Psychiatric diagnosis has long relied on clinical interviews and patient history, often lacking objective and quantifiable evaluation standards. To tackle this challenge, National Yang Ming Chiao Tung University (NYCU) and Taipei Veterans General Hospital (TVGH) have made groundbreaking advancements in brain imaging and artificial intelligence. Since 2019, Professor Chih-Chieh Yang—Chair of the NYCU School of Medicine and Director of the Digital Medicine and Smart Healthcare Center—has led the development of a cutting-edge brain imaging analysis technology capable of accurately localizing brain degeneration across different ages and stages of psychiatric illness. By harnessing AI to detect abnormalities invisible to the human eye, this technology significantly enhances the objectivity and precision of psychiatric diagnoses. Now successfully implemented in clinical services at TVGH, this innovation has not only transformed diagnostic practices but also earned international recognition with the prestigious 2025 Edison Awards in the United States—underscoring its global impact on psychiatric research and clinical care.
At the core of this technology is a precise quantitative method for assessing brain degeneration across various regions. Based on long-term observations of brain aging and disease progression, the research team has established a degeneration trajectory model covering 138 gray and white matter regions. This model predicts the deterioration trends of specific brain regions based on the patient’s age and disease stage, enabling targeted diagnosis and more precise treatments.
Professor Yang explains that the brain undergoes continuous degeneration in psychiatric disorders, but the patterns vary across different regions. Previous AI-based brain imaging technologies had struggled to establish causal relationships and track disease progression. However, the new technology overcomes these limitations by accurately predicting brain degeneration based on a patient’s age and disease stage.
The technology has already been applied to research and clinical evaluations of schizophrenia, bipolar disorder, and major depressive disorder. Findings indicate that patients with schizophrenia experience significant brain volume shrinkage over 22 years post-onset, with abnormalities in cortical thickness observed in the early stages, particularly affecting the frontal, temporal, and insular lobes. The patients with bipolar disorder and major depression show distinct abnormalities in the ventrolateral prefrontal cortex and anterior cingulate cortex, respectively. These discoveries offer important insights for enhancing treatments such as transcranial magnetic stimulation and deep brain stimulation, allowing for more precise targeting of affected brain regions.
This groundbreaking technology not only overcomes the limitations of existing deep learning systems in brain imaging analysis but also provides psychiatry with a scientific and quantifiable diagnostic tool. In the future, it is expected to be expanded for the early diagnosis and assessment of neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease.
