The Evolution of Image-Free Robotic Assistance in Unicompartmental Knee Arthroplasty

The concept of robotics is relatively new in medical practice. The term “robot” itself is less than 100 years old, having been first introduced to popular culture in 1917 by Joseph Capek in the science fiction story Opilec.^1,2 Robots eventually transitioned from this initial fictional literary setting to reality in 1958, when General Motors began adding automated machines to its assembly lines.¹ However, it was not until the 1980s that robotics and their exacting efficiencies would be introduced in the medical field, and it would take another decade before they would enter the specialty of orthopedics.^1-4

The first robotic-assisted orthopedic surgery was reportedly performed in 1992, when the Robodoc autonomous system was utilized for total hip arthroplasty.^2-4 A robotic system for total knee arthroplasty (TKA) was first described in 1993, but it would take several more years until a system for unicompartmental knee arthroplasty (UKA) would be commercialized and used clinically.^5,6 The rationale for advancement of robotic technology for isolated medial or lateral knee arthritis stems from the recognition that while UKA is effective and durable when components and limb are well aligned and soft tissues appropriately balanced, they are less forgiving of even slight component malalignment of as little as 2° to 3° and prone to premature loosening or wear in those circumstances.^7-13,14 In the mid 2000s, Cobb and colleagues⁶ reported using a semiautonomous robot for UKA. Since then, emergence of other semiautonomous robotic systems has led to greater market penetration and technology utilization.¹⁵

Currently, an estimated 15% to 20% of UKA surgeries are being performed with robotic assistance.¹⁶ Further, patent activity and peer-reviewed publications related to robotic technology in UKA (which can be considered surrogate measures of interest and evolving development and experience with robotic technologies) have increased dramatically over the past few years.^{2,6,14,17,18-34} To date, while the most dramatic growth of robotic utilization and case volumes has occurred in the subspecialty of UKA, semiautonomous robotic systems have been used with increasing frequency for patellofemoral and bicompartmental knee arthroplasty.^35,36 Robotics have been used sparingly for TKA, and limited to autonomous systems;^37,38 however, it is anticipated that emergence of semiautonomous platforms for TKA will further expand the role of robotics over the next decade, particularly as our focus shifts beyond component and limb alignment in TKA and more towards the role of robotics in soft tissue balancing, reduction in instrumentation and inventory and its attendant cost savings, and surgical efficiencies. One semiautonomous robotic technology first used in 2006 (Mako, Stryker) reported a 130% increase in robotic volume from 2011 to 2012; another, first used in 2013, reported growth of 480% between 2013 and 2014, due to its improved cost structure, ease of use, smaller footprint, image-free platform and applicability in ambulatory surgery centers (Navio, Smith & Nephew; data supplied by manufacturer), demonstrating the growing popularity of robotic technology.^17,39 Further, a recent analysis of potential market penetration over the next decade published by Medical Device and Diagnostic Industry (http://www.mddionline.com) projected that nearly 37% of UKAs and 23% of TKAs will be performed with robotics in 10 years.

Distinction Between Robotic-Assisted Technologies

Autonomous systems involve pre-programming the system with parameters that define the amount and orientation of bone to be removed, after which the system prepares the surfaces independent of surgeon control, other than having access to a “shutdown” switch. There are currently no autonomous robotic tools approved by the US Food and Drug Administration (FDA) for knee arthroplasty.

Semiautonomous systems involve the mapping of condylar landmarks and determination of alignment indices, which also defines the volume and orientation of bone to be removed. While the systems remove bone and cartilage within the pre-established parameters, the robotic tools are controlled and manipulated by the surgeon (Figure 1). The predetermined safe zones modulate and safeguard the surgical actions. These systems also provide real-time quantification of soft tissue balancing, which may contribute to the reported successful clinical and functional outcomes with semiautonomous systems (Figure 2).^2,4,19,22 There are several semiautonomous robotic systems that are approved for use by the FDA.

Each robotic-assisted surgery (RAS) system utilizes some sort of 3-dimensional digital map of the surgical surfaces after a process of surface mapping and landmark registration.² In the case of Mako, this planning process also requires a preoperative computed tomography (CT) scan. Over the past few years, the requirement of a CT scan has proven problematic and costly, as increasingly third-party payers and insurers are denying coverage for additional studies used for preoperative planning, leaving the burden of cost on the patients and/or hospitals. Additionally, in an era in which bundled payment arrangements are commonplace or in which providers are held accountable for costly care, the use of costly preoperative imaging is untenable. Furthermore, there is a growing concern regarding the risk of radiation exposure from CT scans that makes image-free technologies, such as Navio, an alternative for stakeholders.⁴⁰

At this time, the 2 semiautonomous systems in use for UKA employ different methods to safeguard against inadvertent bone preparation: one by providing haptic constraint beyond which movement of the bur is limited (Mako); the other by modulating the exposure or speed of the handheld robotic bur (Navio) (Figure 3).

Outcomes of RAS in UKA

Compared to conventional UKA, robotic assistance has consistently demonstrated improved surgical accuracy, even through minimally invasive incisions (Figures 4, 5).^6,20-28 Several studies have found substantial reduction in variability and error of component positioning with use of semiautonomous robotic tools.^6,21,25 In fact, precision appears to be comparable regardless of whether an image-free system or one requiring a preoperative CT scan is used (Table). Further, in addition to improving component and limb alignment, Plate and colleagues²² demonstrated that RAS-based UKA systems can help the surgeon precisely reproduce plans for soft-tissue balancing. The authors reported ligament balancing to be accurate up to .53 mm compared to the preoperative plan, with approximately 83% of cases balanced within 1 mm of the plan through a full range of flexion.²²

When evaluating advanced and novel technologies, there is undoubtedly concern that there will be increased operative time and a substantial learning curve with those technologies. Karia and colleagues³⁰ found that when inexperienced surgeons performed UKA on synthetic bone models using robotics, the mean compound rotational and translational errors were lower than when conventional techniques were used. Among those using conventional techniques, although surgical times improved during the learning period, positional inaccuracies persisted. On the other hand, robotic assistance enabled surgeons to achieve precision and accuracy when positioning UKA components irrespective of their learning experience.³⁰ Another study, by Coon,³¹ similarly suggested a shorter learning curve and greater accuracy with RAS using the Mako system compared to conventional techniques. A prospective, multicenter, observational study evaluated the operative times of 11 surgeons during their initial clinical cases using Navio robotic technology for medial UKA after a period of training using cadaver knees and sawbones.⁴¹ The learning curve for total surgical time (tracker placement to implant trial phase) indicates that it takes 8 cases to achieve 95% of total learning and maintain a steady state surgical time.

Potential Disadvantages of RAS in UKA

RAS for UKA has several potential disadvantages that must be weighed against their potential benefits. One major barrier to broader use of RAS is the increased cost associated with the technologies.^17,19,27,32 Capital and maintenance costs for these systems can be high, and those that require additional advanced imaging, such as CT scans, further challenge the return on investment.^17,19,32 In a Markov analysis of one robotic system (Mako), Moschetti and colleagues¹⁷ found that if one assumes a system cost of $1.362 million, value can be attained due to slightly better outcomes despite being more expensive than traditional methods. Nonetheless, their analysis of the Mako system estimated that each robot-assisted UKA case cost $19,219, compared to $16,476 with traditional UKA, and was associated with an incremental cost of $47,180 per quality-adjusted life-year. Their analysis further demonstrated that the cost-effectiveness was very sensitive to case volume, with lower costs realized once volumes surpassed 94 cases per year. On the other hand, costs (and thus value) will also obviously vary depending on the capital costs, annual service charges, and avoidance of unnecessary preoperative scans.¹⁹ For instance, assuming a cost of $500,000 for the image-free Navio robotic system, return on investment is achievable within 25 cases annually, roughly one-quarter of the cases necessary with the image-based system.

Another disadvantage of RAS systems in UKA is the unique complications associated with their use. Both RAS and conventional UKA can be complicated by similar problems such as component loosening, polyethylene wear, progressive arthritis, infection, stiffness, instability, and thromboembolism. RAS systems, however, carry the additional risk of specific robot-related issues.^19,27 Perhaps most notably, the pin tracts for the required optical tracking arrays can create a stress riser in the cortical bone,^19,27,33,42 highlighting the importance of inserting these pins in metaphyseal, and not diaphyseal, bone. Soft tissue complications have been reported during bone preparation with autonomous systems in total knee and hip arthroplasty;^37,38 however, the senior author (JHL) has not observed that in 1000 consecutive cases with either semiautonomous surgeon-driven robotic tool.¹⁹

Finally, systems that require a preoperative CT scan pose an increased radiation risk.⁴⁰ Ponzio and Lonner⁴⁰ recently reported that each preoperative CT scan for robotic-assisted knee arthroplasty (using a Mako protocol) is associated with a mean effective dose of radiation of 4.8 mSv, which is approximately equivalent to 48 chest radiographs.³⁴ Further, in that study, at least 25% of patients had been subjected to multiple scans, with some being exposed to cumulative effective doses of up to 103 mSv. This risk should not be considered completely negligible given that 10 mSv may be associated with an increase in the possibility of fatal cancer, and an estimated 29,000 excess cancer cases in the United States annually are reportedly caused by CT scans.^40,43,44 However, this increased radiation risk is not inherent to all RAS systems. Image-free systems, such as Navio, do not require CT scans and are thus not associated with this potential disadvantage.

Conclusion

Robotics has come a long way from its humble conceptual beginnings nearly a century ago. Rapid advances in medical technology over the past 10 years have led to the development and growing popularity of RAS in orthopedic surgery, particularly during UKA. Component placement, quantified soft tissue balance, and radiographic alignment appear to be improved and the incidence of outliers reduced with the use of RAS during UKA. Further assessment is needed to determine whether the improved alignment and balance will impact clinical function and/or durability. Early results are very promising, though, creating optimism that the full benefits of RAS in UKA will be further confirmed with additional time and research.

The concept of robotics is relatively new in medical practice. The term “robot” itself is less than 100 years old, having been first introduced to popular culture in 1917 by Joseph Capek in the science fiction story Opilec.^1,2 Robots eventually transitioned from this initial fictional literary setting to reality in 1958, when General Motors began adding automated machines to its assembly lines.¹ However, it was not until the 1980s that robotics and their exacting efficiencies would be introduced in the medical field, and it would take another decade before they would enter the specialty of orthopedics.^1-4

The first robotic-assisted orthopedic surgery was reportedly performed in 1992, when the Robodoc autonomous system was utilized for total hip arthroplasty.^2-4 A robotic system for total knee arthroplasty (TKA) was first described in 1993, but it would take several more years until a system for unicompartmental knee arthroplasty (UKA) would be commercialized and used clinically.^5,6 The rationale for advancement of robotic technology for isolated medial or lateral knee arthritis stems from the recognition that while UKA is effective and durable when components and limb are well aligned and soft tissues appropriately balanced, they are less forgiving of even slight component malalignment of as little as 2° to 3° and prone to premature loosening or wear in those circumstances.^7-13,14 In the mid 2000s, Cobb and colleagues⁶ reported using a semiautonomous robot for UKA. Since then, emergence of other semiautonomous robotic systems has led to greater market penetration and technology utilization.¹⁵

Currently, an estimated 15% to 20% of UKA surgeries are being performed with robotic assistance.¹⁶ Further, patent activity and peer-reviewed publications related to robotic technology in UKA (which can be considered surrogate measures of interest and evolving development and experience with robotic technologies) have increased dramatically over the past few years.^{2,6,14,17,18-34} To date, while the most dramatic growth of robotic utilization and case volumes has occurred in the subspecialty of UKA, semiautonomous robotic systems have been used with increasing frequency for patellofemoral and bicompartmental knee arthroplasty.^35,36 Robotics have been used sparingly for TKA, and limited to autonomous systems;^37,38 however, it is anticipated that emergence of semiautonomous platforms for TKA will further expand the role of robotics over the next decade, particularly as our focus shifts beyond component and limb alignment in TKA and more towards the role of robotics in soft tissue balancing, reduction in instrumentation and inventory and its attendant cost savings, and surgical efficiencies. One semiautonomous robotic technology first used in 2006 (Mako, Stryker) reported a 130% increase in robotic volume from 2011 to 2012; another, first used in 2013, reported growth of 480% between 2013 and 2014, due to its improved cost structure, ease of use, smaller footprint, image-free platform and applicability in ambulatory surgery centers (Navio, Smith & Nephew; data supplied by manufacturer), demonstrating the growing popularity of robotic technology.^17,39 Further, a recent analysis of potential market penetration over the next decade published by Medical Device and Diagnostic Industry (http://www.mddionline.com) projected that nearly 37% of UKAs and 23% of TKAs will be performed with robotics in 10 years.

Distinction Between Robotic-Assisted Technologies

Autonomous systems involve pre-programming the system with parameters that define the amount and orientation of bone to be removed, after which the system prepares the surfaces independent of surgeon control, other than having access to a “shutdown” switch. There are currently no autonomous robotic tools approved by the US Food and Drug Administration (FDA) for knee arthroplasty.

Semiautonomous systems involve the mapping of condylar landmarks and determination of alignment indices, which also defines the volume and orientation of bone to be removed. While the systems remove bone and cartilage within the pre-established parameters, the robotic tools are controlled and manipulated by the surgeon (Figure 1). The predetermined safe zones modulate and safeguard the surgical actions. These systems also provide real-time quantification of soft tissue balancing, which may contribute to the reported successful clinical and functional outcomes with semiautonomous systems (Figure 2).^2,4,19,22 There are several semiautonomous robotic systems that are approved for use by the FDA.

Each robotic-assisted surgery (RAS) system utilizes some sort of 3-dimensional digital map of the surgical surfaces after a process of surface mapping and landmark registration.² In the case of Mako, this planning process also requires a preoperative computed tomography (CT) scan. Over the past few years, the requirement of a CT scan has proven problematic and costly, as increasingly third-party payers and insurers are denying coverage for additional studies used for preoperative planning, leaving the burden of cost on the patients and/or hospitals. Additionally, in an era in which bundled payment arrangements are commonplace or in which providers are held accountable for costly care, the use of costly preoperative imaging is untenable. Furthermore, there is a growing concern regarding the risk of radiation exposure from CT scans that makes image-free technologies, such as Navio, an alternative for stakeholders.⁴⁰

At this time, the 2 semiautonomous systems in use for UKA employ different methods to safeguard against inadvertent bone preparation: one by providing haptic constraint beyond which movement of the bur is limited (Mako); the other by modulating the exposure or speed of the handheld robotic bur (Navio) (Figure 3).

Outcomes of RAS in UKA

Compared to conventional UKA, robotic assistance has consistently demonstrated improved surgical accuracy, even through minimally invasive incisions (Figures 4, 5).^6,20-28 Several studies have found substantial reduction in variability and error of component positioning with use of semiautonomous robotic tools.^6,21,25 In fact, precision appears to be comparable regardless of whether an image-free system or one requiring a preoperative CT scan is used (Table). Further, in addition to improving component and limb alignment, Plate and colleagues²² demonstrated that RAS-based UKA systems can help the surgeon precisely reproduce plans for soft-tissue balancing. The authors reported ligament balancing to be accurate up to .53 mm compared to the preoperative plan, with approximately 83% of cases balanced within 1 mm of the plan through a full range of flexion.²²

When evaluating advanced and novel technologies, there is undoubtedly concern that there will be increased operative time and a substantial learning curve with those technologies. Karia and colleagues³⁰ found that when inexperienced surgeons performed UKA on synthetic bone models using robotics, the mean compound rotational and translational errors were lower than when conventional techniques were used. Among those using conventional techniques, although surgical times improved during the learning period, positional inaccuracies persisted. On the other hand, robotic assistance enabled surgeons to achieve precision and accuracy when positioning UKA components irrespective of their learning experience.³⁰ Another study, by Coon,³¹ similarly suggested a shorter learning curve and greater accuracy with RAS using the Mako system compared to conventional techniques. A prospective, multicenter, observational study evaluated the operative times of 11 surgeons during their initial clinical cases using Navio robotic technology for medial UKA after a period of training using cadaver knees and sawbones.⁴¹ The learning curve for total surgical time (tracker placement to implant trial phase) indicates that it takes 8 cases to achieve 95% of total learning and maintain a steady state surgical time.

Potential Disadvantages of RAS in UKA

RAS for UKA has several potential disadvantages that must be weighed against their potential benefits. One major barrier to broader use of RAS is the increased cost associated with the technologies.^17,19,27,32 Capital and maintenance costs for these systems can be high, and those that require additional advanced imaging, such as CT scans, further challenge the return on investment.^17,19,32 In a Markov analysis of one robotic system (Mako), Moschetti and colleagues¹⁷ found that if one assumes a system cost of $1.362 million, value can be attained due to slightly better outcomes despite being more expensive than traditional methods. Nonetheless, their analysis of the Mako system estimated that each robot-assisted UKA case cost $19,219, compared to $16,476 with traditional UKA, and was associated with an incremental cost of $47,180 per quality-adjusted life-year. Their analysis further demonstrated that the cost-effectiveness was very sensitive to case volume, with lower costs realized once volumes surpassed 94 cases per year. On the other hand, costs (and thus value) will also obviously vary depending on the capital costs, annual service charges, and avoidance of unnecessary preoperative scans.¹⁹ For instance, assuming a cost of $500,000 for the image-free Navio robotic system, return on investment is achievable within 25 cases annually, roughly one-quarter of the cases necessary with the image-based system.

Another disadvantage of RAS systems in UKA is the unique complications associated with their use. Both RAS and conventional UKA can be complicated by similar problems such as component loosening, polyethylene wear, progressive arthritis, infection, stiffness, instability, and thromboembolism. RAS systems, however, carry the additional risk of specific robot-related issues.^19,27 Perhaps most notably, the pin tracts for the required optical tracking arrays can create a stress riser in the cortical bone,^19,27,33,42 highlighting the importance of inserting these pins in metaphyseal, and not diaphyseal, bone. Soft tissue complications have been reported during bone preparation with autonomous systems in total knee and hip arthroplasty;^37,38 however, the senior author (JHL) has not observed that in 1000 consecutive cases with either semiautonomous surgeon-driven robotic tool.¹⁹

Finally, systems that require a preoperative CT scan pose an increased radiation risk.⁴⁰ Ponzio and Lonner⁴⁰ recently reported that each preoperative CT scan for robotic-assisted knee arthroplasty (using a Mako protocol) is associated with a mean effective dose of radiation of 4.8 mSv, which is approximately equivalent to 48 chest radiographs.³⁴ Further, in that study, at least 25% of patients had been subjected to multiple scans, with some being exposed to cumulative effective doses of up to 103 mSv. This risk should not be considered completely negligible given that 10 mSv may be associated with an increase in the possibility of fatal cancer, and an estimated 29,000 excess cancer cases in the United States annually are reportedly caused by CT scans.^40,43,44 However, this increased radiation risk is not inherent to all RAS systems. Image-free systems, such as Navio, do not require CT scans and are thus not associated with this potential disadvantage.

Conclusion

Robotics has come a long way from its humble conceptual beginnings nearly a century ago. Rapid advances in medical technology over the past 10 years have led to the development and growing popularity of RAS in orthopedic surgery, particularly during UKA. Component placement, quantified soft tissue balance, and radiographic alignment appear to be improved and the incidence of outliers reduced with the use of RAS during UKA. Further assessment is needed to determine whether the improved alignment and balance will impact clinical function and/or durability. Early results are very promising, though, creating optimism that the full benefits of RAS in UKA will be further confirmed with additional time and research.

The concept of robotics is relatively new in medical practice. The term “robot” itself is less than 100 years old, having been first introduced to popular culture in 1917 by Joseph Capek in the science fiction story Opilec.^1,2 Robots eventually transitioned from this initial fictional literary setting to reality in 1958, when General Motors began adding automated machines to its assembly lines.¹ However, it was not until the 1980s that robotics and their exacting efficiencies would be introduced in the medical field, and it would take another decade before they would enter the specialty of orthopedics.^1-4

The first robotic-assisted orthopedic surgery was reportedly performed in 1992, when the Robodoc autonomous system was utilized for total hip arthroplasty.^2-4 A robotic system for total knee arthroplasty (TKA) was first described in 1993, but it would take several more years until a system for unicompartmental knee arthroplasty (UKA) would be commercialized and used clinically.^5,6 The rationale for advancement of robotic technology for isolated medial or lateral knee arthritis stems from the recognition that while UKA is effective and durable when components and limb are well aligned and soft tissues appropriately balanced, they are less forgiving of even slight component malalignment of as little as 2° to 3° and prone to premature loosening or wear in those circumstances.^7-13,14 In the mid 2000s, Cobb and colleagues⁶ reported using a semiautonomous robot for UKA. Since then, emergence of other semiautonomous robotic systems has led to greater market penetration and technology utilization.¹⁵

Currently, an estimated 15% to 20% of UKA surgeries are being performed with robotic assistance.¹⁶ Further, patent activity and peer-reviewed publications related to robotic technology in UKA (which can be considered surrogate measures of interest and evolving development and experience with robotic technologies) have increased dramatically over the past few years.^{2,6,14,17,18-34} To date, while the most dramatic growth of robotic utilization and case volumes has occurred in the subspecialty of UKA, semiautonomous robotic systems have been used with increasing frequency for patellofemoral and bicompartmental knee arthroplasty.^35,36 Robotics have been used sparingly for TKA, and limited to autonomous systems;^37,38 however, it is anticipated that emergence of semiautonomous platforms for TKA will further expand the role of robotics over the next decade, particularly as our focus shifts beyond component and limb alignment in TKA and more towards the role of robotics in soft tissue balancing, reduction in instrumentation and inventory and its attendant cost savings, and surgical efficiencies. One semiautonomous robotic technology first used in 2006 (Mako, Stryker) reported a 130% increase in robotic volume from 2011 to 2012; another, first used in 2013, reported growth of 480% between 2013 and 2014, due to its improved cost structure, ease of use, smaller footprint, image-free platform and applicability in ambulatory surgery centers (Navio, Smith & Nephew; data supplied by manufacturer), demonstrating the growing popularity of robotic technology.^17,39 Further, a recent analysis of potential market penetration over the next decade published by Medical Device and Diagnostic Industry (http://www.mddionline.com) projected that nearly 37% of UKAs and 23% of TKAs will be performed with robotics in 10 years.

Distinction Between Robotic-Assisted Technologies

Autonomous systems involve pre-programming the system with parameters that define the amount and orientation of bone to be removed, after which the system prepares the surfaces independent of surgeon control, other than having access to a “shutdown” switch. There are currently no autonomous robotic tools approved by the US Food and Drug Administration (FDA) for knee arthroplasty.

Semiautonomous systems involve the mapping of condylar landmarks and determination of alignment indices, which also defines the volume and orientation of bone to be removed. While the systems remove bone and cartilage within the pre-established parameters, the robotic tools are controlled and manipulated by the surgeon (Figure 1). The predetermined safe zones modulate and safeguard the surgical actions. These systems also provide real-time quantification of soft tissue balancing, which may contribute to the reported successful clinical and functional outcomes with semiautonomous systems (Figure 2).^2,4,19,22 There are several semiautonomous robotic systems that are approved for use by the FDA.

Each robotic-assisted surgery (RAS) system utilizes some sort of 3-dimensional digital map of the surgical surfaces after a process of surface mapping and landmark registration.² In the case of Mako, this planning process also requires a preoperative computed tomography (CT) scan. Over the past few years, the requirement of a CT scan has proven problematic and costly, as increasingly third-party payers and insurers are denying coverage for additional studies used for preoperative planning, leaving the burden of cost on the patients and/or hospitals. Additionally, in an era in which bundled payment arrangements are commonplace or in which providers are held accountable for costly care, the use of costly preoperative imaging is untenable. Furthermore, there is a growing concern regarding the risk of radiation exposure from CT scans that makes image-free technologies, such as Navio, an alternative for stakeholders.⁴⁰

At this time, the 2 semiautonomous systems in use for UKA employ different methods to safeguard against inadvertent bone preparation: one by providing haptic constraint beyond which movement of the bur is limited (Mako); the other by modulating the exposure or speed of the handheld robotic bur (Navio) (Figure 3).

Outcomes of RAS in UKA

Compared to conventional UKA, robotic assistance has consistently demonstrated improved surgical accuracy, even through minimally invasive incisions (Figures 4, 5).^6,20-28 Several studies have found substantial reduction in variability and error of component positioning with use of semiautonomous robotic tools.^6,21,25 In fact, precision appears to be comparable regardless of whether an image-free system or one requiring a preoperative CT scan is used (Table). Further, in addition to improving component and limb alignment, Plate and colleagues²² demonstrated that RAS-based UKA systems can help the surgeon precisely reproduce plans for soft-tissue balancing. The authors reported ligament balancing to be accurate up to .53 mm compared to the preoperative plan, with approximately 83% of cases balanced within 1 mm of the plan through a full range of flexion.²²

When evaluating advanced and novel technologies, there is undoubtedly concern that there will be increased operative time and a substantial learning curve with those technologies. Karia and colleagues³⁰ found that when inexperienced surgeons performed UKA on synthetic bone models using robotics, the mean compound rotational and translational errors were lower than when conventional techniques were used. Among those using conventional techniques, although surgical times improved during the learning period, positional inaccuracies persisted. On the other hand, robotic assistance enabled surgeons to achieve precision and accuracy when positioning UKA components irrespective of their learning experience.³⁰ Another study, by Coon,³¹ similarly suggested a shorter learning curve and greater accuracy with RAS using the Mako system compared to conventional techniques. A prospective, multicenter, observational study evaluated the operative times of 11 surgeons during their initial clinical cases using Navio robotic technology for medial UKA after a period of training using cadaver knees and sawbones.⁴¹ The learning curve for total surgical time (tracker placement to implant trial phase) indicates that it takes 8 cases to achieve 95% of total learning and maintain a steady state surgical time.

Potential Disadvantages of RAS in UKA

RAS for UKA has several potential disadvantages that must be weighed against their potential benefits. One major barrier to broader use of RAS is the increased cost associated with the technologies.^17,19,27,32 Capital and maintenance costs for these systems can be high, and those that require additional advanced imaging, such as CT scans, further challenge the return on investment.^17,19,32 In a Markov analysis of one robotic system (Mako), Moschetti and colleagues¹⁷ found that if one assumes a system cost of $1.362 million, value can be attained due to slightly better outcomes despite being more expensive than traditional methods. Nonetheless, their analysis of the Mako system estimated that each robot-assisted UKA case cost $19,219, compared to $16,476 with traditional UKA, and was associated with an incremental cost of $47,180 per quality-adjusted life-year. Their analysis further demonstrated that the cost-effectiveness was very sensitive to case volume, with lower costs realized once volumes surpassed 94 cases per year. On the other hand, costs (and thus value) will also obviously vary depending on the capital costs, annual service charges, and avoidance of unnecessary preoperative scans.¹⁹ For instance, assuming a cost of $500,000 for the image-free Navio robotic system, return on investment is achievable within 25 cases annually, roughly one-quarter of the cases necessary with the image-based system.

Another disadvantage of RAS systems in UKA is the unique complications associated with their use. Both RAS and conventional UKA can be complicated by similar problems such as component loosening, polyethylene wear, progressive arthritis, infection, stiffness, instability, and thromboembolism. RAS systems, however, carry the additional risk of specific robot-related issues.^19,27 Perhaps most notably, the pin tracts for the required optical tracking arrays can create a stress riser in the cortical bone,^19,27,33,42 highlighting the importance of inserting these pins in metaphyseal, and not diaphyseal, bone. Soft tissue complications have been reported during bone preparation with autonomous systems in total knee and hip arthroplasty;^37,38 however, the senior author (JHL) has not observed that in 1000 consecutive cases with either semiautonomous surgeon-driven robotic tool.¹⁹

Finally, systems that require a preoperative CT scan pose an increased radiation risk.⁴⁰ Ponzio and Lonner⁴⁰ recently reported that each preoperative CT scan for robotic-assisted knee arthroplasty (using a Mako protocol) is associated with a mean effective dose of radiation of 4.8 mSv, which is approximately equivalent to 48 chest radiographs.³⁴ Further, in that study, at least 25% of patients had been subjected to multiple scans, with some being exposed to cumulative effective doses of up to 103 mSv. This risk should not be considered completely negligible given that 10 mSv may be associated with an increase in the possibility of fatal cancer, and an estimated 29,000 excess cancer cases in the United States annually are reportedly caused by CT scans.^40,43,44 However, this increased radiation risk is not inherent to all RAS systems. Image-free systems, such as Navio, do not require CT scans and are thus not associated with this potential disadvantage.

Conclusion

Robotics has come a long way from its humble conceptual beginnings nearly a century ago. Rapid advances in medical technology over the past 10 years have led to the development and growing popularity of RAS in orthopedic surgery, particularly during UKA. Component placement, quantified soft tissue balance, and radiographic alignment appear to be improved and the incidence of outliers reduced with the use of RAS during UKA. Further assessment is needed to determine whether the improved alignment and balance will impact clinical function and/or durability. Early results are very promising, though, creating optimism that the full benefits of RAS in UKA will be further confirmed with additional time and research.