Technical Report Draft 1
Background
According to the da Vinci Surgery website, the da Vinci surgical system is a robotic-assisted surgical system that enables surgeons to operate “minimally invasive surgery” using sophisticated surgical tools. The da Vinci System consists of 3 separate components. Firstly, the surgeon console presents the surgeon with the ability to control the surgical instruments while viewing the surgery in high-definition 3D. Secondly, the patient cart is placed beside the operating bed, embedded with the camera and tools for the surgeons to control. Thirdly, the vision cart is the bridge that communicates between the components and supports the high-definition vision system.
Even with such sophisticated design and technology, the da Vinci surgical system is still being limited as a master and slave system. This means that the da Vinci surgical system relies on human inputs to process and function. Based on the article “Accidents Happen” (2019), it states that KK Women’s and Children’s Hospital’s (KKH) sees an average of 60 to 70 cases that require stitches each week. With the absence of automated robotic technology, doctors are required to attend to these patients. With such reliance on human intervention even for cases that require small surgeries, it takes a toll on the workload of doctors. This thus decreases their working efficiency which possibly extends the working hours of doctors. According to The Washington Post, the article “Back to extremely long shifts for new surgeons? Study finds few negatives.” (2016) states that the average working hours of doctors fall between 16 to 28 hours per week. Such increased working hours would then affect a doctor's ability to make a sound judgment.
Thus, we introduce the concept of incorporating artificial intelligence (AI)into the system. Machine learning is a type of AI that allows computers to self learn through the analysis of patterns without any explicit coding needed. Unlike humans, robots are excellent at seeing patterns of big data and then producing an accurate list of predictions within a very short time span. With the incorporation of AI into the current technology, the system that previously required human intervention can now function on its own. This greatly cuts down the manpower needed for trivial cases ie. stitching.
Problem Statement
Medical robots have always assisted surgeons but they are limited to a master-slave system. This over-reliance of the surgeon’s attention even for minor injuries including small cuts and wounds decreases their working efficiency.
Purpose Statement
This report proposes an improvement to the current Da Vinci System to turn it into an ideal medical robot for performing auto-suturing on minor wounds. Through the implementation of specifically designed algorithms and medical data into the Da Vinci System, the robot will be able to perform suturing without the guidance of a doctor.
Proposed Solution
Introducing AI into an already well-established surgical robot like the da Vinci surgical robot, which has operated over 6 million successful surgeries, will help minimize the resources needed to develop an entirely new robot. The da Vinci system is equipped with a set of needle drivers, which are surgical instruments needed for performing suturing at different angles. By introducing machine learning into the da Vinci system, it can be thought of as an improvement to the system as the robot can perform suturing with well-designed suturing tools in the absence of a surgeon, while still having the master-slave option available for major surgeries.
Suturing autonomously by medical robots is made possible through the implementation of a trajectory planning algorithm. The Raven II surgical robot, similar to the da Vinci system, performed the auto-suturing with the integrated algorithm. Before suturing begins, the kinematics of the needle held by the end effector of the medical robot is analyzed thoroughly. This is to translate the current and end pose of the end effector into data. The trajectory planning algorithm is broken down into two components, “GoToPoint” and “PathGeneration”. “GoToPoint” brings the robot to the target point, while “PathGeneration” constructs a trajectory path for the robot to insert the needle into the tissue. Once the trajectory path is generated, the medical robot will begin from its initial position and follow the trajectory. The algorithm allows surgeons to request for auto-suturing, however, for safety concerns, surgeons can interrupt and halt the process.
The implementation of large amounts of data by artificial intelligence can be processed by a system structure similar to that of Artificial Intelligence in Medical Epidemiology (AIMe). A data set is preloaded into the system and used for comparison against every action taken by da Vinci.
Benefits
The implementation of artificial intelligence (AI) allows the robot to apply machine learning. The robotic hands will be able to learn the procedures sequentially and therefore enables it to perform the procedures semi or fully automatically.
The implementation of AI also indirectly affects the efficiency of hospitals, especially the Accident and Emergency (A&E) departments. The robotic hands will be able to replace doctors or surgeons with tasks like stitching, freeing them up for other patients that require more attention. This will boost the efficiency of the A&E department of the hospitals which means that more patients can be treated within the same amount of time compared to having doctors or surgeons being there physically to stitch the patients up.
With the implementation of machine learning through AI, the robotic hands will learn and perform procedures in a standardized and sequential manner. The robotic hand will pull the algorithm of the task from a database that it is assigned to perform. This ensures that the procedures performed by the robotic hands are sequential and standardized. Hence, it minimizes the possibilities of errors on tasks made by humans through the implementation of AI. Furthermore, machine learning enables robotic hands to analyze uncertainties such as the dimensions of the wound, for accurate error propagation which further enhances its capabilities.
Evaluation
Though trajectory planning algorithm allows medical robots to perform auto-suturing, certain limitations and settings need to be in place for the operation to work. The suturing performed by the Raven II robot was not tested on a tissue model due to the algorithm not being able to detect tissue movement. In a realistic clinical setting, patients move as they breathe, causing tissue movement. The angle of entry point for the insertion of the needle is crucial as it determines the exit point of the needle. The trajectory planning algorithm is not advanced enough to set the optimal angle of entry by itself, hence, a desired entry point is adjusted by the surgeon before suturing begins.
The prediction of the AI model depends on the analysis of the data input. Such analysis is dependent on the availability of the data set that is present in the AI model for analysis. Thus, the prediction is not perfect at a 100% success rate as there may be a presence of a new situation where data on dealing with it is absent.
At the end of the day, the accuracy of the system in terms of diagnosis and performing is still not guaranteed. The current algorithm is not sophisticated enough to take tissue movement into consideration. The trajectory planning algorithm is still in its early development stage; hence, more work needs to be accomplished for auto-suturing to come into fruition. Although there is a possibility of failure by the predictive model for analysis, the implementation of the system is guaranteed to be free from human errors when performing suturing.
Methodology
Research articles and websites were used as references for information and data to complete this report.
Secondary research
In building a strong design proposal, the team did extensive research using the official product website on the Da Vinci Surgical System to identify their strengths so as to integrate them into MediHand and also their weaknesses for possible modifications to make MediHand a comprehensive product in its field. Research articles were used such as the auto-suturing algorithm performed by the Raven II robot, which supports the proposal report as the Raven II is like the da Vinci system. The secondary sources were also used to strengthen the credibility of the design proposal through examples and events such as having over six million successful surgeries done by the Da Vinci Surgical system and the suturing autonomously with the Raven II Surgical Robot through artificial intelligence.
Conclusion
In conclusion, for MediHand to be an autonomous surgical hand capable of performing suturing, two modifications: implementation of machine learning and algorithms for suturing should be implemented. These modifications allow Medi-Hand to be capable of performing suturing autonomously without doctors’ intervention. This creates a much ideal situation in which doctors’ can be more focused on much urgent matters.
References
AIME Healthcare Sdn Bhd (n.d.). AI4Good. Retrieved from https://betterproposals.io/proposal/index.php?ProposalID=sE6L0xI0UtPfZjBBINS3JXP2vpVXlh2M18n36aOFVuc&ContactID=-4khI1mEueUpsUws_jFpjaxL0_ACn7uMzUgG_Gl55UE
Channel NewsAsia. (2019, November 26). Accidents happen - but when might a child need plastic surgery for scar therapy? Retrieved from https://cnalifestyle.channelnewsasia.com/wellness/plastic-surgery-children-accident-12123156
Dehghani, H., Farritor, S., Oleynikov, D., Terry, B. (2018). Automation of Suturing Path Generation for da Vinci-Like Surgical Robotic Systems. Retrieved from
Intuitive. (n.d). Retrieved from https://www.davincisurgery.com/
Kang, R., Branson, D. T., Gulielmino, E., & Caldwell, D. G. (2012). Dynamic modelling and control of an octopus inspired multiple continuum arm robot . Computer & Mathematics with Applications, 64(5), 1004–1016. Retrieved from https://www.sciencedirect.com/science/article/pii/S0898122112002234
The Washington Post. (2016, February 03). Back to extremely long shifts for new surgeons? Study finds few negatives. Retrieved from https://www.washingtonpost.com/news/to-your-health/wp/2016/02/02/back-to-extremely-long-shifts-for-new-surgeons-study-finds-few-negatives/
Comments
Post a Comment