Computer vision is a form of AI which mimics the visual abilities of humans. Computer vision algorithms are trained to detect patterns in visual data with the goal of object detection, object classification,edge detection, and more.
Machine learning algorithms aimed at processing visual data, like images and videos, have found application in the medical field. The ability of modern machine learning systems to mimic human cognition makes them a good tool for analyzing medical data. Such analysis can include processing of x-ray scans, MRA imaging, images received from a microscope, etc.
A significant AI use case in healthcare is the use of ML and other cognitive disciplines for medical diagnosis purposes. Using patient data and other information, AI can help doctors and medical providers deliver more accurate diagnoses and treatment plans. Also, AI can help make healthcare more predictive and proactive by analyzing big data to develop improved preventive care recommendations for patients.
A significant portion of computer vision systems in healthcare are used for medical diagnosis purposes. Computer vision can aid healthcare professionals in analyzing large amounts of data more effectively and significantly reduce human error for better patient outcomes.
Computer vision is finding more and more uses in the healthcare industry everyday, and has become standard practice for many healthcare professionals. Many decision-makers in the industry are already aware of how computer vision healthcare systems can benefit daily medical practice and improve patient outcomes, making it easier for innovative products to be introduced.
The computer vision healthcare market is growing every year, with new key players introducing smart medical image analysis constantly. One being NVIDIA, which introduced several computer vision healthcare projects, one of them being an AI platform aimed at helping healthcare specialists automate radiology interpretation. Another large company which embraced computer vision in healthcare is Google, which went as far as to create an entire healthcare subdivision within the company.
There are also a large number of successful startups in the healthcare industry related to computer vision and AI. One of the examples is Aidoc, an AI-powered solution which analyzes medical imaging to provide comprehensive solutions for flagging acute abnormalities across the body. Another is Vara, a computer vision platform for automatic breast screening analysis.
Overall, the market potential of computer vision for healthcare is largely untapped, as computer vision algorithms are constantly evolving and improving, making it possible to use in more and more applications throughout the medical field.
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Let's now take a look at how computer vision is transforming patient care and medical analysis through its applications in medical imaging, medical devices, and at-home patient treatment.
The ability of computer vision algorithms to perform object detection and classification has great potential at improving the accuracy of the diagnosis and its timeliness.
The solutions in this domain rely on neural networks trained on large amounts of data, like a collection of CT scans, to detect abnormalities in a matter of seconds. Given a large enough collection of data at the start, a computer vision system can perform medical image analysis with 99% accuracy in a fraction of time it would take a healthcare worker to analyze the same scan.
Systems like these can reduce the workload of medical staff while maintaining high quality of healthcare services. They can effectively process real-time and aggregate data, making it possible to not only perform patient treatment, but improve medical statistics by analyzing medical scans collected overtime.
Many computer vision imaging applications have found their way into the consumer market, where they help people monitor their key health parameters easily. One of the projects our computer vision developer team has had the chance to work on is an app for easy analysis interpretation of testing strip results.
The user takes a picture of a testing strip within the app and receives accurate test results within seconds. The app is powered by various computer vision algorithms which determine the color of each reagent on the strip, compare them to a color chart and calculate the amount of certain chemicals.
Medical strips are often misinterpreted due to poor lighting conditions or difficulty in determining the reagent color. Apps like these mitigate the discrepancies and are essential for people with visual impairments.
Computer vision algorithms can be used to analyze medical images like scans, but various devices to improve their usability and help patients with various chronic conditions to monitor their health.
Certain medical devices can be hard to use for people with no medical training, and testing results can be misunderstood or neglected, resulting in poor patient outcomes. Computer vision systems can help patients correctly interpret the data provided by their wearable medical devices and coupled with an app, can help patients keep a close record of their health data.
For example, blood glucose monitors provide crucial information on the patient’s health, but can be heard to read and the results hard to interpret. We have had the opportunity to create an app for people with diabetes, who are in need to constantly use glucose monitors.
The app helps patients accurately interpret test results by analyzing a photo of the device, identifying key parameters, and providing them in an easy to understand way.
Other wearable medical devices, like EMG sensors and respiration sensors, can also be augmented with computer vision to provide a better service.
One of the most popular areas of computer vision in general is facial recognition, or the ability of a computer system to detect a human face and track its position, determine facial expressions, and much more.
One of the subsections of facial recognition is eye tracking. Computer systems nowadays can easily track where a person is looking in real time without requiring a lot of processing power, which makes them perfect for mobile apps aimed at personal wellness.
Eye tracking is already used in a medical setting, mostly during experiments to determine where a person is looking for a long period of time or most often. However, eye tracking can be used in a treatment setting as well by incentivizing the user to perform various eye movements and tracking their position.
Apps like Eye Warm-Up gamify eye exercises, prompting the user to achieve daily and weekly exercise goals. The app tracks the user’s eye movements to rate how well the exercises are performed.
Computer vision can turn old medical practices which are difficult for people to follow into a fun and enjoyable process.
It is important to mention not every aspect of healthcare can or should be automated using computer vision. Before diving head first into the development of a new product, one should carefully assess whether the task at hand is a good fit for computer vision automation in general. For example, if the task is not already performed by a human, it will be extremely difficult to set goals and reach them, as there is no workflow for the system to automate.
Another important aspect of computer vision system development to consider is a dataset. Computer vision systems rely on large collections of visual data to train on, and if your data collection is small (under 100 images) or nonexistent altogether, the system will not be as accurate as you may hope. The more visual data is collected, the more accurate the system will be.
While creating a dataset for other computer vision projects can be relatively easy, medical data requires a different approach. If you are aiming at starting a computer vision healthcare company, you need to make sure you have access to medical images in a way which will adhere to local rules and regulations regarding medical data. Finding a lab or a medical facility willing to share medical data in a lawful way is the first step when it comes to computer vision healthcare system development.
Another challenge when working with medical data is complying with local and international regulations, one of them being the health insurance portability and accountability act, or HIPAA. Companies that operate with protected health information must put security measures in place to ensure HIPAA compliance. For example, medical records cannot be shared publicly, which includes not only a doctor discussing a patient’s diagnosis publically, but data leaks as well.
When working with medical data, a company must pay a lot of attention to the security of their systems and the way data is stored. A high degree of software security is essential to prevent data leaks and patient data deanonymization, which can be remedied by choosing the right technology stack and a reliable software development company.
Same goes for medical data used in a training dataset. The data must be collected in a legal way and should not contain any information about the patient.
The choice of a tech stack highly depends on the nature of the project and varies from system to system. To start with, create an outline of your project, describing key parts of the system, as well as describe a user flow. One of the key steps is to determine what visual data the system will process, like photos or videos, and what data will be processed: photos taken with a smartphone, high-resolution medical scans, low-quality webcam video.
Next, measure the size of the dataset, and if you don’t have one, estimate how much data you will be able to collect. Another important aspect which affects the tech stack is the manner of analysis - would the system need to work in real-time or would it analyze aggregated data.
After all of this data is put together, you can talk to a software development team with experience in developing complex AI projects to see which technology stack will fit your project best.
When it comes to computer vision healthcare system development, one of the things that greatly affect the success of your project is a software development team you choose. There are several factors you should keep in mind when hiring computer vision developers:
Hiring computer vision developers with experience in your respective field is crucial when it comes to developing a system which will fit your industry standards. Developing healthcare software requires a high degree of understanding medical data processing, rules and regulations surrounding medical data, and best practices when it comes to creating products for people with medical issues. Look for AI software developers with experience in the medical area.
As you are already aware, not all projects are a good fit for computer vision, and your software development partner needs to be open about that. Look for companies which are ready to test your idea first through creating a proof-of-concept first before going all in into the development process. For example, we at Businessware Technologies provide AI validation services - we build prediction models to assess accuracy and create initial prototypes to determine how effective your system will be in the end.
If you are interested in creating your own healthcare AI, or have questions about the development process, feel free to contact us for a free consultation.