Artificial intelligence can diagnose genetic diseases with only 5 minutes of recording
Guide
If you give a 5min recording, how much information can you get from this recording? The artificial intelligence (AI) developed by the University of Wisconsin brings new medical breakthroughs in the diagnosis of fragile X syndrome, and the use of genes alone. Compared to the test, a machine learning screening method was used to diagnose 1,000 patients with pre-fragile X chromosomes in the population and save more than $11 million.
According to a recent study by the University of Wisconsin–Madison's Waisman Center and the Wisconsin Institute for Discovery, a 5 minute recording is enough to determine whether a person is susceptible to genetic disease and Related complications.
Machine learning to identify fragile X chromosome syndrome
Recently, this study was published on Scientific Reports under the title "Automated screening for Fragile X premutation carriers based on linguistic and cognitive computational phenotypes". Researchers use machine learning to analyze hundreds of voice records and accurately identify individuals' pre-mutation vulnerable X chromosomes. Chromosomes with this characteristic increase the risk of neurodegenerative diseases, infertility and other conditions. In addition, the offspring of the population carrying the chromosome are prone to fragile X syndrome.
Fragile X syndrome is caused by a mutation in the DNA during the formation of the X chromosome in the human body. It is mainly characterized by mental retardation and physical disability. Currently, millions of people around the world have a pre-mutation fragile X chromosome.
Machine learning - artificial intelligence calculation program to make diagnosis easier
As a participant in the study, Professor Marsha Mailick, Associate Dean of the University of Wisconsin Graduate School, said: "These pre-mutation conditions are still not effectively diagnosed, and people often don't know their risk increases."
Diagnosing the pre-mutation fragile X chromosome is a difficult task that is extremely time consuming and requires a lot of resources to make it expensive. “Our research team wants to develop a fast, economical, and effective screening method,†Mailick said. It is with this appeal that they have developed a machine learning-artificial intelligence calculation program. This new type of robot can be "trained" with existing data and then analyzed for new information.
Kris Saha, an associate professor of biomedical engineering at the University of Wisconsin, said: "In the first place, we spent hours analyzing and annotating each record. With such a lot of work, it took less than a second to use them."
Past applications of machine learning
In a previous study, Mailick and colleagues have shown that systematic voice recording analysis can yield valuable information about families with pre-mutated fragile X chromosomes. In 2012, a study led by Professor Jan Greenberg, vice president of the University of Wisconsin, analyzed the voice records of 5 minutes of mothers talking about their children with X chromosome fragility syndrome. Studies have shown that a warm, positive family atmosphere created by parents can reduce a child's behavioral problems.
Audra Sterling, an associate professor of communication science and disease at the University of Wisconsin, used the same recordings to study the results. The results showed a strong correlation between age and speech disorder in the middle-aged and elderly women with pre-mutation fragile X chromosome. These findings suggest that recordings can track the progression of disease in older adults with a pre-mutation fragile X chromosome.
Mailick said: "In the past, speech feature coding was time consuming and required clinical expertise, but the methods used in the new study did not require these features." Saha, Greenberg, Sterling, Mailick, and graduate student Arezoo Movaghar designed the initial machine. Learning algorithms that intelligently differentiate patients into two groups: patients with mothers with vulnerable X chromosomes and mothers who are not.
The researchers first analyzed the recordings of 100 children with X-chromosome vulnerability syndrome talked about by a mother who had a weak X chromosome, and then analyzed the recordings of another 100 mothers of children with autism spectrum disorders.
Create a language cognitive function module
Based on recording and machine learning algorithms, researchers create lists of language and cognitive functions, such as the average length of sentences in a record or the number of padding pauses, such as the pronunciation method of "ah" or "oh", which can be very effective. The difference between the two groups is different. Based on these remarkable features, machine learning algorithms can achieve 81% discrimination accuracy.
According to the researchers' estimates, using machine learning screening methods to diagnose 1,000 patients with pre-fragile X chromosomes in the population can save more than $11 million compared to genetic testing alone. Mailick said: "This work is the first step towards a faster, more cost-effective screening process. We plan to expand screening for other populations, such as men with vulnerable X chromosomes."
More than fragile X chromosome diagnosis
Saha said: "The machine learning algorithms developed in this study are not limited to fragile X chromosome diagnosis. The diagnosis of other diseases in the future may be implemented by this algorithm." Movaghar said: "We want to simplify the way data is collected." Movaghar is working on Develop mobile apps to accomplish this. The app asks a series of simple personal and medical questions and then records a 5 minute voice sample that can even be from an audio recording in a smartphone or home smart speaker.
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