| Abstrakt: |
A recent study conducted by researchers at the New York Institute of Technology explores the use of deep learning algorithms to diagnose and distinguish between major depressive disorder, bipolar disorder, and schizophrenia. The study utilized resting electroencephalograph (EEG) data and achieved classification accuracies between 90% and 100% for different diagnostic categories. The researchers suggest that diagnostic biomarkers derived from EEG data could significantly improve clinical care by reducing reliance on reported symptomatology. However, it is important to note that the study used a relatively small sample size and an outdated diagnostic system, which may affect the generalizability of the findings. [Extracted from the article] |
