Computer-assisted surgery (CAS)
Computer-assisted surgery: Looking to the future
It can take 20 years for a new medical technology to mature through the various development stages. In a way, today’s “cutting-edge” technology reflects the ideas of yesterday. Part 3 gazes into the future of CAS. Will virtual reality rule the operating room? Will there still be a need for a human surgeon?
Computer-assisted surgery (CAS) systems, such as navigation and surgical robots, are currently available in select operating rooms (ORs) around the world. Part 1 introduced the terminology used in CAS and the different types of CAS technology being used. Part 2 looked at the evidence for and against the use of computer-assisted orthopedic surgery (CAOS) systems, concluding that more studies—particularly related to long-term clinical outcomes—are needed to determine the impact of these technologies beyond the documented improved surgical/implant placement accuracy, especially in terms of reconciling costs.
So what direction is technology taking the field of CAS in orthopedics?
“The next frontier in hip and knee surgery may include advancements in joint preservation, soft tissue reconstruction, tissue engineering, and training,” says Mark Roussot, MBChB MPhil, MMed, FC Orth, of the University College London Hospital, Department of Trauma and Orthopaedics.
Georges Vles, MD, PhD, from Belgium’s University Hospitals Leuven, Division of Orthopedic Surgery thinks that, “For me as a hip surgeon, I think the next frontier would be robot-assisted CAM resection. For knees, the logical next steps would be osteotomies (getting a perfect angle and always leaving the hinge intact) and ACL [anterior cruciate ligament] reconstruction (bone tunnels and graft preloading and tensioning).”
Let us look at some of the enduring challenges in orthopedics that have great potential to be resolved with the CAS systems of the future.
Virtual reality (VR) training for surgeons is still in its infancy and the financial investment of a VR simulation system can be significant, depending on its complexity.[1,2] Mark Roussot points out that, “Virtual reality, augmented reality, and procedures performed with CAS/robots on cadavers provide safe environments for trainees to rehearse procedural steps, become accustomed to the technology, and learn about the nuances of achieving a well-aligned, well-balanced, and stable joint replacement. Like other industries, simulation-based training will become increasingly utilized.”
One concern that has been raised around computer-based training is the establishment of proficiency standards. If, after a certain number of attempts or time, simulation training ends, it does not take into account the large variation in attempts individuals take to learn a procedure. See Figure 1. To be effective, VR training should differentiate between user experience levels, then tailor the training accordingly. Interestingly, how precisely a simulation replicates what it is modeling does not necessarily produce superior training outcomes—a low-fidelity laparoscopy simulator was shown to be superior to a comparable high-fidelity simulator in skills transfer to the OR.
Augmented reality (AR) is another modality that has potential to train surgeons and offer guidance during procedures. AR incorporates dynamic VR elements and merges them in real time within a user’s perceived visual environment. For example, it might allow a local surgeon to see an image of a remote surgeon’s hands within their field of view during an actual surgery, or images of a patient’s hidden anatomy superimposed over their field of view. See Figure 2.
Use of AR for surgeon training is not widespread, however, the AR interface has been explored in more depth in relation to distance medicine.
Providing high-quality medical care and specialist consultation to remote communities has several challenges, including high costs and inconvenience related to travel. Real-time video conferencing between geographically separated orthopedic specialists and patients was shown to offer cost savings[6,7] and access to care otherwise not easily found. A 2019 study allowed general practitioners to evaluate the cases of highly rural patients collaboratively with remote orthopedic specialists, allowing nearly 70% of patients’ issues to be resolved during this first evaluation. Ultimately, for those who required a referral, the wait time was reduced from 201 to 40 days.
It has been predicted that through harnessing the strengths of AR technology, telemedicine, and surgical robotics, it will one day be safe and possible for surgeons to operate on a patient from whom they are physically separated. This “telepresence” was shown to successfully facilitate remote instruction of complex surgical procedures in cadavers. In concept, it is well suited to remote teaching but not widely used at present, even though in 2001 the first complete distance surgery was completed with the patient in France and the surgeon in the US.
Garcia et al reported on their Trauma Pod project and showed, among other features, that a surgeon was able to successfully perform a bowel anastomosis and an iliac shunt on a remote patient phantom using teleoperation while being supported by autonomous robotic arms that carried out scrub-nurse and circulating-nurse functions.
However, surgery from a distance carries risks and challenges that need to be solved to enable wider adoption. A few requirements for this technology to be successful include: stable, uninterrupted, high-speed internet connection; reliable power sources on both ends; and a back-up plan for human intervention if required. In addition, security of the connection is paramount—it is not inconceivable that hackers could hijack a robot’s programming and take control of a remote procedure.
Rehabilitation video games
Patient nonadherence to physiotherapy is a barrier to recovery. Computer-assisted rehabilitation offers a way to provide quantifiable measurement of improvement and there is evidence that well designed, video game-based rehabilitation can trigger reward-related dopamine systems in the brain and can motivate patients towards longer, more intensive training sessions.[15,16] However, most robotic rehabilitation research has been focused on neurorehabilitation for patients with central nervous system lesions and not orthopedics. See Figure 3.
Knee ligament reconstruction
Since the 1990s, surgeons have used navigation in ACL reconstruction. It can assist with drill hole placement and in quantifying instability in the sagittal, coronal, and axial planes. Robotic systems have also been used for knee ligament reconstruction, although clinical outcome improvements have yet to be identified. They have been shown to improve the accuracy of tunnel placement, which contributes to achieving stability in the joint. Planning tunnel placement for multiligament reconstructions and ligament reconstructions that are combined with osteotomies would be well suited to the use of robotics, but this has not yet been reported.
In addition, Mark Roussot feels that, “CAS and robotics could play a valuable role in achieving the optimum tunnel position for knee ligament reconstruction, especially for more challenging scenarios such as multiligament reconstructions or a ligament reconstruction combined with an osteotomy.” At a more microscopic level, tissue engineering technologies that are capable of 3D printing osteochondral grafts are also developing.[21,22]
There is also the potential to use robotics, which have the benefit of exact repeatability of motion and force, to measure stability and kinematics intraoperatively. This feature has been used to quantitatively compare the effectiveness of different operative techniques experimentally.[23,24] (See Figure 4 and Figure 5.)
Despite navigation being used in osteotomies around the knee, and shown in cadaver and clinical studies to enhance accuracy of the corrective surgery while limiting the intraoperative radiation exposure for patients and OR personnel, it is associated with higher costs and operative time which in turn has limited the adoption of the technology for this use. Neither has a meta-analysis determined if navigated osteotomies result in improved clinical outcomes or survivorship.
Long-term comparative studies are needed in addition to reporting on sagittal and axial plane measurement reliability; coronal correction is more often the reported parameter. Navigation in osteotomies can be regarded as a sophisticated intraoperative goniometer (an instrument that either measures an angle or allows an object to be rotated to a precise angular position).
Robot-assisted osteotomy has the potential, because of the precision of the robot’s movements, to guide surgeons through their planned procedure and reduce the risk of soft tissue injury. By helping to control the coronal, sagittal, and axial plain correction, robot-assisted osteotomies could make the combination of osteotomy and ligament reconstruction simpler.
Periacetabular osteotomies (PAO) are perfectly suited to the use of computer assistance and robotics. Both image-based and imageless navigation for PAO have been described,[28,29] one of the first in 1997. Although few comparative studies exist, advocates suggest these systems improve surgical planning, increase the accuracy of correction, and aid in the learning curve. A 2019 cadaver study of 3D printed patient-specific implants (PSI) for use in PAO showed accurate fit to bone and precise positioning of the osteotomies which resulted in acceptable corrections when compared to what had been planned.
As discussed in Part 1, preoperative planning using images, most commonly computed tomography (CT), is largely standard in orthopedics. When coupled with the dynamic 3D analysis that planning software offers, it has become possible for surgeons to model a procedure, alter variables, generate hypothetical outcomes, then move into the OR with a digital plan to compare against in real-time.
However, intraoperatively, correction must be applied to compensate for the differences in imaging modalities when, for example CT is used for preoperative planning and these images are compared with on-table supine fluoroscopy, which is prone to distortion. See Figure 7. It is a challenging endeavor and takes a very experienced surgeon to achieve a suitable correction. The correction is performed in the supine position, but this is only one of the functional positions of the pelvis and may be altered by muscle relaxation during anesthesia, making it challenging for the surgeon to determine the appropriate correction intraoperatively.
Robotics, together with artificial intelligence (AI), has the potential to address this problem. It would require the development of an application with the capacity to evaluate pelvic and spinopelvic parameters and incorporate dynamic x-rays in different functional positions (eg, supine, sitting, standing), comparing these factors with preoperative plans and intraoperative corrections, to build an understanding of what an appropriate correction for each individual would be. However, this type of application is not associated with the use of implants and therefore is much less likely to receive resources for research and development.
There are currently several applications that provide analysis of intraoperative fluoroscopy to correct for distortion and measure angles such as lateral center-edge angle and anterior wall index in order to assist the surgeon in monitoring the parameters of the correction.
“Our understanding of femoroacetabular impingement (FAI) and its contribution to early onset osteoarthritis has developed tremendously over the last decade,” says Georges Vles. “However, we are not achieving the results we would like because we are trying to fix a complex 3D problem by means of a 2D procedure. Both over- and under-resection have significant consequences.”
Cam resection for FAI using navigation has been attempted with some success, but not without drawbacks, such as increased operative time and radiation exposure. See Figure 8. With this procedure there is a fine line between adequate resection to prevent impingement and excessive resection that may risk complication, such as fracture. We already have the capacity to evaluate preoperative CT scans dynamically and robot-assisted cam resection has been performed successfully. However, it is still cumbersome to use and will require development of purpose-designed instrumentation.
“It is my hope that in the near future it will be standard practice to preoperatively define the zone of impingement in a kinematic 3D model, plan the bony resection based on a virtual impingement-free range of motion, and execute this planned resection semi-automatically and with high-precision,” remarks Georges Vles. “I am convinced this will lead to better outcomes for our patients and maintain the enthusiasm for FAI surgery.”
Robotic assistance in spine surgery has predominantly focused on helping the surgeon to attain the correct trajectory for pedicle screws, and there is evidence that robotics is at least comparable, if not superior to manual technique in terms of accuracy, however, total operative time is prolonged and radiation exposure is variable.[36,37] Even with robotic assistance for screw trajectory, drilling pathways can be altered by soft tissue pressures, forceful surgical application, and bony surface skiving. Actual placement of the pedicle screw is still performed by the surgeon, and robotics has not yet been used to aid in deformity correction.
PSI and navigation have been used for shoulder arthroplasty to reduce the risk of glenoid malplacement, which can lead to instability, poor function, and early loosening. Currently, the advantage over standard instrumentation seems to be marginal, and there are no long-term comparative studies. Glenoid exposure is an art, and additional navigation instrumentation can make the procedure more cumbersome. PSI accuracy appears to diminish with greater degrees of glenoid deficiency, which is when accuracy is most required.
Much of the success of shoulder arthroplasty relies on the integrity of the soft tissue, which navigation in its current form does not improve.
Computer navigation has been used successfully for tumor surgery and increases the precision of bony resection aimed at achieving a sufficient tumor free margin while minimizing disease-free bone loss. However, recurrence rates are also influenced by the adequacy of soft tissue margins, and are therefore similar when compared to non-navigated resections.
Machine learning (or AI) is characterized by the ability to mimic human-like cognitive functions such as learning and problem solving, and is also proving to be extremely useful in many applications. A study published in January 2020 described a novel process that combines advanced imaging with AI training to diagnose brain tumor tissue intraoperatively in under 3 minutes. See Figure 9. It demonstrated accuracy equal to that of a pathologist and would facilitate intraoperative tumor tissue diagnosis with unprecedented speed, allowing surgeons to differentiate between tumor tissue they cannot distinguish visually and the healthy tissue that surrounds it on a cellular level without need of a pathology laboratory.
Trauma: Fracture reduction
Robotics have the potential to help overcome several bottlenecks associated with traditional fracture surgery, including trial and error reductions, multiple attempts of entry points, freehand fixations, and suboptimal trajectories of screws and nails with perforation of cartilage or neurovascular structures.[44–47]
One of the most common and refined applications in trauma surgery involves sacroiliac screw placement in posteriorly unstable pelvic ring fractures. A prospective randomized comparison by Wang et al, showed superior accuracy, decreased intraoperative radiation exposure, and less guidewire attempts for the robot-assisted group compared with the conventional group, while the surgical time remained the same. Similar results have been shown in case-control studies for robot-assisted percutaneous screw fixation of femoral neck fractures[49,50] and cephalomedullary nail fixation of intertrochanteric fractures. Liu et al showed excellent results of robot-assisted, computer navigated percutaneous fixation of ten nondisplaced scaphoid waste fractures with all patients requiring only a single guidewire insertion attempt with no screw protuberances or other complications.
Although still a long way off from clinical use, several research groups are exploring the possibilities of robot-assisted fracture reduction, especially for femoral fractures. See Figure 10. Creating software that automatically deducts both the desired end-position of fracture fragments as well as the optimal reduction path from preoperative CT scans seems feasible. Accurate and consistent postreduction alignment has been shown in saw-bone and cadaveric studies.[55–57] Nevertheless, meeting the high loading demands of long bone fracture reduction, while keeping the options open for definitive fixation, remains a significant challenge for computer-assisted technology in the future.
Hip and knee arthroplasty
One of the challenges that robot-assisted total hip arthroplasty (THA) and total knee arthroplasty (TKA) face is that the ideal implant position is not yet fully understood.
THA—building a database: With regards to the hip, it is usually straightforward to plan the restoration of length and offset. However, achieving the appropriate acetabular position and femoral anteversion requires an appreciation for the dynamic hip motion, spinopelvic morphology, and alignment (including rotational alignment) of the entire lower limb. These factors are not routinely incorporated in the preoperative plan in most practices.
However, the use of robotic assistance that requires pre-operative CT image acquisition may provide a large database of pathological and reconstructed alignment. See Figure 11. Institutions that include advanced postoperative imaging such as CT evaluation (whether for research purposes or otherwise) will be well positioned to contribute to the understanding of optimal position when this is correlated with subjective and objective outcomes.
TKA—soft-tissue balancing: For TKA, there is much debate about the correct limb alignment and implant position. For example, traditional goals of achieving neutral alignment with orthogonal bone resections or anatomical bone resection have been challenged by the philosophies of kinematic alignment and functional alignment. It takes an experienced surgeon to appreciate and establish the soft tissue balance and tracking in a knee that would be suitable for a patient, and to dynamically test the stability intraoperatively. See Figure 12. There are now ways of evaluating this quantitatively using intracompartmental pressure sensors such as this single-use, wireless product that is compatible with several implant brands. Professor Fares Haddad has been instrumental in demonstrating that these modern surgical aids can be used to reconstruct the functional alignment of the knee that is specific to the individual and still maintained within the acceptable safe zones of alignment.
Future evolution of CAS
Mark Roussot and Georges Vles suggest that the future evolution in CAS is likely to progress in three ways:
- The technologies themselves will evolve and be refined so that the large, cumbersome machine used today can progressively be replaced by one that takes up less space in the OR, is capable of exchanging instruments quickly and efficiently, can recognize a patient’s anatomy with a faster registration process, and does not require extra incisions with bone pins in order to monitor the position of the patient.
- Computer-assisted technologies will be integrated with other technologies to provide more dynamic evaluation of a procedure and implant position.
- The use of machine learning and AI will become an essential part of planning and a procedure. Optimal alignment and soft tissue balance will be determined based on the accumulation of data from previous procedures that have been correlated with outcomes. A CAS of the future would recommend the appropriate alignment and implant position for each patient that would ensure the best chance of a good outcome.
In fact, these advances are not only imaginable, but they already exist in various forms. For example, pinless navigation has shown acceptable accuracy, robots can change instruments and perform soft tissue surgery, and AI is being used in diagnostic imaging.[62,63]
Question: What functionality would you like to see in surgical robots of the future?
Currently, the robot used most in orthopedics is physically large and limits the ability to have assistants/trainees close to the wound. I would like to see technology developed to avoid optical tracking and. “line of sight” issues. Improved dynamic measurement of soft tissue balance throughout range of motion (laxity profile) is needed and accurate registration performed without the need for pre-op CT scans would reduce costs and patient trips.
Looking forward, I think technological developments that would allow robots to better protect soft tissue, compared with standard instrumentation, would really improve patient outcomes. This is functionality I would like to see soon. But, if I were going to really dream about my ideal surgical robot, I’d ask for the possibilities of telemedicine and telesurgery to grow to the point that I could perform remote surgery. This would be fantastic!
The robot should have hand-held controls to let the surgeon navigate without a technician needing to click buttons, have a faster registration process, assist with limb position and stabilization, switch instruments with ease, and rapidly self-calibrate between instrument changes (it is currently a time-consuming manual process). I would also like to see the robots export procedural records to the electronic patient record or joint registry and have wireless connectivity.
I would like to see a robot that does not rely on bone pins that require separate incisions, uploads data gathered on every procedure to a master database, and provides a summary at the end of the procedure of time spend per phase as well as learning points/progress compared to previous procedures. These are just a few of the features I would like to see in the surgical robots of the future.
Outside the box thinking: collaboration to drive change
As we have witnessed in the COVID-19 outbreak, engineering companies have enormous capacity for reinvention when the demand is great enough. See Figure 13. If companies like AMG-Mercedes, Yamaha, Apple, or Microsoft, who already have decades of experience with robotics and AI start to manufacture robots with surgical capacity, their functionality and economy has the potential to advance exponentially.
Open and closed systems
At present, CAS systems are mostly closed, in other words, if you want to perform a robot-assisted orthopedic procedure, you have to use a specific robotic that uses a specific implant or brand of implants, and the planning process can take weeks.
Some have pointed out that the complexity of CAS technology and the proprietary nature of each machine’s programming and implant incompatibility has limited the translation of new ideas into clinical use. Being able to easily bridge, for example, a surgical robot with a new imaging platform could hasten the implementation of new applications by integrating the engineering of both fields. There are researchers who are working on compatibility software interfaces and suggesting common robot software (eg, Open Core Control) to enable this.
One surgical robot called RAVEN has an open source software platform (RAVEN II) to allow universities and/or research centers to contribute to the evolution of robotic surgery. It has been placed in 16 research centers around the world. The user “community is united in the application and support of a common platform, and each member has the power to pursue and develop their own intellectual property.” It allows the community of users to make improvements to the core robotic manipulator, which are then made available to the entire group. Users can then pursue their own proprietary advances in procedures, attached instruments, supervisory software, and human/machine interfaces. Aspects of knee arthroscopy and chon
Cross-discipline integration of technological advancements is on the horizon. For example, tissue engineering techniques (3D printing osteochondral tissue) have been applied to mouse and rabbit models and have shown promising preliminary results in terms of neocartilage formation, osteochondral integration, and a smooth cartilage cap in 3D printed cartilage cells implanted into a rabbit knee.[70–72] Integration with modern imaging provides the potential for individualized osteochondral reconstruction.
Machine communication and interaction
Currently, most ORs contain many machines requiring human operators for monitoring. The need to integrate medical systems and software solutions into a holistic clinical infrastructure is growing. Envisioning an OR of the future where these machines, including CAS systems, are interconnected and communicating with each other will require the development of platforms that are, at least to some extent, open-source.
Imagine if a machine was capable of simultaneously measuring and recording temperature, controlling theatre ventilation, administering anesthesia, monitoring vitals, and tracking operative time and parameters that we may not be aware of, or sense ourselves. These specifications could be met with AI technology of the future. “There is opportunity, says Georges Vles, “for a surgical robot to feed information to an anesthesiologist and the OR floor manager. Then they could understand what phase of the operation we are in, adjust their anesthetic or send for the next patient.”
Robots will need to occupy a smaller footprint within the OR and have the sensing capacity to avoid collisions with people and other equipment in a dynamic manner. Automation of low-level tasks, such as changing, cleaning, or packing instrumentation, could be performed quickly by a robot.
“A simple but big step in the right direction in integrating technologies would be to identify what data can be measured before, during, or after a joint replacement that may provide insight into the best way of performing the procedure. Presently, our main metrics are radiological alignment and patient reported outcomes or revision rates,” commented Mark Roussot.
Who determines the parameters?
The speed of computing and its ability to parse and evaluate reams of data much faster than the human brain holds powerful potential. However, it should be kept in mind that more data is not necessary better, and sticky questions arise about who decides what the “perfect” surgery or surgical parameters might be. The regulation and protection of data lag considerably behind the advancement in data capturing. CAS/robotics will generate an almost infinite amount of data and Georges Vles cautions that “at all costs, we should prevent the wrong data from being used to determine success.
“We as surgeons should take the lead in making sure success is measured by subjective outcomes that are important to the patient as well as objective measures important to the surgeon while improving efficiency and minimizing costs.”
“Absolutely,” affirms Mark Roussot, “it will be essential to involve all the key stakeholders in determining what data is captured, how it is used, and who has access to it.”
Roussot feels that including the patient in these discussions and using metrics that optimize patient care is important. He thinks that success needs to be measured with “evidence-based recommendations for the best objective and subjective (patient-reported) metrics. Surgeons, or councils of surgeons, will also need to evaluate the impact that these metrics and access to data will have on our practice.”
Will robots make surgeons irrelevant?
By 2019, 1.4 million new industrial robots were predicted to be in operation, raising the total to 2.6 million worldwide.[75,76] Always a hot-button topic when it comes to robots and automation is the potential for job loss as tasks move from human workers to machines. Georges Vles notes that, “most surgeons fear becoming redundant when they first become aware of these new innovations.”
However, robots may not be the threat they appear to be at first glance, especially to the realm of medicine and its highly educated practitioners. “Even the best AI and robotic systems produced by the most respected and experienced companies have flaws that humans identify and correct,” says Mark Roussot. “Early attempts at autonomous surgical robots demonstrated how dangerous they can be for a patient.”
“Exactly,” adds Vles, “given our poor results with fully automatic robots in the past, for example robots broaching the femoral canal in an autonomic manner, semi-automatic robots are here to stay, at least for the foreseeable future.”
Mark Roussot and Georges Vles recommend listening to Professor Fares Haddad’s perspective on the future relationship between robots and surgeons. In an interview with CNBC, Professor Haddad asserted that robots will not replace surgeons, but they are capable of making surgeons better at the surgery they perform.
When speaking with media outlet GeekWire, Blake Hannaford, one of the leaders of the open-platform surgical robot RAVEN reaffirmed this when he said that the point of robotic surgery is to expand “the capabilities of human surgeons…and increas[e] the precision of the treatment.”
The World Bank Group put it this way in its 2019 World Development Report, The Changing Nature of Work:
The growing role of technology in life and business means that all types of jobs (including low-skill ones) require more advanced cognitive skills. The role of human capital is also enhanced because of the rising demand for sociobehavioral skills. Jobs that rely on interpersonal interaction will not be readily replaced by machines. However, to succeed at these jobs, sociobehavioral skills—acquired in one’s early years and shaped throughout one’s lifetime—must be strong. Human capital is important because there is now a higher premium on adaptability.
Mark Roussot believes that undertakings requiring adaptability, empathy, common sense, and creativity, such as orthopedic surgery, will continue to need human involvement. If anything, tasks will become automated, not the roles. CAS technology will assist surgeons to become more accurate and efficient and improve patient outcomes, but in doing so will free up a surgeon’s time to offer more services. Consider the classic examples of bank tellers, ATMs, or commercial airline pilots. He points out that, “AI demonstrates significant value with tasks that are data intensive and require several calculations, precision, and endurance; it has enormous potential within the wide scope of orthopedics.”
Georges Vles encourages us to consider that “with these types of robots, surgical skills remain essential, in an unprecedented way. Altering approaches to accommodate robotic requirements, maintaining surgical flow, interpreting new and extensive data, knowing when and how to convert to conventional procedures, these are just some of the new challenges we as surgeons need to meet.”
There will be a need for orthopedic surgeons for a long time to come, but how they perform their jobs and the tools they use are changing. As advances in other fields are integrated into orthopedics, hopefully, long-term outcomes of navigated and robot-assisted procedures will begin to show clear advantages to traditional procedures. Mark Roussot firmly believes that, “through improvements in preoperative planning, precision, and soft-tissue preservation, computer-assisted technology will be shown to facilitate quicker recoveries with better pain resolution and functional outcomes in the future.”
This series of articles was created with the support of the following specialists (in alphabetical order):
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