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Scientists at the University of Birmingham in the UK have trialed Generative Design, a design approach that relies on machine learning and artificial intelligence, to create patient-specific knee implants. At present, knee implants are typically created in a limited range of sizes and shapes. While 3D printing has opened some scope for implants that are adapted to an individual\u2019s orthopedic anatomy, few are created with other important variables in mind, such as the activity level of the patient, their weight, or surgical planning constraints. The Generative Design technique, which is already extensively used to design cars and airplanes, allows for such parameters to be accounted for, potentially resulting in lighter, stronger, and more personalized implants.<\/p>\n
Personalized medicine is gradually becoming a reality. The goal is that medical interventions are highly tuned to the patient they are directed at, in the hope that tailoring therapeutic interventions will lead to better patient outcomes and reduced instances of side-effects. Now, researchers are applying an advanced design technique that is typically more closely associated with the aerospace or automotive industries, to medical implants. \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/p>\n
The technique is called Generative Design, and it employs machine learning and artificial intelligence to create objects that are highly optimized for their intended use, such as airplane parts that are stronger and weigh less. In the context of biomedical implants, Generative Design should allow for implants that take patient-specific biomechanical parameters, such as weight and activity levels, into account as well as constraints around surgical planning. \u00a0\u00a0\u00a0<\/p>\n
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\u201cThe \u2018one-size-fits-all\u2019 approach used in knee surgery to treat osteoarthritis can result in major complications, primarily due to overengineered implant designs and therefore limits surgical adoption and patient outcomes,\u201d said Sanjeevan Kanagalingam, a researcher involved in the study. \u201cThis AI integrated design interface allows us to configure tailored surgical planning parameters and take personal biomechanical information into account, and synergistically combine it with the embedded manufacturing intelligence to model medical-grade titanium implants that are specific to each patient.\u201d<\/p>\n
So far, the researchers have used their design technique to develop some High Tibial Osteotomy plates that would typically be implanted in the knee of patients diagnosed with osteoarthritis. They then used an electron beam powder bed fusion to manufacture their designs.<\/p>\n
\u201cThis generative design approach not only increases the patient-specificity of bone fixations but also serve as a novel, versatile framework in the design of load-bearing patient-specific implants for the hips, shoulders and maxillofacial surgeries,\u201d said Kanagalingam.<\/p>\n
Study in journal Progress in Additive Manufacturing<\/em>: Detailed design for additive manufacturing and post processing of generatively designed high tibial osteotomy fixation plates<\/p>\nVia: University of Birmingham<\/p>\n<\/div>\n
Via: University of Birmingham<\/p>\n
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