A Little Rock high school student with expertise in machine learning has joined a Parkinson's research team at UAMS.
Anu Iyer, a junior at Central High School, is part of a team testing ways to monitor rural patients remotely, improve clinical outcomes and help patients participate in cutting-edge research, the health system reported. Iyer is training a computer model to detect features in a person's voice that could indicate Parkinson's, a progressive nervous system disorder.
That process involves creating computer systems that can learn and adapt by using algorithms and statistical models, according to UAMS. Those models are used to analyze data and provide helpful information based on patterns in the data.
Iyer previously developed a machine learning tool for diagnosing multiple cancers by analyzing MRIs and pathology images. She was an intern at UAMS at the time. Her work, which increased accuracy of diagnoses from less than 90% to nearly 96%, was published in November 2021 in the national Journal of Student Research.
Earlier in high school, Iyer collaborated with UAMS doctors on a project that involved the creation of a machine learning tool to detect eye conditions, including diabetic macular edema and macular degeneration.
Before that, she was part of a team from Forest Heights STEM Academy that theorized a new smartphone app called SmartRnger and won not only a Best in the State accolade but also $5,000 for their school. The app the students envisioned automatically adjusts the volume of a telephone ringtone based on ambient noise and location preferences.
Fred Prior, UAMS professor and chair of the College of Medicine Department of Biomedical Informatics, said Iyer is "one of the best programmers I’ve met in a while."
"She’s very quick, very accurate, and her code is beautiful," Prior said. "I’m working with her like I would a graduate student, and that’s pretty amazing for someone in high school."
The research team meets on Saturday mornings to accommodate Iyer's school schedule.
In the future, she plans to work in an medical field focusing on computer science and biometrics.