One of the biggest remaining challenges is to develop devices which ‘fit’ a vastly diverse range of patients – inspiring a push towards accumulating large anatomical databases, and instigating the modularisation of components to allow a small range of implants to cover all patient needs.
An alternative strategy is to custom design implants for each individual patient, made possible by the drastic reduction of manufacturing lead times, but primarily adopted for complex cases or revision surgeries. Additionally, generic devices can be adapted to the specific patient, prior to insertion, using software for pre-operative planning.
But diversity in patients extends beyond geometry – it includes bone stiffness and strength, soft tissue characteristics and healing rates, as well as lifestyle-related variables such as activity levels which determine degree of loading and direction (for example whether the patient plays golf or hockey). As bone is also a living tissue that adapts to conditions, these parameters are unlikely to remain static.
With this in mind, can we design orthopaedic devices that themselves adapt to the individual patient to provide an optimised treatment, in other words ‘smart’ devices?
Some ‘smart’ approaches already exist. For example, biological responsive technologies have been developed, such as implant coatings which only release their pharmaceuticals when specific signals, such as low pH levels, have been identified. Other surgical devices, such as pacemakers, react to the conditions of their environment; but what about orthopaedic devices?
Considerable research has been conducted in the orthopaedic community on the use of traditional strain gauges to indicate the load levels within devices during use. This data is used to highlight device overloading, loosening, mechanical failure of implants, or to assess tissue characteristics during healing, but it has not, as yet, been transferred to the commercial market.
However, such devices are not ‘smart’ unless they change their performance in response to detected conditions. Research has been conducted into the possible use of smart devices for limb lengthening by distraction osteogenesis – for patients with a limb length discrepancy or congenital shortening of limbs. The surgery involves mechanically severing the bone (an osteotomy) and then separating the two bone ends using an extendible device. Both soft and hard tissues are gradually stretched over a number of weeks to the required final length. Traditionally, a regime of 1mm/day in four steps is adopted unless radiographic evidence suggests changes should be made.
Of course, growth rates vary considerably between patients and thus there is a risk of re-fracture (if the extension rate is too high) or premature consolidation (if the rate is too slow) amongst other complications. Using an automated lengthening device to monitor tissue stiffness and then adapt the distraction regime accordingly may allow optimisation of the procedure for each patient, resulting in improved tissue quality and reduced procedure time.
Could this idea of smart devices be implemented in other areas of orthopaedic device design?
What about an external fixator for fracture healing which could change its stiffness relative to the stiffness of the newly grown callus? Alternatively, consider hip and shoulder replacements that could re-position the ball relative to the stem in accordance with the loading directions? Or fixation plates that could adapt relative to loading conditions?
By optimising procedures in this way, we could both improve clinical outcomes and reduce treatment costs. Although validation of decision-making algorithms will require extensive research followed by thorough verification testing, the future for smart orthopaedic devices remains an exciting prospect.