RESEARCH STATEMENT and OVERVIEW

Automation of robotic surgery can improve the performance of surgeons and the quality of the life of patients. However, there are challenges in the automation of robotic surgeries that must be resolved. Surgical tools are widely used in healthcare and medicine for various purposes. 

Problem Statement: Automating learnings in robot-assisted surgery for surgeon’s training and assistance

Motivation: The research problem's outcome can be automated to give scores on training or assistance during the surgery. The surgical robots may be thought of as information-driven surgical tools that enable human surgeons to treat individual patients with greater safety, improved efficacy, and reduced morbidity than would otherwise be possible.

The research topic involves understanding three main areas: medical surgery, robotic path planning, and machine learning (deep learning).
In Robot-Assisted Surgery or MIS, deployment is based on an indirect manipulation of specialized surgical tools and operations through an indirect visualization of the surgical field captured by an endoscope (camera). The effectiveness of existing medical procedures can potentially be improved using robot-assisted tool steering. A typical surgical tool requires searching through all possibilities in a surgical environment. Investigation of path planning of a dextrous tool renders both computationally expensive and theoretically challenging problems to address.

We need algorithms to avoid collision of the tool with critical organs and reduce the search space while applying optimal path planning algorithms.  Procedural complexity is added during the planning of a surgical trajectory by considering additional non-linear trajectories and different risk structures. 
Also, the quality of surgery and the surgeon’s capability can be improved using intraoperative assistance systems. These systems aid in recognizing surgical actions during any intervention facilitate the interaction with technical additives and provide support depending on the surgery context and surgical actions. 

The decoupling between the planning stage and the execution stage relies on the underlying assumption that the surgical tool will execute the motion strategy computed by the planning stage successfully. Machine learning methods can be used to address both motion planning and motion execution problems. 
The experience of doctors is fairly needed to apply ML methods. For this, the patient-specific information needs to be combined with doctors' statistical information about human anatomy, physiology, and disease to produce a comprehensive computer representation of the patient, which can then be used to produce an optimized interventional plan.