Robotic Surgery and Machine Learning
In the wide range of AI’s current real-world goals, machine learning healthcare applications seem to win the race for the past few years.
According to an article by Economic Times, India in 2019 has a deficiency of 600,000 doctors and 2 million medical attendants and nurses. There is also a lack of properly trained staff.
In India, there is one doctor specialist for every 10,189 individuals. WHO (World Health Organisation) prescribes a proportion of 1:1,000, inferring a deficiency of 600,000 doctors; and the nurse-patient proportion is 1:483, inferring a lack of 2,000,000 nurses.
This is where machine learning comes in. Machine Learning systems can be developed for health care services, that have the ability to change the diagnosis and treatment of diseases, guaranteeing that patients get the correct treatment at the perfect time.
The expectation is that this innovation can be used to help doctors and patients settle on better health care choices. These technological advancements will give assistance, helping care specialists analyze significant signals in large data that would somehow or another stay covered up. Below are a few machine learning applications in surgical robotics:
1.) Robotisation of Suturing
Suturing — or the method of closing up an open injury or entry point — is a significant step of surgery, yet it can be a tedious part of the procedure. Robotisation can possibly diminish the length of surgeries and surgeon exhaustion.
An expected 44.5 million delicate tissue operations are performed yearly in the U.S. On account of stomach medical procedures, intricacies, for example, “leakages around the creases” happen in approximately 20% to 30% of cases in human surgeries. Advancements like automation of suturing will lessen these inconveniences.
2.) Machine Learning for Assessment of Surgical Skillsets
The assessment of surgical skills has customarily been a subjective practice frequently directed by other trained surgeons. Machine learning can be used to evaluate surgeon performance in robot-assisted minimally invasive surgery, analyzing features like completion time, path distance, depth perception, speed, smoothness, etc.
A similar test was carried out and the test assessment framework apparently classified with 85% of accuracy. This is a promising outcome offering a positive light to the future of machine learning in robotic surgery.
3.) Machine Learning for Improved Surgical Robotics
Certain surgeries like neurosurgery, require extremely sensitive maneuvering. No matter how trained a surgeon is, there is a possibility that his hands might shake during the surgery or during stitching. This may result lead to life-changing consequences for the patient or even death. The rate of complications in surgery range from an estimated 3–17%. If such surgeries are to be performed by trained robots, they would perform the surgery with the utmost precision.
Concluding Thoughts
Potential uses of machine learning in the medical field many and address numerous aspects including preparing, tasks and clinical information. Advancements which can demonstrate their value in the long run by preventing surgeon fatigue and decreasing hospital costs will be the best options.
Robotized suturing would require machines to undergo a broad training and testing framework for maximum accuracy. Expenses related to preparing doctors on how to use the robots should likewise be considered.
The present absence of information makes it hard to foresee the time span that would be required. Machine learning flourishes with powerful and massive amount of data and inclines toward pattern recognition.
Thus, the aim should be to create robots that are human-safe, smart, dependable and versatile to an unpredictable domain.
