Home-AMA: Healthcare GenMat: Generative Design for Patient-Specific Orthopedic Implants
WithAMA: Healthcareon June 4th putting3D printing in medicineunder 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 ofGenMat, 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.
Source: 3D Printing Industry
