Release date: 2017-06-19
Give you a 5 minute recording. How much information can you get from this recording? According to a recent study by the University of Wisconsin-Madison Westman Center and the Wisconsin Discovery Institute, a five-minute conversation is enough to determine whether a person is susceptible to genetic disease and related complications.
As early as this study in Scientific Reports, researchers used machine learning to analyze hundreds of voice records and accurately identify the vulnerable X chromosomes in the pre-mutation phase of an individual. Increase the risk of neurodegenerative diseases, infertility and other diseases. In addition, the offspring of the population carrying the chromosome are prone to X chromosome vulnerability syndrome.
The main features of X-chromosomal vulnerability syndrome are intellectual and physical disabilities, and millions of people around the world currently have X-chromosome mutations. 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 are generally unaware that their risk of illness has increased."
Diagnosing a pre-mutation 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 was with this appeal that they developed a machine learning-artificial intelligence calculation program. This new type of robot can "train" with existing data and then analyze 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."
In a previous study, Mailick and colleagues have shown that systematic speech recording analysis can yield valuable information about families with pre-fragile X chromosomes. In 2012, a study led by Vice President of the University of Wisconsin, Jan Greenberg, analyzed five-minute mothers' conversations about their children's voice records with X-chromosomal vulnerability 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. There was a strong correlation between age and speech disorder in the middle-aged and older women with a pre-mutated X chromosome. These findings suggest that recordings can track the progression of disease in older adults with a pre-mutated vulnerable 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-susceptible syndrome who were talking about a 5-minute mother with a fragile X chromosome, and then analyzed the recordings of another 100 mothers of children with autism spectrum disorders.
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 notable 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 the screening of other populations, such as men with vulnerable X chromosomes."
Saha said: "The machine learning algorithms developed in this study are not limited to the diagnosis of vulnerable X chromosomes, and 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, then records a five-minute voice sample that can even be from an audio recording in a smartphone or home smart speaker.
Source: ScienceLondon Technology (Micro Signal science_london)
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