NCSA and UICOMP researchers have developed a revolutionary machine-learning diagnostic tool to screen for anxiety and major depressive disorders, promising more accessible and accurate mental health assessments.
Scientists at the National Center for Supercomputing Applications (NCSA), a cornerstone in supercomputing and digital resource innovation for scientific research at the University of Illinois Urbana-Champaign, and the University of Illinois College of Medicine Peoria (UICOMP) have developed an advanced, automated screening method for anxiety and major depressive disorders, offering hope to millions who suffer undiagnosed.
Their groundbreaking research, published in the Journal of Acoustical Society of America Express Letters, leverages machine learning to analyze acoustic and phonemic data from brief verbal fluency tests, significantly improving diagnostic accuracy and accessibility.
New Diagnostic Breakthrough
In an era where mental health issues are increasingly prevalent and pervasive, this innovative tool offers a beacon of hope.
Anxiety impacts 19.1% of U.S. adults, while major depression, which affects 8.3%, is a leading cause of disability among individuals under 40. Alarmingly, many remain undiagnosed due to barriers like stigma, cost and lack of access to healthcare.
A Leap Forward in Mental Health Screening
Mary Pietrowicz, lead research scientist at NCSA, highlighted the significance of their findings.
“This research demonstrates that analysis of short samples of acoustic voice, specifically one-minute verbal fluency tests, can be used in screening for anxiety and depression disorders, and can function online, at any time, addressing many of the barriers to screening and treatment,” she said in a news release.
The study involved a custom dataset including individuals both with and without comorbid depression and anxiety, meticulously curated to ensure the accuracy and reliability of the results. Remarkably, the acoustic models used in the study discerned the presence of these disorders with high success rates, representing a substantial advancement in mental health diagnostics.
Transformative Potential for Health Care
Sarah Donohue, director of research services at UICOMP, underscored the collaborative effort behind the study.
“The data for this study were collected by multiple medical students at the University of Illinois College of Medicine Peoria,” she said in the news release, stressing the meticulous process of data collection and analysis.
The primary advantage of these acoustic tests lies in their accessibility, potentially administered online, via apps, or in clinical settings. This adaptability directly mitigates barriers to screening, such as stigma, low self-perception of need and logistical challenges, including cost and transportation.
Ryan Finkenbine, chair and professor of clinical psychiatry at UICOMP, emphasized the broader implications of this technological progress.
“The development of an efficient, accurate and easy-to-use method for screening patients who may be suffering from depression or anxiety offers tremendous promise,” he said. “The application of advanced machine learning models to the clinical setting provides a remarkable path for clinicians to screen for signs of mental illness in an adaptive and practical way. Patients and clinicians alike will benefit from improved methods for comprehensive medical and mental health care.”