Critical Reflection

During the start of the course, we were asked to write an introduction email to our professor about ourselves and what we set out to improve by the end of the course. The two goals that I set for myself was to improve on the clarity of speech and to improve on the language used both verbally and written. I am happy to say that I have improved in both fields as large to most parts of the course required us to paraphrase and have proper usage of language. The course also required me to give presentations to the class. Some ways that I used to improve on my communication skills is to rehearse the night and morning before so that I would be prepared for the actual presentation. During the rehearsals, I will think of ways to improve the content and language used for the actual presentation to keep it engaging to my audience. I believe the best way to further improve on my communication skills is to put what I have learned to practice. Other than talking to people and taking opportunities to present in front of a crowd, taking in constructive feedback will further enhance the learning experience in improving communication skills.

This project experience has been a delightful one as I had supportive teammates, Daryl and Aloysius. Throughout the project, Medi-Claw, we met some hurdles and had to change the dynamics of our project. Thankfully, through the meetings, the team was very constructive in how we can venture into a topic and make our proposed solution work. As the project progressed, we had to come up with a technical report and an oral presentation to present our proposed solution. Despite the overwhelming workload, the team managed to pull through by helping each other out in the content in our report and presentation to make sure that the project was comprehensive and holistic.

My main takeaway from this project is to realize that it is possible to have a very enjoyable experience doing a project if there is a sense of responsibility in each member and all of us will have to be team players. I am very thankful that Daryl and Aloysius have been very constructive, and everyone was appreciative of each other.

Overall, it has been a great experience throughout the module and it the lessons allowed me to learn the art of report writing as well as presentation skills. I am sure that this module will benefit me beyond my studies in SIT.

Annotated Summary

Wall, J, Krummel, T. (2020). The Digital surgeon: How big data, automation, and artificial intelligence will change surgical practice. Journal of Pediatric Surgery, 55. Retrieved from https://doi.org/10.1016/j.jpedsurg.2019.09.008

This article focuses on the potential impact of artificial intelligence [AI] to surgery in the future in three main areas: enhancement of training modalities, cognitive enhancement of the surgeon, and procedural automation. The development of AI surgical operations is more complex than diagnostic specialities as they require “preoperative planning”, instantaneous decision making to perform the operation that is based on highly complicated inputs in comparison to radiological and pathological interpretation. However, according to the article, the nature of surgical operations in relation to the development of autonomous cars are similar. Therefore, it is arguable that the application of AI to surgical procedures is eminently achievable. In the aspect of enhancement in the current training modalities, machine learning [ML] has been applied to vast and abundant data sets “from training to stratify surgical skill and recommends personalized training strategies to improve individual deficiencies.”. The article also highlights that cognitive enhancement will allow for the access to a collective medical data and experiences of experts from any part of world for any unpredicted findings during an operation. This will enhance the decision-making ability of the surgeon during complex surgical procedures. Early attempts have been conducted to integrate AI with surgical robots like the Mako and the Aqublation system to reduce tissue damage and the treatment of benign prostatic hypertrophy respectively. Although these surgical systems have yet to be integrated with AI on full fledge, attempts on autonomous performance such as suturing have been made as a catalyst for autonomous surgery.

This article also provides useful ideologies that can be implemented in our system, Medi-Claw, a modification of the da Vinci Surgical System. With the implementation of AI, the surgical system will have a library with full spectrum of resources from ML, to the ability to get instantaneous support from other surgeons around the world. This will enable Medi-Claw to perform minimally invasive surgeries autonomously with high precision and accuracy which relates to the purpose of Medi-Claw.

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 

https://asmedigitalcollection.asme.org/BIOMED/proceedings/DMD2018/40789/V001T07A008/271978

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/

Technical Report Draft 1 (Background + Benefits)

Background

According to the DaVinciSurgery 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 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 possibily 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 sound judgement.

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 off 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.

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.

Design Summary and Analysis Final Draft

According to the article “Seabin using plastic to fight plastics” (2019), The Seabin Project highlights the consequences of microplastics (2-5mm) in the ecosystem. It also discusses the benefits of the Seabin as a holistic solution to the pollutants in the sea. The article mentions that many sea-creatures ingest microplastics as it resembles their food source with its size and appearance. Microplastics also gather organic wastes at an accelerated rate compared to other inorganic particles which have acute detrimental effects on the marine environment. As a countermeasure, Seabin functions by ingesting water within its vicinity and traps any detritus through its filtration system. By integrating sensors into the Seabin, it can double up as a monitoring device, allowing the Seabin Project to conduct comprehensive studies on its effectiveness and potential improvements. Because of the accessibility, simplicity and dynamic functions of the Seabin, it is a favorable method of marine pollution countermeasures used by governments all around the world. Despite some of its limitations, Seabin stands out in its efforts to reduce water pollution, mainly due to its sustainability in its production and operations, as well as keeping its efficiency on par with other sea cleaning products, specifically the Interceptor (The Ocean Cleanup, n.d.), in fulfilling its purpose.

Despite its limitations, Seabin stands out from other sea cleaning products for its ability to catch microplastics as small as two millimeters in size that enhances its efficiency. Due to the design of the Seabin, they are unable to collect mass amounts of debris at each point of time. The Seabin Overview Book (2017) states that the catch bag can hold up to a maximum mass of 20 kilograms of waste at each point of time and the approximate amount that Seabin can capture a day is one and a half kilograms. Despite the efforts of redesigning and enlarging its capacity, it will require many Seabins to collect an equivalent amount of plastic compared to other products such as The Interceptor from The Ocean Cleanup project, “50,000 kilograms” (The Ocean Cleanup, n.d.) of plastic to be exact. However, the Seabin is capable of capturing microplastics down to the size of two millimeters with the purpose of reducing “the risk to animals by being mistaken for food”, “eventually reaching our plates” (Seabin Project, 2019). In addition to its edge, the Seabin is also efficient as it “runs 24/7” (Seabin Project, 2019) just like its counterparts, the Interceptor (The Ocean Cleanup, n.d.), making it on par in the capability of collecting debris at any time of the day. Therefore, they have made up for their disadvantage by being efficient in filtering microplastics and operating round-the-clock.

Another advantage that Seabin possesses is its sustainability. Although the Seabin can function by itself, there is still a need for minimal manpower to maintain Seabin’s operations. According to the Seabin Overview Book (2017), to maintain a Seabin, it has been advised that they should be “checked twice a day and emptied as needed” (Seabin Overview Book, 2017). There is also a need for a Seabin to be cleaned and checked monthly to keep it operational and if any of the catch bags are found to be damaged, it can be replaced instantly just like normal trash bins on land (Seabin Overview Book, 2017). After the damaged catch bags are being replaced, they can be recycled into new catch bags, making it an ecosystem on its own. In comparison, the Interceptor (The Ocean Cleanup, n.d.) would demand more manpower as they require “multiple barge exchanges per day” (Interceptor-Spec-Sheet, 2019). Additionally, The Seabin V5 (2020) website mentioned that a Seabin is mostly made from recycled ocean wastes and the running cost of it is only up to three dollars a day. Therefore, making the Seabin a sustainable solution to marine pollution.

In conclusion, there are several exclusive features for both the Interceptor and the Seabin. However, the Seabin has additional advantages over one of its recognized counterparts, the Interceptor (The Ocean Cleanup, n.d.). The Seabin edges over the Interceptor (The Ocean Cleanup, n.d.) by having the ability to filter microplastics and being a sustainable product with an ecosystem in place, which is absent in the Interceptor (The Ocean Cleanup, n.d.). Therefore, in my opinion, Seabin makes a superior solution to marine pollution.

References

Seabin. (2020). The Seabin V5- The Seabin Project – For Cleaner Oceans. Retrieved on February 17, 2020 from https://seabinproject.com/the-seabin-v5/

Seabin Project. (2019). Seabin using plastic to fight plastics. Retrieved on February 17, 2020 from https://seabinproject.com/seabin-using-plastic-to-fight-plastics/

Seabin Overview Book. (2017). Retrieved on February 17, 2020 from https://seabinproject.com/wp-content/uploads/2019/04/seabin_overview_book.pdf

The Ocean Cleanup (n.d.). Rivers. Retrieved on February 17, 2020 from https://theoceancleanup.com/rivers/

The Ocean Cleanup, Interceptor. (October 26^th 2019). Interceptor-Spec-Sheet. Retrieved on February 17, 2020 from https://assets.theoceancleanup.com/app/uploads/2019/10/191021_Interceptor-Spec-Sheet.pdf

Design Summary & Analysis Draft 2

According to the article “Seabin using plastic to fight plastics” (2019), The Seabin Project highlights the consequences of microplastics (2-5mm) in the ecosystem. It also discusses the benefits of the Seabin as a holistic solution to the pollutants in the sea. The article mentions that many sea-creatures ingest microplastics as it resembles their food source with its size and appearance. Microplastics also gather organic wastes at an accelerated rate compared to other inorganic particles which have acute detrimental effects on the marine environment. As a countermeasure, Seabin functions by ingesting water within its vicinity and traps any detritus through its filtration system. By integrating sensors into the Seabin, it can double up as a monitoring device, allowing the Seabin Project to conduct comprehensive studies on its effectiveness and potential improvements. Because of the accessibility, simplicity and dynamic functions of the Seabin, it is a favorable method of marine pollution countermeasures used by governments all around the world. Despite some of its limitations, Seabin stands out in its own ways in efforts to reduce water pollution, mainly due to its sustainability in its production and operations, as well as keeping its efficiency on par with other sea cleaning products, specifically the Interceptor (Rivers, 2019), in fulfilling its purpose.

Despite its limitations, Seabin stands out from other sea cleaning products for its ability to catch microplastics as small as two millimeters in size. Due to the design of the Seabin, they are unable to collect mass amounts of debris at each point of time. The Seabin Overview Book (2019) states that the catch bag can hold up to a maximum mass of 20 kilograms of waste at each point of time, which adds up to an approximate amount of one and a half kilograms a day per Seabin. Despite the efforts of redesigning and enlarging its capacity, it will require many Seabins to collect an equivalent amount of plastic compared to other products such as The Interceptor from The Ocean Cleanup project, “50,000 kilograms” (River, 2019) of plastic to be exact. However, Seabins can capture microplastics down to the size of two millimeters with the purpose of reducing “the risk to animals by being mistaken for food”, “eventually reaching our plates” (Seabin using plastic to fight plastics, 2019). Therefore, they have made up for their disadvantage of being unable to capture waste in bulk by being able to filter microplastics.

An advantage that Seabins possesses is its sustainability. Although the Seabins can function by themselves, there is still a need for minimal manpower needed to maintain Seabins’ operations. According to the Seabin Overview Book (2019), to maintain a Seabin, it has been advised that they should be “checked twice a day and emptied as needed” (Seabin Overview Book, 2019). There is also a need for Seabins to be cleaned and checked monthly to keep it operational and if any of the catch bags are found to be damaged, it can be replaced instantly just like normal trash bins on land. After the damaged catch bags are being replaced, they can be recycled into new catch bags, making it an ecosystem. As compared to their counterparts, the Interceptor (Rivers, 2019), it would require more manpower as they require “multiple barge exchanges per day” (Interceptor-Spec-Sheet, 2019). Additionally, The Seabin Project Website (2020) mentioned that Seabins are mostly made from recycled ocean wastes and the running cost of a Seabin is only up to three dollars a day. Therefore, making the Seabin a sustainable solution to marine pollution.

In addition to its edge, the Seabin is also designed to operate efficiently. The Seabins “runs 24/7” (Seabin using plastic to fight plastics, 2019) just like its counterparts, the Interceptor (Rivers, 2019), making it on par in the capability of collecting debris at any time of the day.

In conclusion, there are several exclusive features for both the Interceptor and the Seabin. However, Seabin has additional advantages over one of its recognized counterparts, the Interceptor (Rivers, 2019). The Seabin edges over the Interceptor (Rivers, 2019) by having the ability to filter microplastics and being a sustainable product with an ecosystem in place, which is absent in the Interceptor (Rivers, 2019). Therefore, in my opinion, Seabin makes a superior solution to marine pollution.

References

Seabin Project (2019). Seabin using plastic to fight plastics. Retrieved on February 09, 2020 from https://seabinproject.com/seabin-using-plastic-to-fight-plastics/

Seabin Overview Book (n.d.). Retrieved on February 09, 2020 from https://seabinproject.com/wp-content/uploads/2019/04/seabin_overview_book.pdf

The Ocean Cleanup (n.d.). Rivers. Retrieved on February 9, 2020 from https://theoceancleanup.com/rivers/

The Ocean Cleanup, Interceptor (October 26^th2019). Interceptor-Spec-Sheet. Retrieved on February 12, 2020 from https://assets.theoceancleanup.com/app/uploads/2019/10/191021_Interceptor-Spec-Sheet.pdf

Design Summary and Analysis Draft 1

According to the article “Seabin Using Plastic to Fight Plastics” (2019), The Seabin Project highlights the consequences of microplastics (2-5mm) in the ecosystem. It also discusses the benefits of the Seabins as a holistic solution to the pollutants in the sea. The article mentions that many sea-creatures ingest microplastics as it resembles their food source with its size and appearance. Microplastics also gather organic wastes at an accelerated rate compared to other inorganic particles which have acute detrimental effects on the marine environment. As a countermeasure, Seabins function by ingesting water within its vicinity and traps any detritus through its filtration system. By integrating sensors into the Seabin, it can double up as a monitoring device, allowing the Seabin Project to conduct comprehensive studies on its effectiveness and potential improvements. Because of the accessibility, simplicity and dynamic functions of the Seabin, it is a favorable method of marine pollution countermeasures used by governments all around the world. Despite some of its limitations, Seabins still stands out in its own ways in efforts to reduce water pollution, mainly due to its sustainability in its production and operations which makes it easy to maintain, as well as its efficiency in fulfilling its purpose.

Despite its limitations, Seabins still stands out from other sea cleaning products for its ability to catch microplastics as small as 2 millimetres in size. Due to the design of the Seabins, they are unable to collect mass amounts of debris at each point of time. The Seabin Overview Book (2019) states that the catch bag can hold up to a maximum mass of 20 kilograms of waste at each point of time. Despite the efforts of redesigning and enlarging its capacity, it would still require many Seabins to collect an equivalent amount of waste as compared to other products, such as the Interceptor from The Ocean Cleanup project. However, also according to the Seabin Overview Book (2019), Seabins can capture microplastics of 2 millimetres or larger in size with the purpose of reducing “the risk to animals by being mistaken for food”. Therefore, they have made up for their disadvantage of being unable to capture waste in bulk.

An advantage that Seabins possess is its sustainability. Although the Seabins can function by themselves, there is still a need of minimal manpower needed to maintain Seabins’ operations. To maintain a Seabin, it has been advised that they should be checked twice a day and emptied as needed. There is also a need for Seabins to be cleaned and checked monthly to keep it operational and if any of the catch bags was found to be damaged, it can be replaced instantly just like normal trash bins on land. After the damaged catch bags are being replaced, they can be recycled into a new catch bags which can then in turn replace other damaged catch bags, making it an ecosystem. According to The Seabin Project Website (2020), Seabins are mostly made from recycled ocean wastes and on top of that, the running cost of a Seabin is only up to three dollars a day. Therefore, making it a sustainable solution to marine pollution.

Other than being a sustainable solution to marine pollution, the Seabin has also been designed to be able to operate efficiently. The Seabins operate round the clock through a pump, making it one of the more efficient sea cleaning products in the market by collecting surface debris despite the almost any condition at any time of the day or night. With its well-thought design, they can also be easily deployed or repositioned to locations where there are is a higher concentration of marine pollution. With this flexibility, it could make Seabin one of the more efficient sea cleaning products.

In conclusion, these advantages make Seabin an optimal solution to marine pollution as it is not only able to filter smaller debris compared to other sea cleaning solutions, they are also able to do it sustainably and efficiently.

References

Seabin Project (2019). Seabin using plastic to fight plastics. Retrieved on February 09, 2020 from https://seabinproject.com/seabin-using-plastic-to-fight-plastics/

ABC News July 28, 2016. Surfers set to turn Seabin dream into sales reality. Retrieved on February 02, 2020 from https://www.abc.net.au/news/2016-07-28/seabin-project-nears-reality-for-wa-surfers-with-2017-sales-plan/7665270

Seabin Overview Book (n.d.). Retrieved on February 09, 2020 from https://seabinproject.com/wp-content/uploads/2019/04/seabin_overview_book.pdf

Kickstarter (n.d.). Seabin Project. Cleaning our oceans one marina at a time. Retrieved on February 09, 2020 from https://www.kickstarter.com/projects/1902540740/seabin-project-cleaning-our-oceans-one-marina-at-a