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Xenco Medical is launching its first line of titanium cervical 3D-printed cages, a move that represents a foundation for the company’s future implant strategy.
“We invested in additive technology because it allows us to achieve a modulus of elasticity that closely resembles bone, while still giving us the freedom to create architectures that support optimal biomaterial insertion and leverage the biocompatibility of titanium,” said Jason Haider, Xenco Medical Founder and CEO.
The new cages set the stage for the company’s next generation of implants. “Our vision is to incorporate biomaterials inside the cages, and we anticipate doing that beginning next year,” Haider said. “Having 3D printing in our armory, so to speak, gives us the ability to refine and adapt architectures to meet those needs.”
Xenco Medical is also leaning into artificial intelligence as a driver of its next wave of implant innovation. Haider noted that AI has become a powerful tool for exploring architectures that balance strength, biocompatibility and ease of use in surgery.
“We wanted to look at a range of architectures that would be ideal not only for biomaterials and strength, but also for compatibility with various plates,” Haider said. “Ideally, surgeons will use our 3D-printed plates and interbodies, but we also wanted the design to be agnostic. That’s where AI came in. It allowed us to create a wide range of implant configurations that we then shared with our surgeon design teams.”
The creation of 3D-printed implants traditionally requires engineers to design in CAD platforms, share virtual files, print prototypes and iterate over time. AI has added a new dimension to the process.
“We can set the parameters, generate the engineering drawings and let the system create 10 or more variations,” Haider said. “It’s almost like an evolutionary algorithm.”
Xenco Medical’s engineers then run a Finite Element Analysis (FEA) on those models and use the results to explore new configurations and even navigate around intellectual property constraints. That optionality sparks discussion among the design team, allowing them to iterate to improved final designs.
One of the tangible outcomes of the company’s AI-driven design work was the development of a new cage architecture that enabled the design of a complementary delivery device.
“If we didn’t have AI to generate countless iterations, we might have felt stuck,” Haider said. “Instead, it gave us the freedom to explore solutions that would have been difficult to achieve using traditional approaches.”
AI allowed Xenco Medical’s team to look at the design problem from a completely different perspective to create a more holistic implant design that wasn’t just mechanically driven, but also biologically driven.
“Even though it’s a titanium, 3D-printed implant, we consider it an extension of our biomaterial line because biomaterials were so involved in the design process thanks to AI,” Haider said.
Some of the implant architectures that AI generated were so compelling that Xenco Medical’s team prototyped them, printed polymer models and ran an FEA to make sure they were strong enough.
“That’s been valuable because designing an implant that’s optimized for graft delivery while maintaining mechanical strength is a real challenge,” Haider said. “AI gave us options we might not have imagined on our own.”
Advanced Problem Solving
AI design tools have reshaped the way Westconn Precision Technologies thinks about engineering challenges. The approach represents a fundamental shift from traditional parametric modeling.
“The style of design has flipped how we approach a project,” said Jake Marasco, Vice President of Sales and Operations at Westconn Precision. “With traditional parametric modeling, you start with solids and then extrude features. You come up with a design and try to make it light and minimal. It works — until the conditions change.”
When weight or load increases, traditional designs often reveal their limitations.
“What was good enough before suddenly isn’t,” Marasco said. “So, you end up in a cycle of iterating, fixing, adjusting and trying again.”
Designing with AI begins with the problem rather than the solution. “Instead of starting with a shape and tweaking it, you define the loading conditions, material properties and constraints, and then let the software tell you what the best configuration should be,” Marasco explained. “The software calculates the optimal structure based on the actual demands of the application.”
He believes one of the biggest challenges facing medical device companies isn’t access to AI design tools, but having the expertise to use them effectively.
Plus, keeping pace with the speed of AI innovation remains difficult. The CAD program SolidWorks has essentially been the same for the last 20 years, so design engineers have had time to learn the program and become experts. Generative and computational design tools have only been around for about five years, and they’re quickly evolving.
That rapid progression raises an important question for implant designers: How do you keep up?
“It’s something every company has to ask themselves,” Marasco said. “You can’t rely on the way things have always been done. You need to invest in building the expertise to match the technology.”
Standing Out
The real differentiator in additive manufacturing may not be the technology itself, but how quickly and efficiently 3D-printed parts can be brought to market.
Some companies chase speed by thickening layers or adding more lasers to printing machines, but Marasco sees the opportunity as broader than that.
“At the end of the day, as contract manufacturers working with our customers, we need to figure out how the process can be done more efficiently,” he said. “Can we get more parts on a build plate? Can we automate steps? Labor costs are rising, so how do we validate everything while keeping timelines tight?”
Validation, he noted, is often the biggest unknown for orthopedic companies.
“How do you prove that every part will be consistent?” Marasco asked. “Regulators want to know that you’ve tested worst-case scenarios, that your process won’t deviate from those outcomes. It’s not enough to show that you can 3D-print a part. You need to prove reliability and repeatability.”
Still, Marasco believes that improvements in both process and design can help shorten production timelines.
“Time to market is big, and our customers are starting to realize there’s opportunity to get creative,” he said. “They’re learning to design with the constraints of the machine in mind, and when you do that, you can leverage the benefits of additive. Our customers are educating themselves, and we’re working with them to accelerate that learning curve.”
For Westconn Precision, success in additive manufacturing often comes down to the details. How is a part oriented on a build plate? How is it nested to minimize secondary operations?
“The biggest factor to success is getting involved in the design process as early as possible and really understanding what’s important for clients,” Marasco said. “If the priority is to produce as many parts as quickly as possible, we’ll approach the project one way. Creating a device that meets the specific needs of surgeon users demands a more collaborative approach.”
The key is honesty and flexibility. “Every opportunity is different,” Marasco said. “Sometimes 3D printing is the right solution, sometimes machining is, and sometimes it’s a combination of both. We try to be upfront with customers about that.”
That approach has become especially important as competition heats up in orthopedics.
“It’s starting to get a little crowded with 3D-printed spinal cages and orthopedic devices,” Marasco said. “Everybody’s trying to reinvent the wheel, but which companies are actually building something unique?”
