Top 11 Computer Vision Applications in Healthcare
Top 11 Computer Vision Applications In Healthcare
Healthcare is one of the areas most heavily touched by artificial intelligence and emerging computer vision technologies. Developments and strides in these technologies bring about many significant shifts in the diagnosis and treatment methods in healthcare.
Such technologies, which mean a radical change in the workload for doctors, are just as important for the patients. Remote treatment, accurate diagnosis, correct treatment, and advanced access to healthcare services are just a few of the benefits that these technological developments bring to those patients.
How are computer vision technologies changing in healthcare? More importantly, what exactly are these technologies, and how are they used? Read on to learn about the latest and most useful computer vision technologies and the role they play in healthcare.
What Is Computer Vision and How Does It Operate: Healthcare Example
Before we look in more detail at how computer vision is used in healthcare and which solutions offered by computer vision technologies are used, let's briefly talk about what computer vision is.
Computer vision (CV) is the ability of machines to analyze digital images by converting them into data - or extracting data from digital images. Computer vision mimics human vision and, like human vision, operates in a highly complex way (For more detailed information about Computer Vision, visit this blog post).
There are some prominent uses of computer vision technologies in healthcare. These uses illustrate the functionality of different computer vision solutions. Recent technological developments in computer vision technologies have accelerated the process, making computer vision a rival to human vision, so to speak. It is possible to perform object detection, image classification, and image segmentation with these technologies even without any coding experience. Build your own computer vision application from scratch in 3 minutes on Cameralyze no-code platform, instantly send application results to third-party tools such as google sheet and dropbox with premium third-party integrations.
Let's briefly summarize three main functions of computer vision:
Object detection (see object detection application work with Cameralyze) is the ability of computers to recognize and locate desired objects on visual, video, or live surveillance video. This function is one of the first steps in many later computer vision technologies. Image classification is when machines analyze different criteria about the content of an image and classify (label) the image into an appropriate class. Image segmentation seems to be quite similar to classification, but it is a very different process. It is the division of an image into multiple segments based on the similar objects it contains.
Computer vision technologies are not only used in healthcare. We can say that it is also easy to access these technologies that make professional and daily life easier. Although we mentioned that computer vision works in a complex way, it is possible to benefit from technologies without getting involved in this complexity. Cameralyze is a web-based platform that offers no-code computer vision solutions and it is possible to access many solutions, such as object detection, human detection, and visual classification, in a very simple way at very affordable prices.
Top Applications of Computer Vision in Healthcare
We have already mentioned that computer vision technologies have different applications in healthcare, and some of them stand out. We have compiled the most important ones among these prominent uses for you.
Detecting Tumors in the Brain and Lungs
Detecting tumors is one of the most important applications of computer vision. So much so that the application process, which first started with the detection of tumors in the brain, has also started to detect tumors in the lungs, with recent developments and developments in technology continuing.
Deep learning technologies are among the key developments that make this change and development possible. Such algorithms can learn from real experiences to understand what a healthy organ should look like.
Algorithms fed with healthy and unhealthy radiology images of the lungs and/or brain can detect even irregularities too small for the human eye to see. Recognizing the structure of these organs and the shape/pattern of the healthy organ, the technology detects distortions in this pattern even when they are very small. The use of technology saves lives by enabling early diagnosis and early treatment of brain and lung tumors. Many experts believe that even the most trained radiologists cannot detect the small abnormalities detected by computer vision. Technology may compete with the human eye, but it is better at seeing details than the human eye.
Medical Imaging and Searching for Anomalies
Most medical conditions require X-rays so that doctors can understand what is happening. Although X-rays are an essential cutting-edge application, the complexity of the scan makes it challenging for doctors to identify organs and detect pathologies. Where medical imaging comes in by pointing out the placement of organs on X-Ray images so that defects and abnormalities can be easily identified, this enables both quick judgment and accurate diagnostics.
How does computer vision work in medical imagining? To answer this question, we need to go back to the functions of computer vision that we discussed earlier in this article. Typically, object detection algorithms are used in the scanning process. These algorithms not only locate organs in X-ray scans but also directly detect abnormalities in organs because they are heavily fed data about how healthy organs should look. Computer vision technologies are not only used in X-rays. They can also detect abnormalities in mammograms and ultrasounds, which have similar operating principles. Typically, lung tumors, breast legion, and tissue degeneration can be detected early in scans by computer vision algorithms.
Early Stage Cancer Detection With Computer Vision and AI
It is a well-known fact that early diagnosis is life-saving for cancer patients. Unfortunately, in many types of cancer, the later the diagnosis, the later the treatment. For example, in light of the data we have, it is known that in the detection of lung cancer, in 70% of cases, the disease cannot be detected until the later stages of cancer.
Computer vision technologies have an essential place in the early detection of multiple types of cancer. Among the cancers that can be detected by these algorithms are lung cancer, brain cancer, breast cancer, and skin cancer. In the detection of lung cancer, algorithms fed with data from many X-ray images can detect distortions and tumors too small for the human eye to see. Likewise, tumors and abnormalities that can be missed in mammograms can be detected at an early stage. In skin cancer, algorithms developed using data obtained from dermoscopic images compare the malignancy of a spot or mole on the skin with previous data and accelerate detection. In the case of skin cancer, it is a very difficult process, even for a specialist doctor, to understand whether a skin mole or a spot is cancerous or not. However, an algorithm trained with real-life data can make the most accurate predictions based on the data it analyzes.
Training Medical Staff With Algorithms and Computer Vision
It is well known that computer vision technologies have a wide range of uses, not only for the detection of diseases but also for the training of healthcare personnel. Traditional training and practice models are no longer the only options for healthcare professionals, especially surgeons. Just like in pilot training, healthcare professionals who learn surgery and treatment procedures and practices through a simulation can gain intensive practice and receive very detailed feedback thanks to these simulations. These technologies, which offer students the opportunity to make mistakes and learn from their mistakes, have an extremely important place in effective learning.
Just like the flight simulations found in pilot training, simulations are being prepared for healthcare professionals on topics such as highly realistic surgery, suturing, and patient care. Although students are free to make mistakes in these simulations, they learn in detail from every mistake they make. The best part of the simulations is that they provide "learning by doing."
Computer Vision for Coronavirus (Covid-19) Control
The Covid pandemic has played an important role in highlighting the growing importance of technological advances in human life. In a time when all of life is digitized and computerized, the heaviest burden is on the shoulders of the health sector. The health sector made extensive use of computer vision technologies in its fight against the coronavirus. At a time when healthcare personnel was at significant risk, computer vision-enabled digital X-rays to diagnose patients.
Computer vision algorithms have been used effectively at many different stages in the fight against Covid-19. These phases can be divided into disease detection, prevention, and treatment.
In the detection of Covid-19, computer vision algorithms were used to understand and diagnose the spread of the disease to the lungs, just like in tumor and cancer detection. The detection of changes and abnormalities caused by the virus in the lungs on chest X-rays was achieved with the object detection function.
It is known that "image classification" algorithms are used in tomography scanning, which is another highly effective method in the diagnosis of the disease. With computer vision, it became possible to detect the parts of the lungs of infected individuals that were affected and altered by the virus.
Meanwhile, CV solutions were utilized in another very important issue in the course of the pandemic. In order to prevent the spread of the disease, many technologies such as "masked face detection," "thermography," and "germ screening" were on the front line of the war against the virus in daily life.
Masked face recognition technology relies heavily on training algorithms with visual data containing masked people. In this way, it was possible to determine from security cameras whether citizens were wearing masks, the most basic and effective protection method in the fight against the pandemic.
Another important prevention technology was thermography. Thermography is based on the processing of images. Here, the body temperature of the people in the live images is determined. In this way, individuals with high fever can be identified without the need for close contact.
Germ scanning, on the other hand, is the ability of algorithms to distinguish microscopic traces of viruses and germs on certain surfaces from existing images. At the time of its introduction, this technology was 90% effective.
How to Monitor Patient Health with Computer Vision
Our smart watches, smart wrists, calorie counters, and pedometers... There is no doubt that technology has largely taken over health checks. Computer vision technologies are crucial for health checks. They can measure doctors' performance in surgery, monitor patients' vitals, and raise alarms in unexpected situations. These technologies can be used inside and outside the hospital.
In addition to being quite common, we can briefly examine how this technology works and where it is used effectively. Tracking the disease is one of the most important processes between diagnosis and treatment.
Of course, patient follow-up is not limited to these. In fact, it is a vast and complex field that involves both computer scientists and health scientists. It makes it possible to track many neurological diseases with facial recognition systems and benefits patients and healthcare professionals in many areas, from diagnosis to follow-up and treatment. Computer vision-assisted patient monitoring plays a very important and effective role in the diagnosis and follow-up of the following diseases: dementia, depression, healthcare, physiological measurement, apathy, heart rate monitoring/anomalies, and rare neurologic diseases.
Some procedures that can be performed with computer vision-assisted patient monitoring:
- Monitoring disease by analyzing facial features
- Tracking illness by analyzing gestures and facial expressions
- Diagnosis and monitoring of depression by analyzing facial features
- Diagnosis and monitoring of anxiety with gesture and facial expression analysis
- Creating a database for percentage analysis of psychiatric diseases
- Emotion analysis (Build your own app in three minutes with Cameralyze)
- Accessible apps for the visually impaired
- Monitoring practices for elderly care
Machine-Assisted Diagnosis With Computer Vision
We often reiterate the importance of computer vision technologies for accurate diagnosis and treatment. Accurate diagnosis, which is the first stage of the right treatment, can be achieved at extremely high rates thanks to advances in computer vision. Small details that may be overlooked by doctors are taken into account thanks to the analysis of machines, thus ensuring that the patient receives the correct diagnosis and, as a result, the treatment they need. For example, computer vision technologies can quickly determine whether a brown spot, which can easily be mistaken for a mole, is cancerous tissue or just a skin mole.
We have mentioned examples of this frequently in the features above. Let us briefly list examples of where computer vision is used in accurate diagnosis:
- Diagnosis of skin cancer (recognition of potentially cancerous spots with image segmentation)
- Diagnosis of brain tumors (detection of tumors and tissue disruptions that can be missed by object detection)
- Diagnosis of lung cancer (detection of small tumors and legions by object detection and image segmentation)
- Diagnosis of depression and anxiety (matching facial expressions and symptoms with facial recognition)
- Thermography (measurement of body temperature on video)
Timely Detection Of Diseases With Computer Vision Technologies
Early diagnosis is very important in the treatment of diseases. Many life-threatening diseases can be treated, and the patient's life can be saved if diagnosed on time. Often it is the slow or subtle symptoms that delay diagnosis. For example, an X-ray may not yet show a small tumor. However, if the same X-ray is analyzed by computer, the invisible abnormality can be detected. Computer vision applications allow for the timely diagnosis of diseases. It paves the way for patients to receive the treatment they need.
As mentioned in the previous examples, the diagnosis of diseases is accelerated by the fact that very small symptoms that can easily escape the human eye can be detected by algorithms in X-ray, mammography, and ultrasound results, as well as in tomography outputs.
In addition, the detection of anxiety and depression, which are used for patient follow-up, with facial recognition and facial expressions plays an important role in the diagnosis of these mental disorders that require very fast action, even if the patient is not aware of it.
Remote Patient Monitoring: Computer Vision Solutions
Treatments can take a long time. The recovery process may need to be checked and monitored periodically. Patients may not prefer to stay in the hospital for long periods, or the available capacity of hospitals may not allow for this. Computer vision solutions are used to monitor patients in their own homes. Image tracking makes fall detection possible. The system notifies when the patient faints or falls. This technology can be used not only for patients but also for older people. In this way, in case of urgent need, even if the patient cannot notify, the relevant health personnel will be aware of the patient's condition.
Lean Management in Healthcare
The use of computer vision technologies in disease detection, accurate diagnosis, and treatment is extremely important. On the other hand, the management of hospitals continues to become easier with computer vision technologies. Considering that administrative technologies are mostly used in the retail sector, let's talk about how this can be used. For example, people counting is the easiest way to identify the number of people in images, videos, or live recordings. In the event of potential danger, a hospital must know how many people are in the area, or when precise statistics are needed, such as during a pandemic, people counting becomes crucial.
Hospitals, like other offices, are places where a lot of data entry, statistics collection, and recording are required. Tracking the number of people and delegating office work to AI that can be automated increases the time available to care for patients. For example, relying on "people counting with computer vision" technologies to keep track of people coming into the hospital or using "text recognition" technologies to digitize documents is an important choice that saves a lot of time and money.
We mentioned that people counting technology is more often used in the retail sector. Please visit the link to use this highly effective technology without code.
Online Dental Treatment
It is likely to be one of the most interesting applications on the list: dental treatment. Let's explain this use with a short example. Imagine you have a patient complaining of a toothache; he/she is currently out of town but reaches out to you to ask what to do. You examine the photos of your patient, and there is nothing visible in the tooth. When you analyze these photos with an object detection algorithm, it detects tooth decay. As a result, you advise yours patient on what they can do about it and reduce their pain until they come to you for an examination. For a much simpler example, imagine not having to go to the doctor for regular dental check-ups. Some companies have already started using computer vision to monitor the development of simple ailments such as dental plaque! All you need to do for this kind of development is to create a visual data set with Cameralyze, and then it's all up to the application, thanks to its visual detection function.
To do this, it is enough to create a dataset with images of healthy and unhealthy teeth and then train an object detection and image segmentation algorithm with these datasets. It then becomes possible to automatically detect cavities that are not large enough to be seen or dental tartar that requires careful examination!
It sounds like the distant future, but it's very simple and can be done in just a few steps! Try creating a dataset with Cameralyze. For example, use the labels caries and non-caries. Create your set with enough images. Then the app will be able to easily detect decay in the photos you upload! This is very easy to do with object detection!
Throughout this article, we've covered how computer vision can provide vital solutions. In healthcare, the impact of computer vision on data analysis is undeniable. On the other hand, you may have questions about how data is shared in such a private area as health.
The protection of your data has become an issue that needs to be addressed with digitalization. At this point, the solution is not to stay away from technology but to find the right provider. Cameralyze, for example, offers you a guarantee of the security of your data. The platform takes privacy very seriously, protecting your data with the utmost care and allowing only you to access it.
Whether it's healthcare or retail, if you want to advance in your industry, you need to embrace new technologies. So, how? With Cameralyze, you can benefit from flexible, affordable, and fast solutions. Sign up now to get access to the latest AI solutions for free!
A summary of what has been mentioned up to this point in our article:
There are many solutions made possible by computer vision technologies. These solutions can be used effectively both in healthcare and in many areas of your professional life.
Computer vision technologies have an important role in the early diagnosis of many diseases, from cancer to depression.
Dental treatment is simple and possible with computer vision technologies.
With computer vision technologies, patient follow-up is much easier than before, and these technologies are highly accessible.
Utilizing computer vision technologies in hospital management enables more successful management.
With the help of algorithms, artificial intelligence, and computer vision, great advances can be made in many fields.
Although computer vision technologies cannot yet replace doctors in the diagnosis and treatment of diseases, they can take much of the burden off doctors and prevent life-threatening mistakes.
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