Embedded vision systems in healthcare, clinical settings

Embedded vision technology has the potential to help healthcare professionals identify disease more quickly and provide them with more accurate information about their patients.

11/23/2017


Image courtesy: Bob Vavra, CFE MediaEmbedded vision systems are a relatively new technology where a processing board and board-level camera are combined to create "smart" vision, or embedded vision. The technology is driven mainly by the miniaturization of PCs and improving processing power. Embedded vision technology has the potential to change the practice of medicine, giving physicians and other healthcare professionals diagnostic tools they've never had before.

Common embedded vision healthcare applications

While the medical market for embedded vision is still one of the least developed for embedded vision systems, the potential for widespread adoption exists nonetheless.

The most common imaging devices include CT, MRI, mammography and X-ray machines. Computer-aided diagnosis (CAD) is being implemented in its earliest stages to assist in the analysis of medical images by spotting trends and potential problem areas.

Embedded vision systems will one day be used over the course of a patient's lifetime to help the doctor identify advancing diseases more accurately and in less time than currently required.

Embedded vision in dermatology

The use of embedded vision systems in dermatology provides a simple, yet illuminating example of how this technology can change the practice of healthcare. Traditionally, dermatologists would examine a patient with a dermatoscope that would optically magnify a suspicious region of skin.

With the use of embedded vision, dermatologists can now take high-quality digital images of a patient's skin and store the information. Images taken over time can be compared with one another to track changes in skin pigmentation and size with a high degree of accuracy, and the results can be shared with other doctors in moments.

The deployment of machine learning algorithms can also enable embedded vision systems to learn to detect the difference between areas that may or may not have a tumor. This allow a doctor to spend more time examining a potentially threatening area.

Embedded vision systems have enormous potential in the medical industry. Even if they're not yet as highly developed as other industries, they'll still prove to be disruptive.

This article originally appeared on the AIA website. The AIA is a part of the Association for Advancing Automation (A3). A3 is a CFE Media content partner. Edited by Chris Vavra, production editor, Control Engineeringcvavra@cfemedia.com.

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