AI Device to Improve Surgical Training in Keyhole and Laparoscopic Procedures

The current methods for evaluating and training in these essential skills are usually time-intensive, laborious, limited in accessibility, and expensive. A new system empowered by artificial intelligence (AI) is now set to enhance the training process for trainee surgeons in laparoscopic surgery, enabling them to complete their training more quickly and efficiently.

Researchers at Heriot-Watt University (Scotland, UK), along with academics from the University of Dundee (Scotland, UK), are developing a self-training platform known as AILap. This system uses AI to track and assess human movements in real-time. It integrates machine learning and machine vision technologies with affordable physical box trainers, which are essentially surgical simulators, to provide instantaneous feedback for refining keyhole surgery techniques and skills.

A recent survey had revealed that more than half of the trainees who completed their fellowship training were not proficient in laparoscopic suturing. Moreover, surgical trainees reported that while laparoscopic suturing is the skill they are least proficient in, they also consider it the most crucial to master by the end of their training. AILap is designed to address this gap by allowing surgical trainees to practice independently and receive real-time feedback from the AI system, thus enhancing their suturing abilities. With a significant investment of GBP 600,000 from the UK government, the initiative will not only assist trainees but also enable clinical academics to restructure their training programs in order to accommodate and effectively teach more trainees with the assistance of AI.

“Laparoscopy training takes a significant amount of time to learn and currently requires access to training platforms and guidance from expert surgeons who are often time-poor. That’s why AILap technology has the potential to play an incredibly important role in supporting professional training in our public services and health systems,” said AILap project lead Dr. Mustafa Suphi Erden. “We hope AILap will enable training a greater number of surgeons without the need of an expert supervision. The technology will work with off-the-shelf components so it will be affordable and accessible for health care systems around the world.”

Related Links:
Heriot-Watt University
University of Dundee