Performance and Capability Assessment in Surgical Subtask Automation

Abstract

Robot-Assisted Minimally Invasive Surgery (RAMIS) has reshaped the standard clinical practice during the past two decades. Many believe that the next big step in the advancement of RAMIS will be partial autonomy, which may reduce the fatigue and the cognitive load on the surgeon by performing the monotonous, time-consuming subtasks of the surgical procedure autonomously. Although serious research efforts are paid to this area worldwide, standard evaluation methods, metrics, or benchmarking techniques are still not formed. This article aims to fill the void in the research domain of surgical subtask automation by proposing standard methodologies for performance evaluation. For that purpose, a novel characterization model is presented for surgical automation. The current metrics for performance evaluation and comparison are overviewed and analyzed, and a workflow model is presented that can help researchers to identify and apply their choice of metrics. Existing systems and setups that serve or could serve as benchmarks are also introduced and the need for standard benchmarks in the field is articulated. Finally, the matter of Human–Machine Interface (HMI) quality, robustness, and the related legal and ethical issues are presented.

HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics

“…The application of novel methodologies to implement accurate systems to simulate the interaction between humans and robots advances the design of non-invasive strategies that provide medical support [ 53 ] or avoid risks in workplaces [ 54 ], such as collisions by using virtual sensors [ 55 ] or by human motion and behaviour prediction [ 56 , 57 ]. On the other hand, although Cobots are not yet fully accepted in industry, increasing evidence supports the valuable contribution of artificial intelligence in terms of feasibility and flexibility to achieve an improvement in security and communication [ 58 , 59 ].…”

Evolution of a surgical system using deep learning in minimally invasive surgery (Review)

“…The classification level of recognition is as follows: Level 1, awareness of the environment; Level 2, understanding the current status; and Level 3, prediction of the future status. The classification level of tasks, or Level of Task Complexity (LoTC), is as follows: LoTC 1, simple training tasks that are limited to surgical tasks such as distance considerations; LoTC 2, high training tasks; LoTC 3, simple surgical tasks; LoTC 4, advanced surgical tasks, such as suturing; and LoTC 5, complex surgical tasks, such as stopping sudden bleeding ( 30 ). The development of autonomous surgical robots should be aimed at accounting for the abovementioned classification levels.…”

Determining boundaries of accurate tracking for electromagnetic sensors

“…In current breast conserving surgery 8 mm electromagnetic (EM) sensors are used for tracking. The clinical accuracy of indirect image-guided surgery should maintain 2-5 mm root mean square (RMS) accuracy [3]. When tracking surgical tools and tissues it is important that the sensors and technology do not interfere with the surgeon and medical staff.…”