Deep Learning is Improving Surgical Training

Deep learning is changing the way surgeons are trained and assessed. For many years, video-based assessments (VBAs) have been used to help surgeons improve their skills. VBAs allow experts to watch videos of surgeries and give feedback to the surgeons. While this method has been useful, research shows it has some problems. It takes time, depends on human judgment, and can lead to mistakes or bias. Privacy concerns also arise because human reviewers handle sensitive data.

Deep neural networks (DNNs) are offering a new way to solve these problems. These advanced computer programs can analyze surgical videos and give feedback automatically. This makes the assessment process faster, fairer, and more accurate.

How Deep Neural Networks Help

DNNs bring many improvements to surgical training. One major advantage is that they provide real-time feedback. Instead of waiting days or weeks for an evaluation, trainees can get immediate advice. For example, DNNs can highlight when a surgeon needs to improve their technique or praise correct movements. This allows trainees to learn and fix mistakes quickly.

DNNs are also consistent in their evaluations. Unlike human reviewers, they do not get tired or distracted. This means their feedback is reliable every time. In addition, DNNs can work with basic equipment, like a simple camera, which makes them useful even in hospitals with fewer resources.

Challenges to Overcome

Even though DNNs are promising, recent research indicates there are still some challenges to solve. First, DNNs need a lot of data to work properly. This means many surgery videos need to be recorded and labeled carefully. Creating this data takes time and effort.

Another challenge is trust. Surgeons need to feel confident that the feedback from DNNs is accurate and fair. To address this, researchers are using explainable AI (XAI). This technology shows why the DNN gave certain feedback, making it easier for surgeons to trust the system.

Benefits for Healthcare

Using DNNs in surgical training can improve healthcare in many ways. By providing faster and better feedback, they help surgeons develop their skills more effectively. This leads to fewer mistakes during surgeries and better outcomes for patients.

DNNs also make it easier to train surgeons in areas with fewer trainers or resources. They can be used in hospitals around the world, making high-quality training available to more people.

The Future of Surgical Training

In the next 5 to 10 years, DNNs could become a standard part of surgical training. They save time, reduce bias, and improve learning for trainees. With these benefits, DNNs are set to play a big role in making surgeries safer and healthcare systems better.

Deep learning is an exciting step forward for surgical training. It combines advanced technology with practical solutions to help surgeons and patients alike.

Definitions:

Video-Based Assessment(VBA): A method for evaluating surgical skills using video recordings to provide formative feedback

Deep Neural Networks(DNNs): Advanced AI systems that process data and recognize patterns, improving the speed and accuracy of VBAs

Explainable Artificial Intelligence(XAI): AI tools that make decisions understandable, building trust in automated systems

Reference:

Deep Learning for Video-Based Assessment in Surgery" by Erim Yanik, PhD, Steven Schwaitzberg, MD, and Suvranu De, ScD (2024)