Developing an intelligent tutoring system for robotic-assisted surgery instruction

Area of Science:

• Medical Education
• Surgical Technology
• Human-Computer Interaction

Background:

• Robotic-assisted laparoscopic surgery (RALS) is rapidly growing, with over 750,000 procedures in 2017.
• Current da Vinci surgical system training is limited, often isolating psychomotor skills from cognitive and perceptual ones.
• Novice surgeons face challenges in acquiring the necessary skills for RALS due to training limitations.

Purpose of the Study:

• To explain the data acquisition process for fundamental robotic surgical tasks.
• To develop an entry-level intelligent tutoring system (ITS) for robotic surgery skills.
• To offer insights and recommendations for future ITS development in surgical training.

Main Methods:

• Developed a computer-based intelligent tutoring system (ITS) using the generalized intelligent framework for tutoring (GIFT).
• Captured procedural information through video, instruction sets, and flowcharts.
• Validated and encoded data into the ITS with input from experienced surgeons.

Main Results:

• Successfully developed an entry-level ITS for training basic robotic suturing skills.
• The ITS integrates cognitive and procedural training for novice robotic surgeons.
• The system provides a foundation for learning fundamental robotic surgery skills.

Conclusions:

• The ITS effectively trains cognitive and procedural skills for basic robotic surgery tasks.
• This approach addresses limitations in current robotic surgery training.
• Lessons learned and recommendations are provided for future ITS development in surgical education.