Artificial intelligence is spreading throughout healthcare, bringing with it many benefits. Among other areas, surgery is being impacted by AI. In preoperative planning, foe example, AI processes vast amounts of patient data to create personalized treatment plans, moving beyond the one-size-fits-all approach.

AI-driven 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, adapting to anatomical changes, identifying the safest surgical paths and minimizing 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 anatomy and the surgical scene.

Here, Jones speaks about AI in preoperative planning and intraoperative guidance, as well as AI in visualization and digital twins, and predictive analytics.

Q. You suggest prior to surgery, AI can process vast amounts of patient data to create personalized treatment plans, moving beyond the one-size-fits-all approach. Please explain how this looks.

A. Understanding complex anatomical relationships is essential for effective surgical planning and execution. Traditional approaches often relied on static imaging, which only offered a snapshot of a patient's anatomy.

However, with emerging technology, we're capable of digitizing the entire operative field. By harnessing 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 prior to surgery. This model incorporates data from various sources – such as MRI, CT scans and patient history – to create a 3D representation that can be manipulated in real time. This allows surgeons to assess the location of what they are looking for but also their relationships with surrounding anatomy, enabling highly tailored surgical plans.

This approach shifts us away from a one-size-fits-all mindset, as it integrates each patient's individual data. Surgeons can simulate different surgical techniques and visualize potential outcomes based on the specific anatomical variations present. The result is a personalized treatment plan that enhances precision and ultimately improves patient safety and outcomes.

Q. Where does AI-driven 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. While these tools have served us well, they can quickly become outdated if the patient moves, or reference points shift, during surgery. This can leave surgeons working with inaccurate information, posing significant risks.

AI-driven intraoperative guidance changes this by merging preoperative imaging data with light-field and depth-sensor technologies, powered by artificial intelligence. This integration allows for the delivery of real-time anatomical visualizations continuously aligned with 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 fusion of AI with imaging technologies streamlines workflow, reduces unnecessary radiation exposure and enhances overall surgical guidance.

The result is a substantial improvement in surgical precision and safety for both patients and surgical teams. Surgeons can trust they're receiving the right data, at the right time and that it's reflective of the patient's anatomy, which better equips surgeons with a variety of skill and experience levels while reducing the likelihood of errors.

Q. Please explain visualization and digital twins and discuss how AI can generate real-time 3D models of the surgical field and what that enables.

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 various surgical scenarios, test different approaches, and predict outcomes before making decisions in the operating room. This capability enables treatment planning precisely tailored to each patient's unique anatomy and specific circumstances.

Through the integration of light-field and depth-sensor technologies with AI, we can generate real-time 3D models of the surgical field – aka digital twins. This allows surgeons to see beneath structures, around corners and across planes often invisible using traditional imaging techniques.

With new surgical tools, a surgeon can visualize critical structures such as nerves and blood vessels in three dimensions, enhancing their ability to navigate complex anatomies.

This unprecedented level of visibility enables not only better planning but also more informed decision-making during surgery. Surgeons can adjust their approaches in the moment, significantly improving accuracy and minimizing risks associated with unseen anatomical complexities.

Q. You say predictive analytics can leverage data from previous procedures to forecast patient outcomes. How does this help surgeons and bolster precision medicine?

A. In surgery, knowledge is power. The more we understand from past procedures, the better we can inform future surgical practices. By capturing and analyzing surgical data from all cases, we can define optimal outcomes and learn from even the most basic or complex procedures.

Predictive analytics plays a critical role in this process by examining patterns and outcomes from similar cases alongside individual patient factors. Using AI algorithms, we can identify potential risks and complications based on historical data.

This means before a surgeon even steps into the operating room, they have insights into the best approaches tailored to each patient. By factoring in individual characteristics – such as anatomy variations, previous medical history and specific risks – surgeons can make highly data-driven decisions that optimize care.

This level of personalized planning enhances patient outcomes while minimizing the risk of adverse events. As we continue to refine predictive analytics capabilities, the goal is to enable surgeons to leverage this data for continuous improvement, ultimately elevating the standard of care across the board.

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