stiehl J, Konermann W, Haaker R, et al., eds. Navigation and MIS in Orthopedic Surgery. Germany: Springer Medizin Verlag Heidelberg; 2007:2–8.
- Docquier PL, Paul L, TranDuy K. Surgical navigation in paediatric orthopaedics. EFORT Open Rev. 2016 May;1(5):152–159.
- Haglin JM, Eltorai AE, Gil JA, et al. Patient-Specific Orthopaedic Implants. Orthop Surg. 2016 Nov;8(4):417–424.
- Lin Y, Cai W, Xu B, et al. Patient-Specific or Conventional Instrumentations: A Meta-analysis of Randomized Controlled Trials. Biomed Res Int. 2020 2020:2164371.
- Teeter MG, Marsh JD, Howard JL, et al. A randomized controlled trial investigating the value of patient-specific instrumentation for total knee arthroplasty in the Canadian healthcare system. Bone Joint J. 2019 May;101-b(5):565–572.
- Lachiewicz PF, Henderson RA. Patient-specific instruments for total knee arthroplasty. J Am Acad Orthop Surg. 2013 Sep;21(9):513–518.
- Rivière C, Lazennec JY, Van Der Straeten C, et al. The influence of spine-hip relations on total hip replacement: A systematic review. Orthop Traumatol Surg Res. 2017 Jun;103(4):559–568.
- Corin Group. OPS: Optimised Positioning System. Corin Group. https://www.coringroup.com/uk/solutions/optimized-positioning-system-ops/. Published 2020. Accessed April 19, 2020.
- Florida Medical Clinic. OPS-Optimized Positioning System. https://www.floridamedicalclinic.com/fmchipdoctor/ops-optimized-positioning-system/. Published 2020. Accessed April 19, 2020.
- Good Design Australia. OPTIMIZED POSITIONING SYSTEM OPS™. https://good-design.org/projects/optimized-positioning-system-ops/. Published 2015. Accessed April 19, 2020.
- Shimmin A, Pierrepont J, Bare J, et al. Early results of the CORIN optimized positioning system: A registry analysis of 841 consecutive total hip arthroplasties. Orthopaedic Proceedings. 2019;102-B(Supp_1). https://online.boneandjoint.org.uk/doi/abs/10.1302/1358-992X.2020.1.083. Published February 5, 2020. Accessed April 25, 2020.
- Bartlett JD, Lawrence JE, Stewart ME, et al. Does virtual reality simulation have a role in training trauma and orthopaedic surgeons? Bone Joint J. 2018 May 1;100-b(5):559–565.
- Collins JP. International consensus statement on surgical education and training in an era of reduced working hours. Surgeon. 2011 9 Suppl 1:S3–5.
- Sevenoaks H, Ajwani S, Hujazi I, et al. Shift working reduces operative experience for trauma and orthopaedic higher surgical trainees: a UK multicentre study. Ann R Coll Surg Engl. 2019 Mar;101(3):197–202.
- Logishetty K, Rudran B, Cobb JP. Virtual reality training improves trainee performance in total hip arthroplasty: a randomized controlled trial. Bone Joint J. 2019 Dec;101-b(12):1585–1592.
- Wikipedia. Augmented reality. https://en.wikipedia.org/wiki/Augmented_reality. Published 2020. Updated April 18, 2020. Accessed April 19, 2020.
- Jud L, Fotouhi J, Andronic O, et al. Applicability of augmented reality in orthopedic surgery - A systematic review. BMC Musculoskelet Disord. 2020 Feb 15;21(1):103.
- Vadalà G, De Salvatore S, Ambrosio L, et al. Robotic Spine Surgery and Augmented Reality Systems: A State of the Art. Neurospine. 2020 Mar;17(1):88–100.
- Verhey JT, Haglin JM, Verhey EM, et al. Virtual, augmented, and mixed reality applications in orthopedic surgery. Int J Med Robot. 2020 Apr;16(2):e2067.
- Miyake RK, Zeman HD, Duarte FH, et al. Vein imaging: a new method of near infrared imaging, where a processed image is projected onto the skin for the enhancement of vein treatment. Dermatol Surg. 2006 Aug;32(8):1031–1038.
- Rojas-Muñoz E, Cabrera ME, Lin C, et al. The System for Telementoring with Augmented Reality (STAR): A head-mounted display to improve surgical coaching and confidence in remote areas. Surgery. 2020 Apr;167(4):724–731.
- Zarrinpar A, Lee DK, Silva A, et al. Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform. Sci Transl Med. 2016 Apr 6;8(333):333ra349.
- Koo CW, Anand V, Girvin F, et al. Improved efficiency of CT interpretation using an automated lung nodule matching program. AJR Am J Roentgenol. 2012 Jul;199(1):91–95.
- Clemens DL, Lee BY, Silva A, et al. Artificial intelligence enabled parabolic response surface platform identifies ultra-rapid near-universal TB drug treatment regimens comprising approved drugs. PLoS One. 2019 14(5):e0215607.
- Shademan A, Decker RS, Opfermann JD, et al. Supervised autonomous robotic soft tissue surgery. Sci Transl Med. 2016 May 4;8(337):337ra364.
- Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017 Feb 2;542(7639):115–118.
- Lonner J. Robotics in Knee and Hip Arthroplasty: Current Concepts, Techniques and Emerging Uses. Springer Nature Switzerland AG; 2019.
- Martinez D. 5 Reasons Why Medical Grade Computers Matter in the Hospital. TechnologyAdvice. https://technologyadvice.com/blog/healthcare/medical-grade-computers/. Published March 22, 2018. Accessed April 10, 2020.
- Andrews S. Robot means “serfdom” in Czech and Asimov was the first to use the word “robotics”. The Vintage News. https://www.thevintagenews.com/2017/01/04/robot-means-serfdom-in-czech-and-asimov-was-the-first-to-use-the-word-robotics/. Published 2017. Updated January 4, 2017. Accessed April 10, 2020.
- Ranev D, Teixeira J. History of Computer-Assisted Surgery. Surg Clin North Am. 2020 Apr;100(2):209–218.
- Lechky O. World's first surgical robot in B.C. The Medical Post. November 12, 1985.
- Rahman J, Al-Tawil K, Khan W. Use of Robotic-Assisted Surgery in Orthopedics. In: Iyer K, Khan W, eds. General Principles of Orthopedics and Trauma: Springer; 2019.
- Hillel AT, Kapoor A, Simaan N, et al. Applications of robotics for laryngeal surgery. Otolaryngol Clin North Am. 2008 Aug;41(4):781–791, vii.
- Kyberd P. Technology: Robodoc carves a place in medical history. NewScientist. https://www.newscientist.com/article/mg13618492-600-technology-robodoc-carves-a-place-in-medical-history/. Published November 28, 1992. Accessed April 10, 2020.
- Bargar WL, Bauer A, Borner M. Primary and revision total hip replacement using the Robodoc system. Clin Orthop Relat Res. 1998 Sep;(354):82–91.
- Liow MHL, Chin PL, Pang HN, et al. THINK surgical TSolution-One((R)) (Robodoc) total knee arthroplasty. SICOT J. 2017 3:63.
- Yamamura M, Nakamura N, Miki H, et al. Cement Removal from the Femur Using the ROBODOC System in Revision Total Hip Arthroplasty. Adv Orthop. 2013 2013:347358.
- Wikipedia. da Vinci Surgical System. https://en.wikipedia.org/wiki/Da_Vinci_Surgical_System. Published 2020. Updated April 16, 2020. Accessed April 19, 2020.