American Academy of Orthopaedic Surgeons’ (AAOS) 2022 annual meeting was March. The exhibit was full of digital technologies. The market leader, arguable, Stryker – whose MAKO brand of robotic assist devices has the largest installed base in orthopaedics 9excluding spine)
Stryker presented their 2021 sales performance to Wall Street’s analysts in February. CEO, Kevin Lobo disclosed MAKO’s robotics sales jumped 27% in 2021 while knee and hip implants sales declined slightly between 2019 and 2021. Stryker’s hip and knee sales had declined from $3.198 billion to $3.190 billion.
More than 50% of all Stryker’s total knee arthroplasty surgeries in the final quarter of 2021 were performed using a MAKO platform. Over 25% of Stryker’s hip cases were also performed using MAKO.
This is not a unique experience, Zimmer’s ROSA system and Smith & Nephew’s NAVIO system, similarly grew multiples faster than underlying implant or instrument sales.
How do we come to understand these technologies and, more importantly, incorporate them into the treatment of musculoskeletal disease?
Eric Timko, CEO of OrthAlign, and former CEO of Blue Belt Technologies, which sold its robotic system to Smith & Nephew in 2016 for $275 million, gave his opinion and outlook.
Timko’s OrthAlign recently announced Lantern, a smart intuitive, handheld navigation system for partial and total knee arthroplasty surgery. In total, under Timko, OrthAlign’s line of smart handheld navigation systems have been used in more than 250,000 arthroplasty cases globally.
Moore’s Law Redux
The world’s most powerful computer chip with 2.6 trillion transistors. What can this chip do? It powers the artificial intelligence network software programs. Who is using it? Argonne National Lab to accelerate cancer research. GlaxoSmithKline, AstraZeneca, and other pharmaceutical companies, large and small, to slash drug discovery times from decades to weeks
Moore’s law hypothesizes that the number of transistors in a dense integrated circuit will double every two years and has been doing so every year since 1965. Your smartphone is smarter than the super computers of 20 years ago. The same cycle will continue in the next 20 years.
Moore’s law is alive and well.
Robotics, Navigation, Augmented Reality: What’s the difference?How much functional overlap is there?
These technologies match each other well in terms of clinical outcomes, key differentiators will be economics and workflow efficiencies.
In terms of different types of digital systems for surgery both can deliver better clinical outcomes than manual surgery, and there is an abundance of data to support that. There is a mass movement from manual instruments to using some sort of technology. Much of it is being driven by Stryker, Zimmer, DePuy, or Smith & Nephew, and patients are jumping on the bandwagon to demand technology as well.
Robotics has had a significant impact on healthcare. The problem, though, is that robotics is increasingly inefficient particularly when compared to newer, smaller systems that bring huge processing power in a smaller package to the surgeon. The other issue is adaptability to every setting where orthopaedic services are provided. There is a new technology battleground in orthopedics, the ambulatory surgery centre (ASCs), with a totally different set of user needs.
When digital technologies can also improve efficiencies and lower costs, then we’ll see them being employed for every patient, in every room, whether at the downtown hospital or the ambulatory surgery centre.
The place for robotics, is where difficult patients are treated. Morbidly high BMI [body mass index], significant co-morbidities, severe deformities and so forth. But the mass market, your young, active straightforward total knee or total hip patients, who by the way benefit the most from computer assisted technology, the surgeon’s focus is to lock-in alignment, and hand-held navigation is the most efficient way to achieve those goals. In effect, it’s like the back-up camera on the car. It gives the surgeon the precise, objective information so they know exactly where they are so when they’re ready, they can lock it in.
If the patient is high-risk, then you surely want that protection of being in the hospital and using a big-box robotic platform. But we know that the best thing for a patient who is otherwise healthy is to get that knee or hip done, get up, get moving, get out and get home.
Every patient deserves advanced technology. That’s really what it gets down to.
Are robotics destined to be a standard of care?
Many truly believe that the use of technology in TJA [total joint arthroplasty] will be the standard of care, not necessarily robotics. The current healthcare environment can’t support that massive infrastructure of robotics at scale. It’s the space in the OR, the time required, the inefficiencies, the cost, and the need for multiple systems to support each implant.
Buyers are assessing the pieces of the puzzle to ensure the technology fits the need of the provider in terms of optimising outcomes, fitting into surgery workflow, and being cost effective.
In terms of orthopedics, we’re just scratching the surface of digital technology potential. Where we’re going to be in 5 years, 10 years, 15 years…who knows, but one thing is certain: Technology is here, and it is here to stay.
Right now. Today. Every patient deserves a computer assisted total joint replacement. Matching the right care and technology for the patient that meets the operational and economic considerations of the site of service is where we are at. The more surgeons adopt accessible technologies, the faster we will move toward technology as the standard of care in total joints.
Challenges robotics could face
“I’d like to answer that by going back to the early day of Blue Belt Technologies. When I first looked at it, I recognized that robotics for knee arthroplasty was a great indication, particularly for partial knees which are very, very difficult to do.
I recognized when looking at the Blue Belt robotics technology that, first, Blue Belt was doing robotics differently and, second, that the impact of a more user-friendly system on the surgeon as well as the patient would be profound.
When I looked at OrthAlign, I checked with the surgeons and asked them ‘Is this technology for the masses?’ Is this something everybody is going to embrace?Everybody is going to say, ‘I have to have this.’ We are solving the simplicity challenge that comes with any technology implementation.
No way can big box robotics be a technology for the masses. Even if you put one robot into a facility, with seven or eight or nine operating rooms, you’re not going to write the check for seven, eight or nine robots.
But when a handheld navigation system can do hips, can do knees, can do unis, can do balancing, can do everything—and cost effectively fit into existing OR workflows—then every patient benefits, not just the few who can get to an academic centre.
The promise of these digital technologies, I firmly believe, is to provide access to the best technologies to everybody: patients and surgeons regardless of setting, ASCs, independent clinics, or hospitals. As facilities work to build total joint programs that offer technology in every case, we strongly believe that those facilities that can address these needs through distributed handheld navigation will lead the way.” – Eric Timko, CEO of OrthoAlign, former CEO of robotics’ pioneer: Blue Belt Technologies.
Artificial Intelligence (AI), is that a thing? How will it enter and affect orthopedics?
“You use AI today. Whenever you use Google Search, Google ads, spelling correction, Google translate. The story on AI is not well understood. In the 1960s and ‘70s, AI was going ’going to happen within a decade.’ My friends who were AI obsessed, got their Ph.D.’s in this area. And then everything stopped. AI stopped working. There was a period of about 20 years, which is known as the ‘AI winter,’ where the systems didn’t work. And then a series of mathematicians in the ‘80s and ‘90s invented what is today known as ‘deep learning.’
The important thing about ‘deep learning’ is that it allows the manipulation of patterns at scale that allow these algorithms to work. The big breakthrough was in 2011 with a process called ‘image net’ where there was a contest to see if computers could see better than humans.
Today, computers CAN see better than humans. Their vision is literally better. I didn’t realize at the time how important ‘sight’ was for everything. Cars should be driven by a computer. The doctor should use an AI system to examine you and then give his or her recommendations on your care.
I’d much rather have the computer look at my skin rash or the retina in my eye because we now know from many, many tests that humans make observational mistakes. The computers when properly trained don’t.
It was from this insight that you could do vision at scale that you began to be able to do prediction at scale. Suddenly, now we are seeing systems that can predict the next ‘thing.’ Computers have gotten very good at predicting what will happen next.” -Eric Schmidt, former CEO of Google, and chair of the National Security Commission on Artificial Intelligence
In what way does AI “predict”?
There are three events in the last three years that really are the index point.
GO is a game that humans have played for 2,500 years. It was thought to be incomputable. Not only did a computer solve the game, but it beat the top humans in both Korea and China. In that process, the computer invented some new moves and strategies that had not been known to humans for 2,500 years. That’s a big deal.
The next thing that happened was that at MIT a set of synthetic biologists and computer scientists did a very complicated trick involving going through 100 million different compounds and figuring out which compounds would create a reaction for antibiotic use. They came up with, using this technique, a new drug that could not be foreseen, which is called Halcyon, and it appears to be the next broadscale antibiotic. We haven’t had one in roughly 40 years.
The third thing that happened was that a group called Open AI built what are now called Universal Models where they read everything, they could find on the web into something they call GTP3, also known as a transformer, and suddenly, we have a computer that can speak what it knows.
What are the implications for medicine and, indeed, daily life of AI, GTP3 and pattern recognition at such a massive scale?
These models are interesting because you train them, and you don’t know what they know. And furthermore, they can’t tell you, you must ask them. Many people think these universal models will profoundly change language and thought because they only get better with scale.
Five companies, a couple big ones and a couple start-ups, that are building what are called ‘trillion parameters’ models. These trillion parameter models cost $100 million or so to make.
That’s how exciting this new area is. You have strategy in the form of GO, you’ve got medicine in the form of Halcyon and now you’ve got language and learning models in the form of GTP3 and the Universal Model.
How fast are these technologies developing and when will they be active in medicine?
It is collectively believed that in the next 10 years this is going to come together and transform everything.
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