Different industries, including medical care, widely use Artificial intelligence (AI) and machine learning (ML) tools. One of the essential characteristics of AI-powered technologies is the possibility to identify, understand, and maintain visual data. This technology is called Computer Vision (CV).
The essential goal of Computer Vision in healthcare is to enable computers and systems to pull out valuable information from digital assets like photos, videos, and other data — and to act on or make recommendations based on that data.
Many customized Computer Vision medical applications already exist, beginning with image recognition and predictive analytics to individual electronic health records. CV in healthcare enables better quality of delivered medical services and the more efficient administration of medical facilities.
A global standard for different medical images is Digital Imaging and Communications in Medicine. DICOM specifies a communication protocol and a file format (one to several images, a header with patient demographics, and technical image details) to guarantee that different companies can share data to achieve interoperability across the healthcare industry.
The most common CV apps for medical purposes work with image recognition and classification, analyzing medical images such as MRI, CT, and X-ray scans. With the help of a Computer Vision, physicians can examine and analyze the images to improve the precision of diagnosis and prescription of appropriate therapy. Medical image classification based on a convolutional neural network (CNN) can help identify such diseases as tumors or aneurysms in the brain or even help to diagnose Alzheimer’s disease at the early stages.
Related: ML in medical imaging: feature extraction in diagnostics
A facial image or video recognition in the developed AI-face scanning applications can effectively classify distinctive features in photos of patients with congenital and neurodevelopmental disorders. AI-based tools already assist therapists with detecting different kinds of abnormalities.
Computer vision applications are beneficial in accurately detecting brain tumors and other types of cancer. If brain tumors are left untreated, they spread quickly to other parts of the brain and spinal cord. Early detection is highly crucial to saving the patient’s life. Medical professionals can use CV applications to make the detection process more tedious and fast.
Skin cancer can be challenging to detect at early stages since the symptoms often resemble common skin ailments. As a remedy, scientists effectively use computer vision applications to differentiate between cancerous skin lesions and non-cancerous lesions.
Computer vision apps often help to detect breast cancer. Algorithms trained to compare healthy and cancerous tissues work with a vast database of images. It helps to automate the process of detection and reduce human error cases.
Computer Vision systems for healthcare may be used for diagnosing other types of cancer, including bone and lung cancer, soon. CV helps doctors identify minor changes to detect malignancy.
Computer vision in healthcare focuses on image and video interpretation involving object detection, image classification, and segmentation. CV has been used in medical applications for dermatology, radiology, or pathology to improve the patient’s treatment quality. Medical imaging creates a visualization of particular organs and tissues to enable a more accurate diagnosis. Computer vision makes it easier for doctors and surgeons to detect abnormalities. MRI, X-ray radiography, ultrasound, endoscopy, etc., are a few disciplines within medical imaging.
Early detection of coronavirus and other lung diseases signs from chest X-rays is vital for proper treatment and prevention. The most advanced AI algorithms for lung medical images analysis achieved almost 97% accuracy. Computer vision applications can aid in diagnosing, controlling, treating, and preventing Covid-19. Computer vision is widely used to enforce and monitor strategies to prevent pandemics by performing masked face detection.
Computer vision is also useful for medical skill training for surgeons on simulation-based surgical platforms. CV-based apps are a more effective medium for training and assessing surgical skills. With surgical simulation, trainees can work on their surgical skills, gain detailed feedback, and evaluate their performance. It allows them to better understand patient care and safety before performing surgeries.
Computer vision tools can do even more. For example, CV-based apps can measure the amount of blood lost during surgeries to determine whether the patient has reached a critical stage. It helps doctors determine the amount of blood needed by the patient during or after the surgery.
Computer vision applications help monitor patients’ health and fitness, allowing doctors and surgeons to prescribe better treatment, even during emergencies. CV also helps measure the level of activity, detect hectic movement, and count time spent by people in specific areas.
Using Computer Vision in healthcare helps to make more accurate diagnoses by recognizing patterns to spot diseases without error. CV-based tools can aid the identification, prevention, and treatment of several diseases by scanning medical images.
A lot of patients prefer to rehabilitate at home after an illness instead of staying at a hospital. Medical professionals can use CV to track patients’ progress virtually and offer them the necessary physical therapy. Training at home is not only more convenient, but economical too.
In addition, computer vision technologies can also aid in remote monitoring of patients or the elderly in a non-intrusive manner. CV-based fall detection systems aim to reduce dependency and care costs in the elderly community.
New software, based on Computer Vision, is changing medical care and can have a disruptive effect on the industry. Existed Computer Vision solutions are efficient, which will inevitably lead to more research and development.
Healthcare facilities have always looked for better patient treatment. Computer Vision technology is going to accelerate the evolution. In other words, Computer Vision technology in healthcare is just getting started.
Computers can’t replace medical staff, but they can help avoid mistakes and improve the quality of care in the industry, where accuracy and quick decisions are crucial. There are several benefits from CV-powered applications for any area of medicine that generates visual data — from gastroenterology to neurology or dermatology:
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