Data Is King: Medical Imaging Is the Key to Unlocking Orthopedics’ Next Era

In the changing landscape of orthopedics, there has been a common refrain in recent years: the future is personalized, and it is digital. You could argue that we are already living in that future; for example, most of Stryker’s knee surgeries in the U.S. are now performed using the Mako robotics platform, using CT data to build a personalized plan tailored to a specific patient.¹ Each one of these plans is dependent on a robust digital backbone to manage the image data, construct the plan, and transfer it to the operating room in a timely and secure manner.

Stryker, of course, isn’t alone in this: the value of personalized medicine in orthopedics has been demonstrated over and over, and companies are putting a focus on it. Whether it’s CT-based robotics, patient-specific instrumentation, or custom implants, the common theme is that everyone involved with the personalized treatment has a better understanding of the situation before entering the operating room and can more accurately execute a procedure for a specific patient. It offers benefits for all stakeholders involved (device company, hospital, patient), and it makes perfect sense that this represents the direction where things are heading.

When you consider some of the most significant innovations in orthopedics (e.g., robotics, patient-specific instrumentation, custom implants, AI-assisted planning, etc.) over the past decade or so, each has one thing in common: they all begin with medical imaging data.

Medical Imaging: The Underlying Anchor

The understanding and value that comes with pre-op planning hinges on capturing a good medical image and creating an accurate reconstruction. For many years, this was not a trivial task, and even in the year 2026, it is not a given that you can easily and accurately understand a patient’s anatomy from a scan. Advances in imaging and image processing (for example, AI-based shape intelligence to help segment bones) are helping to automate this process, reducing error and variability as much as possible. 

With that said, challenges remain. The most complex cases, with large deformity or metal artifacts from existing implants, cause most segmentation algorithms to struggle. Even with advances in image filters and metal artifact reduction, trained specialists can have a difficult time defining bone margins when the image is flooded with scatter from an implant on the bilateral side. When human operators are visually identifying bone in situations like this, discrepancies in output from user to user could inevitably exist.

Automated workflows can help reduce risk and variability to make reconstruction more repeatable. There is substantial precedent for tools and techniques to ensure the image reconstructions are an accurate representation of what’s happening in the patient’s body. It is now possible to implement tailored AI models based on a specific scan protocol, patient demographics, and use case, even for scans where there are already implants present.² Groups that have successfully implemented image-based, personalized treatment planning at scale have figured out how to automate where possible while also keeping control of accuracy and variability. To strike that balance, one approach is to utilize shape-based AI algorithms to provide a baseline for bone segmentation and then have an operator quality-check the output against the original images, fine-tuning the results as necessary. 

With medical imaging becoming such a core part of the standard of care, this also requires a shift in mindset through the entire care ecosystem. CTs or MRIs are now ordered for hundreds of thousands of knee procedures, where they weren’t before. Insurance companies need to be convinced that the cost of the scan is worth the value they will see (in reduced surgery revisions, for example). Imaging techs need to familiarize themselves with newer modalities, such as cone beam CT, as these become more commonplace for various indications. This shift can be painful at first, but the continued adoption of medical imaging is crucial to driving the next innovations in orthopedics.

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Driving the Next Generation of Orthopedics

This brings us to the “holy grail” everyone is striving for—tying medical imaging data and pre-operative plans to patient outcome measures. Making the connection between pre-op condition, treatment, and outcome provides invaluable insight into the true efficacy of a treatment. Among other things, it can also help inform ideal surgical approaches, or screen for patients who might not be good candidates for a particular procedure. 

For years, orthopedic companies have focused on improving surgical execution. The next frontier may be understanding which patients benefit most from which interventions, and why.

As it relates to the original medical imaging data, we haven’t even broached the discussion around the concept of data ownership and other non-clinical uses for this trove of information once you have it. These aspects will be covered in more depth in future columns, but, unquestionably, having access to medical imaging data for a given population or specific demographics at your fingertips can offer valuable insights. A dataset that includes hundreds or thousands of reconstructed bone models will provide a statistically significant view on the average bone sizes and ranges you can expect in that population. Certainly, this can be helpful to iterate on new implant shapes and sizes or validate existing product lines. 

Conclusion

Without a doubt, personalized medicine is here to stay, and medical imaging data is the engine driving it. To continue to innovate in this space, it is imperative to set up best practices for managing, processing, and interpreting the growing influx of data in the most efficient and effective way possible.  

References

1. tinyurl.com/odt260731
2. tinyurl.com/odt260732

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Mike Lawrenchuk is a senior business development manager at Materialise, responsible for supporting the North American medical device industry. For nearly 20 years, he has helped companies in the orthopedic industry implement patient-specific solutions and additive manufacturing processes. Lawrenchuk also supports the cranial maxillofacial and dental markets in a similar capacity. His recent focus has been on utilizing AI techniques and design automation to help groups streamline their pre-operative planning and production workflows. Lawrenchuk is a graduate of the University of Michigan with a BSE and MSE in biomedical engineering.