How AI can Evaluate Surgeons' Skills

One emerging application of artificial intelligence (AI) is the evaluation of surgical technical skills.

Many surgeons are evaluated on complication rates, mortality rates, length of stay, blood loss, patient’s length of recovery, and recurrence rates. However, objectively evaluating the technical competence of a surgeon can be challenging.

Both artificial intelligence (AI), machine learning (ML), and deep learning are being used to assess technical skills in surgery, using computer vision (VC).

Computer vision is a subset of artificial intelligence. It is how computers can “see”.

Computer vision enables computers to derive informations from digital images and videos.

Computer vision can interpret and analyze information of data collected from surgeries performed with laparoscopic or robotic surgery.

A recent study used AI to identify operative steps in laparoscopic sleeve gastrectomy and found that quantitative data can be obtained from surgical videos with 85.6% accuracy using artificial intelligence.

AI can assess surgical skills in a variety of ways:

• The use of electromagnetic sensors attached to instruments

• Hand-mounted eye trackers

• Force and sensors attached to surgical instruments

• Direct capture from the robot

Robotic instrument vibrations can be measured to determine how forcefully the instruments are handled.

AI can objectively collect data such as:

• The number of times an instrument comes into contact with certain structures.

• Eye trackers can determine the surgeon’s object of focus.

This data can be used to assess a surgeon’s skill or experience.

Collecting this data and using machine learning to analyze it provides insight into a surgeon’s strengths and weaknesses. It can help identify which skills and maneuvers are important for good patient outcomes and efficient procedure length.

Surgical technical skill can be evaluated by technologies that are already built into surgical equipment, such as the da Vinci Systems recording device.

One outcome that is often used to assess surgical skill is the length of the total procedure being performed.

Using recordings of laparoscopic or robotic surgeries, machine learning can be implemented to analyze the time it takes to perform critical tasks during the surgery, not simply the overall procedure time.

Pauses during the surgery that are considered flow disturbances can be evaluated.

Each step of the surgical procedure can be analyzed, and the time it takes to complete various phases of the operation can be compared.

Using these algorithms, experienced surgeons can be differentiated from beginners within the first 10 seconds of starting a task with 90% accuracy.

Surgical robotic systems provide valuable data that can be utilized by AI to objectively evaluate a surgeon’s technical skill.

These algorithms can also detect patterns that lead to better outcomes, which may help in training future surgeons.