Applications of Artificial Intelligence in Surgical Skills Assessment and Transfer
Creators: Soleymani, Zeynolabedin
Publisher: name: University of Alberta Library
Year: 2023

<p>Safety and high-quality education are critical elements in the training of surgical residents before operation on real patients.In the conventional method of education and evaluation, trainees are educated and evaluated through traditional and qualitative methods that are expensive, time-consuming, and prone to mentor bias.Autonomous and quantitative surgical skills assessment and transfer approaches can improve the consistency, repeatability, and reliability of the evaluation and education in surgical training programs.Such methods can tackle various challenges faced in surgical education and training. These challenges include the requirement for more objective and standardized ways to assess surgical skills, the necessity for personalized training plans, and the need to make sure trainees are well-equipped to promptly detect frequent and life-threatening surgical errors before handling advanced surgical technologies, such as autonomous surgery systems, which are primarily based on the technologies in autonomous surgical skills assessment and transfer.The primary target in this work is to detect and utilize the underlying skills-related features of surgical trajectories to classify and improve the levels of expertise of users in surgical training platforms by incorporating state-of-the-art AI-powered approaches, domain knowledge in the field of surgery, and addressing drawbacks of current work in this area.The first portion of this thesis thus focuses on machine/deep learning-based approaches that extract skills-related features from trajectory or video data to classify or cluster surgical trials according to their performance.The outcome of the presented methods is a class label indicating the performance level of the performed trial or a visualization of the performed task relative to that of other experts, intermediates, and novice users.In this way, in addition to the final rating assigned to each trial, users will receive additional instructive feedback about their performance, weaknesses, and strengths in different characteristics of surgery and develop more effective training plans.Additionally, the presented unsupervised data visualization methods can help trainees to track their progress over time in terms of learning curve, providing a clear measure of improvement and helping to build confidence and motivation.As a result, trainees will be provided with personalized feedback and training plans tailored to their individual needs, which can improve the quality and efficiency of their training.The acquired knowledge about surgical skills assessment and transfer methods have the potential to play an important role in the development and implementation of autonomous surgery systems.Such insights about the skills and techniques used by expert surgeons can be utilized to inform the design and development of autonomous surgery systems, making it possible to mimic and improve upon these techniques.Moreover, surgical skills assessment methods can be used as a benchmark to evaluate the performance of autonomous surgery systems to ensure that the technology is safe, effective, and performing at the highest possible performance.The second half of this thesis thus contains the work related to autonomous surgery and methods that facilitate this important futuristic technology.According to the research community, learning from demonstration and reinforcement learning are both critical components in the development of autonomous surgery systems.Learning from demonstration allows the robot to learn from expert surgeons by observing and replicating their movements and techniques.In this way, human dexterous trajectory encoding plays a crucial role in autonomous surgery as it allows for the replication of the skillful movements of an experienced surgeon with a minimum dimensionality of the data which reduces the overall computational demand.This approach can significantly reduce the time and resources required for robot programming, as the robot can learn from a skilled human mentor.Reinforcement learning allows the surgical robot to learn through operational trial and error, with the system adjusting its actions based on feedback from the surgical environment and/or human experts.This enables the robot to continually improve its surgical performance over time and adapt to new situations in operations.In this way, extracted features and acquired field knowledge in surgical skills assessment and transfer methods can be helpful for better training a reinforcement learning agent for autonomous surgery.This information can be used as a part of reward function to promote the training procedure of the reinforcement learning agent, enabling it to better understand the complexities of surgical procedures and make more accurate decisions.</p>