Artificial intelligence algorithms of major depressive disorder

Major Depressive Disorder (MDD) affects a substantial number of individuals worldwide. New approaches are required to improve the diagnosis of MDD, which relies heavily on subjective reports of depression-related symptoms.

The clinical need is addressed by a research team led by Associate Professor Chen Nanguang, the Senior Principal Investigator from NUSRI-Suzhou Biomedical and Health Technology Research Platform, in collaboration with Cyrus Ho Su Hui, from the National University Hospital and Roger Ho, from the Institute for Health Innovation and Technology (iHealthtech). They have demonstrated the performance of integrating the functional near-infrared spectroscopy (fNIRS) system with artificial intelligence algorithms for classifying and staging MDD in clinical settings using a large dataset. This approach can potentially enhance MDD assessment and provide insights for clinical diagnosis and intervention.

By utilising this approach, researchers have accomplished good diagnostic accuracy in MDD identification and staging, and improved understanding of the cortical regions potentially associated with MDD. Future research will focus on achieving more quantitative hemoglobin concentration measurement and depth selectivity by implementing the time domain technique. However, the current study has already demonstrated the potential of combining the fNIRS technique with deep learning to provide a complementary and objective diagnostic instrument for clinicians in psychiatry.