Artificial intelligence makes major advances in surgery possible
Artificial intelligence is spreading throughout healthcare and bringing many benefits. Surgery is influenced by AI, among other things. In preoperative planning, for example, AI processes vast amounts of patient data to create personalized treatment plans, transcending the one-size-fits-all approach.
AI-powered intraoperative guidance supports precision medicine by combining patient-specific preoperative data with real-time insights during surgery. This approach allows surgeons to tailor their techniques to each patient’s unique anatomy, adapt to anatomical changes, identify the safest surgical pathways and minimize risks.
Gabriel Jones is the CEO of Proprio, a surgical medical technology company that developed Paradigm, a navigation platform that integrates AI, machine learning, light field and depth sensor technologies to provide a real-time 3D view of the anatomy and the surgical scene.
Here, Jones talks about AI in preoperative planning and intraoperative guidance, as well as AI in visualization and digital twins, and predictive analytics.
Q. You suggest that AI can process vast amounts of patient data prior to surgery to create personalized treatment plans, going beyond the one-size-fits-all approach. Explain what this looks like.
A. Understanding complex anatomical relationships is essential for effective surgical planning and execution. Traditional approaches often relied on static imaging, which provided only a snapshot of a patient’s anatomy.
However, with emerging technology, we are able to digitalize the entire surgical field. Using advanced AI and machine learning, we can create dynamic, real-time visualizations of a patient’s unique anatomy.
Imagine a scenario where a surgeon can interact with a fully digitized model of a patient’s anatomy before surgery. This model integrates data from different sources – such as MRI, CT scans and patient history – to create a 3D view that can be manipulated in real time. This allows surgeons to assess the location they are looking for, as well as their relationship to the surrounding anatomy, allowing for highly customized surgical plans.
This approach moves us away from a one-size-fits-all mentality as it integrates each patient’s individual data. Surgeons can simulate different surgical techniques and visualize possible outcomes based on the specific anatomical variations present. The result is a personalized treatment plan that increases precision and ultimately improves patient safety and outcomes.
Q. Where does AI-powered intraoperative guidance come from and how can it support precision medicine?
A. Traditionally, surgeons have relied on preoperative imaging and surrogate markers for guidance during procedures. Although these tools have served us well, they can quickly become outdated if the patient moves or reference points shift during surgery. This allows surgeons to work with inaccurate information, which poses significant risks.
AI-driven intraoperative guidance changes this by combining preoperative imaging data with light field and depth sensor technologies, powered by artificial intelligence. This integration enables the delivery of real-time anatomical visualizations, continuously tailored to the patient’s current positioning.
For example, if a patient’s body shifts during surgery, the system can instantly adjust the visual data to provide accurate guidance in real time throughout the procedure. This combination of AI with imaging technologies streamlines workflow, reduces unnecessary radiation exposure and improves overall surgical guidance.
The result is a significant improvement in surgical precision and safety for both patients and surgical teams. Surgeons can be confident that they are receiving the right data at the right time and that it is reflective of the patient’s anatomy, allowing surgeons to better accommodate a variety of skills and experience levels while reducing the potential for error.
Ask. Explain visualization and digital twins and discuss how AI can generate real-time 3D models of the surgical field and what that makes possible.
A. Visualization and digital anatomical twins are powerful tools for improving accuracy, safety, and surgical outcomes. A digital twin is a virtual replica of a patient’s anatomy that simulates and predicts real-world processes in real time.
By creating a digital twin, surgeons can virtually explore different surgical scenarios, test different approaches and predict outcomes before making decisions in the operating room. This capability enables treatment planning that is precisely tailored to each patient’s unique anatomy and specific circumstances.
By integrating light field and depth sensor technologies with AI, we can generate real-time 3D models of the surgical field – also known as digital twins. This allows surgeons to see under structures, around corners and over surfaces that are often invisible using traditional imaging techniques.
New surgical instruments allow a surgeon to visualize critical structures such as nerves and blood vessels in three dimensions, improving their ability to navigate complex anatomies.
This unprecedented level of visibility enables not only better planning, but also more informed decision-making during the operation. Surgeons can adjust their approach at any time, significantly improving accuracy and minimizing the risks associated with unseen anatomical complexities.
Q. You say predictive analytics can use data from previous procedures to predict patient outcomes. How does this help surgeons and strengthen precision medicine?
A. In surgery, knowledge is power. The more we understand about past procedures, the better we can inform future surgical practices. By capturing and analyzing surgical data from all cases, we can define and learn optimal outcomes from even the most basic or complex procedures.
Predictive analytics plays a crucial role in this process by examining patterns and outcomes of similar cases in addition to individual patient factors. Using AI algorithms, we can identify potential risks and complications based on historical data.
This means that before a surgeon even steps into the operating room, he already has insight into the best approach, tailored to each patient. By taking into account individual characteristics – such as anatomical variations, medical history and specific risks – surgeons can make highly data-driven decisions that optimize care.
This level of personalized planning improves patient outcomes and minimizes the risk of side effects. As we continue to refine predictive analytics capabilities, the goal is to enable surgeons to use this data for continuous improvement, ultimately raising the standard of care across the board.
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