By Daryna Pesina, COO @It-Jim
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Computer Vision in Healthcare Applications

Artificial intelligence (AI) and machine learning (ML) are being progressively used across different sectors including healthcare. One of the AI-powered tools is computer vision (CV), the ability to recognize, interpret, and process visual data. Thus, potential applications of computer vision in the medical field are multifold, from image processing and predictive analysis to automated health records. All this enables improving the quality of delivered medical services and the healthcare administration system. Let us consider in general CV use cases in healthcare for today.

Computer vision in medical imaging

Currently, the most widespread use of computer vision is image recognition and classification for medical purposes. It refers primarily to an analysis of medical images such as CT, MRI, and X-ray scans. Aided with CV and deep learning tools, physicians can inspect and interpret the images in-depth improving the accuracy of diagnosis and adjusting appropriate therapy. Thus, medical image classification with a convolutional neural network (CNN) is applied for identifying possible diseases. Dealing with MRIs, for example, various CNN architectures may reveal tumors or aneurysms in the brain, and even predict the development of Alzheimer’s disease in the early stages.

Another scenario of applying CV in medical imaging is a facial image or video recognition for specific case finding. One of the benefits of using CV algorithms is that they can be successfully trained to reveal even the slightest abnormalities. This could be extremely helpful for patients suspected to develop conditions and rare genetic malfunctions that are difficult to detect in routine screenings. There are several computer vision healthcare companies developed AI-face scanning applications. Based on ML algorithms and neural networks, they classify distinctive features in photos of patients with congenital and neurodevelopmental disorders.

Simulation and risk identification

CV has also a great application in surgery and therapy of certain diseases. It enables computer-supported surgical training and simulation, surface reconstruction, and implant design in orthopedic procedures. In this field, computer vision is proved to be a powerful instrument in reaching prompt and precise segmentation of bones and joints. That helps to achieve higher levels of precision in modeling skeleton and implants and handle the artifacts of MRI and CT scans with robust deep learning solutions.

Another example of a successful CV application is real-time tracking of vital signs and fitness characteristics to prevent acute neurological and cardiac events like a stroke and heart attack.

Enhancing administrative processes

Finally, by using a CV in the healthcare system, lots of manual administrative processes can be easily automated. Among them are patients’ health records that should be reviewed and updated by doctors, logging protocols, searching and analyzing plenty of information and research data when planning study and clinical trials.

To summarize, the computer vision technology for healthcare systems may assist in:

  • providing more accurate diagnosis and health monitoring
  • developing personalized medicine
  • detecting illnesses and conditions that are difficult to identify
  • creating infrastructure for future research and clinical trials
  • improving decision-making and assist in prescribing appropriate treatment
  • optimizing medical administrative tasks (generating automated protocols and reports, noticing some factors that are difficult to detect via the naked eye)
Computer Vision in Healthcare