In this article, we will talk about the role of computer vision in medical imaging, the way it improves healthcare outcomes and the medical image market tendencies.
Being a constantly growing market, medical imaging’sglobal value is foreseen to reach up to more than 48 billion USD in three years, and more than 144 Billion USD by 2028, according to Verified Market Research. No wonder, medical images form the biggest source of information in healthcare, the impressive 90%. In this case, we do not even dare to talk of daily manual study, unless doctors possess some superpowers to manage a huge flow of images and figures every day. Besides, if a radiologist or pathologist fails to process one of the thousands of different images in time, it is an open welcome to a wrong diagnosis or a serious medical error. Computer vision in medicine is a savior, fulfilling routine diagnostics and optimizing the workflows of diagnosticians. Dermatology, radiology, and pathology are especially lucky here. It connectsvarious methods of collecting, managing, and analyzing the image and video sources. This is an open pathway to well-informed decisions and the right diagnostics.
Computer vision tools have already become common support
Nowadays it is difficult to imagine a decision-making process in healthcare without computer vision tools. They are widely used to help doctors be more confident in their diagnostic and treatment decisions. Image analysis and computer vision make a particular organ visualized, hence a more accurate diagnosis is done.
Computer and human visions are two different things, the first one focuses on image and video recognition and understanding based on the data it was programmed and trained on. The human vision is all about their brain’s contextual info. In the case of perfect computer training, there is nothing more perfect in tiny details catching than medical imaging tools. They have become even more powerful recently due to segmentation and object detection features. Physicians are offered second opinions and useful flagging related to areas in images.
The areas where medical image data is managed with computer vision
In this section, we would like to show some of the computer vision cool applications in Healthcare. Of course, there are so many more apart from the listed below.
Computer vision in medical imaging provides great decision support in urgent situations. Leading companies such as IBM,GE Healthcare, Samsung and others cherish tools with patient-specific data, 3D imaging, deep vision, cognitive analytics, etc. Real-time data is a priority for the right treatment in time as well as a precaution from serious chronic issues. According to Johns Hopkins researchers, every 9 seconds someone in the USA gets a brain injury, and medical errors are the third cause of death in the country with an annual number of 250 thousand deaths. It is shocking that wrong diagnosis can lead to 40–80 thousand deaths, which were possible to prevent. The figure below demonstrates some more impressive numbers and facts.
- Deep-learning technology is the foundation of the systems, created for abdomen, spine head and chest pathologies detection in medical images, AIDoc, for example.To be more precise, the system can notice free air in the chest, bone hypodensity, free fluid in the abdomen, etc.
- 15 seconds of Magnetic Resonance Imaging (MRI) algorithm – and the image is thoroughly studied. If we compare to the human specialist processing time, it will take at least half an hour. MRI is great when spotting problems in the circulatory system, joints and other soft tissues. Blood flow visualization and calculation are cool things for cardiology.When decoded, cardiac MRI images, are engaged in a lot of important diagnostic missions like myocardium perfusion study, ventricular contours uncovering, etc.
- X-ray works great in showing inner damages or organ anomalies. With the help of computer vision, all possible issues can be seen in one session. Automated scans classification hugely helps here too. The whole procedure is way healthier as radiation exposure is much reduced. InnerEye tool, for example. A radiologist uploads a 3D scan into the device so that the software sees tumors if any. Suspicious spots are marked with colors for further more detailed inspections.
- CT scans’ use is normally narrowed down to tumors detection and internal brain bleedings, one of the most dangerous health conditions. Image analysis is present in a lot of human body systems care processes. A crucial coronary calcium scores automatic measurements are easily done via common chest CTs, for example.
- Thanks to computer vision, the healthcare industry also benefits greatly from prime-quality organ scanning – ultrasound. This method is the least intrusive, and it is widely used on pregnancy monitoring. With the ultrasound technique, it is also easy to spot any abnormalities in organs as well as to check on their proper functioning. It is a gift in countries with less developed economies, where highly skillful doctors are seldom met. Specially designed algorithms read echocardiograms with a precise focus on the technology’s application. When interpreting the ultrasound video, it is possible to notice traces of cardiovascular issues like cardiomyopathy or congenital heart disease, etc. This way even a young medical professional is able to set the relevant diagnosis and treatment.
Hospitals and clinics are the primary close-by opportunity
Let’s ponder a bit on the financial part of the computer vision tools. How about the user devices adoption outside of traditional healthcare institutions?
No doubt that neurology, cardiology, infectious diseases, oncology, and ophthalmology settings will witness medical images analysis solutions beneficial outside of the common physician’s offices and hospitals, and into homes and extra-care housing. Special cases are oncology and ophthalmology as well as some infectious diseases, which is natural and understandable.
Shifting diagnostics from centralized clinics towards doctors’ offices and opening new revenue streams for these small settings, will lead to a great impact on profit margins. It is predicted that the appeal from primary care doctors will rise extremely over the years.
While a lot of technologies are being created for consumer markets, most of the users that are ready to use computer vision solutions and manage large volumes of devices and consumables in the next five years are medical professionals in hospitals and clinics.
The road to income and effectiveness is pretty clear referring to both pay structure and market size. That is why companies developing medical image analysis technology have to focus on giving most of their resources to delivering solutions for the traditional healthcare markets.
Conclusion
Medical imaging analysis is in great demand today and with time the need for this kind of solution will only grow further. However, a lot of healthcare services are made with traditional tools. Computer vision innovations will reveal new opportunities.
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