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3 Trends that MedTech Companies Should Consider When Developing AI-based Surgical and Interventional Assistance Applications

The potential for the use of artificial intelligence in medicine is beyond what anyone on the outside looking in could fathom in a single sitting. But one thing that should be noted, especially when it comes to surgical interventions, is that as this technology continues to grow, physicians will be exploring a new world of abilities when it comes to making proper treatment decisions and life-saving interventions.

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Artificial Intelligence (AI) and its application in robot-assisted surgeries have consistently proven to increase efficiency by saving practitioners time and improving the accuracy of diagnosis. With algorithms able to provide precise, automated surgical planning, create 3D replications from 2D images, and track all the data during and after procedures, surgeons are equipped with more tools than ever to greatly increase efficiency and raise the bar for patient outcomes. 

Particularly with image-based navigation and intraoperative assessment, AI can identify key information that is unclear in the original imaging. This gives surgeons more precise measurements and provides greater quantitative context before finalizing treatment decisions. Down the line, this technology democratizes the most difficult to conduct procedures, as the computer vision software constructs much of the initial groundwork and therefore outcomes are less dependent on the practitioner’s experience. 

When it comes to the application of AI in surgical interventions, there are three key trends in the industry that MedTech companies need to keep in mind:

AI for Improved Treatment Predictions

Physicians operate with the greatest efficiency when working in tandem with artificial intelligence applications. While an experienced orthopedist will look at an image and diagnose the best path forward, they may easily switch gears once they go into surgery and see the targeted area more clearly. With artificial intelligence, algorithms capture that clarity for the orthopedist right from the start. With so much information and so many images available, the software learns from thousands of cases and images, as well as the decisions that were made based on those images, and subsequently predicts what will be the most effective treatment plan for that patient.

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Although nothing can replace the wisdom of the most experienced physicians, we can use AI to take this available information and make it more accessible to everyone, training the system based on the accumulated data and providing recommendations in a way that’s reproducible and accessible to all physicians. This will play a huge role in helping to reduce variations among procedures and greatly improve patient outcomes.

Using AI for 3D Reconstruction from Collected 2D Images 

Our anatomy is three-dimensional and therefore 3D information is essential for a proper assessment of medical conditions. However, 3D imaging is expensive and there is not always reimbursement for it. It also can require radiation, which comes at a risk and even if it’s done, it’s not easy to get those preoperative images to the operating room and to present them in the proper context. In many cases, practitioners are working from 2D images, and with AI, a lot of information can be extracted from an assortment of these images alone.

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Cutting edge software developed by companies like RSIP can learn what the 3D model would look like, based on the 2D model, and teach neural networks to reconstruct specific 3D anatomies from these 2D samples. The reconstructed 3D model then can rotate and come up with new pictures that physically can look, rotate, and measure, knowing the specific type of anatomy and how it should be shaped based on the patient-specific information it gleans from the 2D model.

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Through this technology, surgeons can be provided rich 3D modeled bones, arteries, and much more that yield critical data for surgical planning and navigation, bypassing high-cost, high-radiation methods. Without computer vision, the surgeon can only imagine, but with AI and a 3D model, they can now look at every detail to quantify that 3D information.

Using Computer Vision for Soft-Tissue Surgery

This is where the greatest potential is still yet to unfold for medtech companies.

Although there’s a lot of research and development activity among competitors who are racing to develop this, the industry is yet to see this technology brought to production in a significant way. Leading surgeons are telling the industry that they want the computer vision software to help them find, measure, and automatically assess parts of the anatomy that require much greater sophistication than trying to segment rigid structures such as bones.

Eventually, this will be such a lucrative subfield of robot-assisted surgery that there will be a stand-alone category of multiple classes of AI-enabled soft-tissue robots, as well as non-robotic solutions for laparoscopy and endoluminal imaging. It’s important to note that while the DaVinci was strident in revolutionizing precision and ergonomics, its high expense and large footprint leaves much to be improved upon for wider access and greater capability. 

Moving forward, partnerships between data scientists and surgeons will be vital to building upon the research being conducted to recognize its future clinical applications by MedTech companies. As computer vision systems and improved algorithms begin to distinguish between complex material and track the targeted tissue areas within vigorous surgical situations, AI-enabled systems and their users will be able to execute more elaborate surgical tasks. 

The future utilization of AI in medicine is endless, but over the next few years, I believe these three areas will jumpstart the revolution for robot-assisted surgical intervention and it behooves medical technology companies to pay closer attention to these developments in AI capabilities to incorporate within their own technological developments. Once the software can give surgeons accurate reads for reliable treatment decisions, create 3D models from 2D scans, and develop use-cases for computer vision to execute soft-tissue surgical interventions, our visual intelligence technology will provide an array of new possibilities for saving lives in the long run. 

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