Description: A computer-aided detection software tool which aimed to accurately detect and classify patients with Major Depressive Disorder (MDD) and Alcohol Use Disorder (AUD). This was done by integrating EEG and machine learning , creating a revolutionary approach in diagnosing mental health to facilitate early and effective intervention.
According to WHO, approximately 264 million of people in this world is suffering from some form of mental disorders. One main thing that most neurological disorders share in common is the conventional or existing method of diagnosing them which is using the Diagnostic and Statistical Manual of Mental Disorders (DSM 5) method. This method classifies mental disorders based on symptom clusters from clinical observations and patient reports. However, it is often criticized for its categorical approach that may not capture the complex and overlapping nature of mental health conditions. Therefore, our innovation aims to solve these issues by utilizing EEG recordings and machine learning to objectively identify Major Depressive Disorder (MDD) and Alcohol Use Disorder (AUD). By analyzing brain activity through effective connectivity matrices, our AI-powered model classifies individuals into MDD, AUD, or healthy control groups with an excellent accuracy of 87.7%. This technology promises a new era of precision in diagnosing these common yet challenging disorders, moving away from subjective methods prone to errors.
Organisation: Universiti Teknologi PETRONAS
Innovator(s): Dr. Norashikin binti Yahya, Muhammad Nur Aiman bin Haja Maidin
Category: Medicine, Biotechnology and Medical Devices
Country: Malaysia