Artificial intelligence has revolutionized various prominent sectors of the market through its numerous benefits and applications. Years of research have made AI worthwhile in understanding the human way of thinking, acting, etc. There are still some areas where it needs improvements, such as emulating the thought and behavior of humans. In the last decade, we have seen millions of processes where AI has simplified the process while enhancing results. Apart from all the industrial sectors, healthcare has benefitted the most from this revolutionary technology.
AI has changed the healthcare industry in numerous ways, from delivering precision to simplifying various internal processes. One such AI subset is computer vision technology that has enhanced the functionality of the healthcare sector by delivering accurate results by analyzing the contents of visual data such as images and videos.
Computer vision in healthcare acts as an extra set of eyes that assists the medical professionals in various processes from basic tasks to complicated tasks which involve a human’s life. Since AI-based computer vision has been introduced, it has completely changed the healthcare sector for good. Let’s understand AI-based computer vision.
AI-based Computer vision: What is it?
AI-based computer vision can be described as a subset of AI that enables computers and various related systems to extract relevant information from videos, digital images, and various other inputs. Further, the extracted information is analyzed to take appropriate actions. In simple terms, if AI enables the machines to think, computer vision technology enables machines to see.
Computer vision has a number of similarities with the human eye. The only difference is that we have a lifetime of context training our eyes to extract the exact information. At the same time, computer vision requires time to analyze various parameters of visual input and process the information to derive efficient results.
Machines throughout the industries use computers for enhanced functionality; computer vision trains these machines to perform a visual inspection with the help of cameras, computer vision algorithms, etc. For instance, AI computer vision working on a product sorting line would be much efficient in sorting out products with defects rather than a human eye. It enhances the results along with the execution time. AI-based computer vision is the most preferred technology used in various industries from energy, automotive, manufacturing, healthcare, and much more. The total market valuation of AI computer vision is projected to surpass 49 billion US dollars by the year 2022. It is expected to create various new milestones in the next coming years.
The working of computer vision is not that complicated. Delivering accurate results requires a ton of data to match the ideal result. It repeats the process of analyzing the data until it reaches the perfect result. It had to be fed with numerous sets of data to make it understand the various levels of the perfection of any product. For instance, a bottle-producing plant will have to feed it a range of images showing minor to major defected bottles. The computer vision will analyze those images and learn to sort out the defective bottles that are defected over a fixed percentage. Computer vision technology uses deep learning, which is a type of machine learning and a convolutional neural network. These advanced algorithms enable it to self-learn and tech itself to deliver precise results.
AI-based Computer Vision: How is it Transforming Healthcare?
Healthcare is of the booming sectors in terms of technology. It uses a vast percentage of all the available technology. AI-based computer vision has taken healthcare to a whole new level. In healthcare, a human’s life is at stake sometimes, and every decision requires precision; computer vision in healthcare delivers that precision. It has completely transformed the healthcare industry for good. Here are some points that run in favor of computer vision technology in transforming the healthcare industry.
1. Precise Measurement of Health Factors
There are times when people ignore various health indicators that tell them about the risk of a particular health condition. Computer vision technology is used to analyze these indicators and forecast a procedure that decreases the risk of any fatality. To enhance the course of treatment, a number of doctors and other medical professionals are making the most of this technology. With the help of computer vision algorithms, they can determine the amount of blood loss in various surgeries and help prevent blood loss past a dangerous level. In various surgeries such as c-section and others, there is a chance of excess loss of blood. The doctors just hold the blood-strained sponge in front of the scanner, and it measures the amount of the blood. The right healthcare technology consulting service can offer insight into how computer vision technology could help these patients by alarming the doctors if the drained volume of blood passed a dangerous level. Similarly, it can help determine the body fat percentage and the sugar levels in the patients by analyzing the images. However, the doctors have to feed various images to it to learn about the level of body fat and other parameters.
2. Counting of Cells and Image Recognition
Louis-Charles Malassez invented the hemocytometer more than a century ago, a chamber designed to count red blood cells accurately. Fast forward to the present day; computer vision is used to identify and count cell nuclei not only more accurately than humans can but much faster. To achieve this, an artificial intelligence model is trained to detect cell nuclei. From there, sample images are added with annotations, and then machine learning models create neural networks. When microscopic images are sent to a computer vision model, the number of cells in seconds is calculated. This process can be changed indefinitely, as one GPU chip can process more than 100 simultaneous queries. This means that drug development researchers can test many more compounds much faster and more accurately than ever before. New drugs can hit the market years earlier, saving lives and reducing drug development costs.
3. Surgical Records
The transition from a “paperless office” to electronic surgical logs was perfectly reasonable as part of the productivity increase of the computer revolution, but the use of computer vision in the operating room maybe even deeper. Monitoring activities in the outermost region, from draping patients to closing surgical wounds, is now changing the way records are kept. Anesthesiologists still compile handwritten data between surgeries, but now their time can be used more efficiently. Another advantage is the prevention of unintentional seizure of surgical instruments, which, according to the American Society of Anesthesiologists, already showed 4,500 to 6,000 cases per year in the United States in 2018. Computer vision systems will also be set up to monitor instruments and procedures to make electronic accounting even more accurate.
4. Medical Imaging
Computer vision and in-depth learning can be used to read and convert 2D scanned images into interactive 3D models so that healthcare professionals can understand the patient’s health in detail. 2D images, even when taken multiple times from a wide-angle, cannot provide as complete a view of a person’s brain or heart as an interactive 3D model. This allows radiologists to thoroughly examine scanned images and easily detect signs of even the slightest disturbances without spending a lot of time scanning.
Also Read- How to Develop a Medical Imaging Analysis Software Solutions?
5. Patient Identification
Authentication of patients’ faces during admission and throughout the course of treatment is another area of whether artificial intelligence, integrated into the workflow of medical facilities, can play a critical role in healthcare. Misidentification of patients is detected more than 90 percent of the time before injury occurs. However, between 2013 and 2015, 7,600 false patient events occurred in 181 US hospitals over a 2.5-year period, and approximately 9 percent of these errors resulted in injury or death. The cause of death is different because patients are not resuscitated in the outermost regions because doctors chose the wrong health card that included a non-resuscitation order. Patients were given another prescription or food that they could not eat. By using face authentication associated with a patient’s MRN at every step, hospitals and caregivers can reduce these catastrophic events.
6. Image Analysis
Computer vision technology is used in healthcare to analyze scan reports and identify patterns that may indicate a possible health condition. For example, a New York hospital used a computer vision application that allowed computed tomography to be analyzed and the presence of neurological diseases to be determined in an average of 1.2 seconds. This is 150 times less than what human doctors would take to analyze CT scans and draw conclusions to confirm the presence of such disorders. Such experiments act as a stepping stone to the full application of artificial intelligence and computer vision in even more effective healthcare applications.
7. Medical Report Generation
Computer vision with natural language processing and generation can be used to generate potential reports from CT, X-ray, and MRI scanned images. These computer vision algorithms can be trained by feeding it various samples of CT and MRI scanned images with the past results and analysis reports generated by various radiologists and physicians. If there is sufficient training data, the system can generate reports independently based on the content of the images. Further, it can save a lot of time for radiologists and doctors who don’t have to spend a lot of time analyzing pictures, gathering conclusions, and then scribbling or writing down their findings.
The Bottomline
With great technological advancements being made daily, the future certainly holds numerous surprises for us. Computer vision in healthcare is making the treatment process effective and easy. In the next few years, it is possible to detect various diseases through image analysis and treat them with innovative solutions. From remote patient monitoring to determining the body fat through image analysis, AI computer vision has come a long way in transforming the healthcare sector.
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