What can be a better time to test how artificial intelligence (AI) brings healthcare innovations for a better lifestyle than this? When everyone is crying out the fear of coronavirus and increasing mortality rate induced by the pandemic, AI is serving governments, officials, doctors, nurses, and patients as well to fight against the virus. Amid all the chaos, you would be relieved to know that AI has proved its innovative healthcare potential yet another time. Researchers have revealed that using AI their trained computer model predicted the future incidence of diabetes with an overall accuracy of 94.9 percent.

The preliminary research, which is yet to be published, applies a machine learning method to assess the risk of a person developing a lifelong condition that causes high blood sugar levels.

According to the charity Diabetes UK, around 3.9 million people in the UK are living with diabetes. The condition is linked to increased risks of other severe health problems, including heart disease and cancer. Experts have said that preventing diabetes, to begin with, is essential to reduce the risk.

"Currently, we do not have sufficient methods for predicting which generally healthy individuals will develop diabetes," said Dr. Akihiro Nomura, of the Kanazawa University Graduate School of Medical Sciences in Japan.
As part of the study, the researchers at the Japanese graduate school investigated whether machine learning could be used to diagnose diabetes.

Nomura and colleagues analyzed 509,153 nationwide annual health checkup records from 139,225 participants from 2008 to 2018 in the city of Kanazawa. Among them, 65,505 participants without diabetes were included.

The data included physical exams, blood and urine tests, and participant questionnaires. Patients without diabetes at the beginning of the study who underwent more than two annual health checkups during this period were included. New cases of diabetes were recorded during patients' checkups.

The researchers identified a total of 4,696 new diabetes patients (7.2 percent) in the study period. Their trained computer model predicted the future incidence of diabetes with an overall accuracy of 94.9 percent.

Nomura says he next plans to perform clinical trials to assess the effectiveness of using statins to treat groups of patients identified by the machine learning model as being at high risk of developing diabetes.

Moreover, for patients with diabetic kidney disease, using an artificial intelligence-enabled diagnostic test to predict rapid kidney function decline and kidney failure could result in substantial savings to the U.S. health care system, a speaker told the audience at the virtual National Kidney Foundation Spring Clinical Meetings.

According to Thomas Goss, PharmD, of Boston Healthcare Associates, the current method for risk stratification in diabetic kidney disease fails to identify about half of the patients who ultimately have rapid kidney function decline. Furthermore, up to two-thirds of these patients initiate dialysis acutely due to a crash which, he argued, is a very costly way to initiate dialysis.

"We know that many patients who are at high risk [for rapid kidney function decline and failure] often don't get referred from primary care physicians to nephrologists early in the stages of the disease when the most opportunity for intervention is available," he said. "We feel there's an important need for a predictive tool that will help us to identify patients at high risk and improve our intervention strategy."

Goss added that US$42 billion is currently spent by Medicare on this population annually (patients with type 2 diabetic kidney disease stages 1 to 3b).