With AMA: Healthcare on June 4th putting 3D printing in medicine under the spotlight, voices from across the industry are weighing in on where the technology is heading.
Orthopedic implant design has progressed steadily over the past five decades, from solid metal blocks to sophisticated lattice structures. Yet revision rates have barely moved, remaining between 10 and 20%. Dr. Sajjad Raeisi, Founder and CEO of GenMat, argues the field has been solving the wrong problem. His platform, Ossevo, applies bio-inspired computational methods to address what he sees as the root cause: the mechanical mismatch between synthetic implants and living bone.
His platform, Ossevo, short for osseous evolution, applies bio-inspired computational methods to produce implants that replicate bone’s structural behavior rather than approximating it through conventional engineering logic.
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The Limitations of Current Implant Design
Despite decades of advancement in orthopedic implants, revision rates remain low. The underlying cause is largely mechanical. Standard titanium implants carry a stiffness five to ten times greater than human bone, a disparity that causes the implant to absorb mechanical load that would otherwise pass through the surrounding bone tissue. Deprived of that stimulus, bone begins to resorb, a process known as stress shielding, ultimately leading to implant loosening and the need for revision surgery.
Successive generations of implant design have attempted to address this. Porous coatings improved osseointegration. CAD-based modeling introduced patient-specific geometry. Metal additive manufacturing enabled lattice and porous structures with greater permeability. More recently, implicit modeling techniques such as triply periodic minimal surfaces have offered geometrically smooth porous architectures suited to biomedical applications. Topology optimization, now widely used across industries, has further improved structural efficiency by computationally identifying optimal material distribution within a given constraint.
Each of these approaches, however, shares a fundamental limitation: none incorporates biological feedback into the design process. Topology optimization, for instance, optimizes for stiffness, the very property that drives stress shielding. Lattice structures, however refined, remain geometrically uniform and unresponsive to the local mechanical demands of individual patient anatomy. The result is implants that perform well as engineering objects but incompletely as biological replacements.
“Standard implants perform well as engineering objects, but incompletely as biological replacements. None of the current design approaches incorporates biological feedback into the process, and that is the fundamental gap we are working to close,” said Raeisi.
The Ossevo Platform: Bio-Inspired Structural Optimization
Ossevo addresses this gap by drawing directly from the mechanism bone uses to regulate its own structure. Under normal physiological conditions, bone continuously remodels in response to local mechanical stimuli, adding material in high-stress regions and resorbing it where load is minimal. This adaptive process produces a spatially graded architecture that is neither uniform nor arbitrary, but precisely calibrated to the mechanical environment it inhabits.
Ossevo replicates this logic computationally through the Hybrid Cellular Automata method. Rather than minimizing a compliance function as topology optimization does, Ossevo minimizes the deviation from a target mechanical stimulus across all elements of a finite element model, effectively instructing the design to seek the same uniform mechanical response that bone seeks through remodeling. The result is a field-driven lattice structure whose local geometry, pore size, wall thickness, strut diameter, varies continuously in response to the mechanical field rather than being assigned uniformly across the implant.
“The geometry is neither uniform nor arbitrary. Pore size, wall thickness, strut diameter, all vary continuously in response to the mechanical field. That is what bone does. That is what Ossevo replicates” said Raesi.
The platform integrates implicit modeling to generate these structures, leveraging signed distance fields to define geometry with precision independent of mesh resolution. This approach supports smooth, manufacturable geometries compatible with metal additive manufacturing and allows grading across multiple parameters simultaneously, including density, porosity, cell type, and orientation, all within a single unified workflow.
Current Development and Clinical Trajectory
GenMat is currently in NSF Phase 1 development, targeting a 30% improvement in strain energy uniformity, the metric most directly associated with stress shielding reduction. Early simulation results have demonstrated meaningful progress toward stiffness matching with native bone, and a working prototype of the Ossevo solver is planned for release within 2026.
Ongoing development priorities include mesh-free finite element methods to streamline structural validation, multi-physics coupling to account for fluid flow and fatigue alongside mechanical load, and tighter integration between design, simulation, and surgical planning, a workflow that remains fragmented across most existing tools. Validation studies are underway to confirm that simulation outcomes translate to physical performance targets.
The clinical case for bio-inspired implant design is clear. “Replacing a component of the human body with an object designed around biological parameters rather than mechanical efficiency is not simply a technical preference, it is a prerequisite for long-term performance. Ossevo represents a systematic effort to close the gap between what implant design has historically optimized for and what the body actually requires.”
Where Implant Design Has Stalled and What’s Changing
The challenge GenMat is working to solve is not new, but the tools to address it are. Orthopedic implant manufacturers have long understood that mechanical mismatch between synthetic materials and living bone drives revision surgery, yet solving it through geometry and porosity alone has proven insufficient. The broader industry is now moving toward approaches that go further.
Croom Medical’s Biofuse platform, developed using laser powder bed fusion, integrates lattice structures directly into implant geometries with precise control over pore size, porosity, and friction, targeting bone ingrowth through engineered architecture rather than surface coating alone. Similarly, restor3d’s Ossera AFX system combines patient-specific geometry with porous lattice architectures designed to promote osseointegration while maintaining mechanical strength, an acknowledgment that off-the-shelf implants cannot reliably conform to the anatomical and mechanical variability of individual patients.
What these efforts share is a focus on structure. What most still lack is biological feedback, the ability to let the mechanical environment of a specific patient’s bone inform the design from the outset. Researchers at RWTH Aachen have taken a step in this direction with bioresorbable implants whose internal lattice structures are adjusted to match the mechanical forces acting on the bone, generated automatically from medical imaging data. The gap GenMat is targeting sits at this intersection: not just patient-specific geometry, but patient-specific mechanical behavior, derived from how bone itself regulates its own structure.
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