Facial recognition can help make our lives better and keep us healthier. We can use biometric tools to prevent heart attacks, diagnose illness and determine treatment options.
For example, doctors have found a way to use facial recognition software to predict heart disease by examining the blood vessels in the eyes. In hospitals, both nurses and doctors can use it to identify a patient and avoid mistreatments. Medical innovations along with machine learning promise to open up new opportunities to improve patient care.
Google sees value in biometric innovations
When Google released its latest version of Chrome last year, it shared its plans to move away from passwords to using fingerprints, facial recognition and other biometric information as a way for sites to log consumers in. This would be a way to avoid having to remember passwords from every device used to sign into to a site.
Google and Verily Life Sciences have felt facial recognition was an area worthy of investing time and resources. Recently that paid off for them with the discovery of a new way to assess a person’s risk of heart disease by analyzing scans of the back of a patient’s eye. Using machine learning, the company’s software can tell a person’s age, blood pressure, and whether or not they smoke. This can then be used to predict their risk of suffering a heart attack with about the same accuracy as current leading methods. Although more testing is required, the algorithm makes it quicker and easier for doctors to analyze a patient’s cardiovascular risk without a blood test. This means someone like you or I could simply have our eyes’ scanned without having to wait for tests to return.
Google and Verily’s scientists used machine learning to analyze a medical dataset of nearly 300,000 patients. This information included eye scans as well as general medical data. With all deep learning analysis, neural networks are used to mine this information for patterns. This helped the researchers to associate telltale signs in the eye scans with the metrics needed to predict cardiovascular risk like age and blood pressure. By studying the blood vessels in the rear of the eye, doctors can infer things like a person’s blood pressure, age, and whether or not they smoke, all important predictors of cardiovascular health.
Australian computer model uses a face to determine health
Luke Oakden-Rayner, a medical researcher at the University of Adelaide who specializes in machine learning analysis, has also demonstrated was that Artificial Intelligence (AI) helps improve existing diagnostic tools. He showed that this analysis can take data that’s been captured for one clinical reason and extend it to learn more and to assist doctors in treating their patients.
In another study, a computer model has accurately guessed health factors working in a similar way to the brain by looking at the face. Dr. Ian Stephen, of Macquarie University in Sydney, Australia, used facial shape analysis to correctly detect markers of physiological health in more than 270 individuals of different ethnicities. He was able to determine a person's health simply by analyzing their face, showing that the face contains clues about a person's physiological health.
By extending facial recognition, machine learning and AI we provide doctors with ways to improve and extend our lives. For nurses it offers a much more secure identification system to ensure every patient gets the correct medicine. Overall, biometric tools offer powerful tools to assist doctors and nurses in diagnostics and patient care.