Researchers from Amsterdam UNC and Radboundumc have revealed a method to predict the effectiveness of antidepressants within a week of treatment
This advancement powered by Artificial Intelligence (AI) offers a ray of hope for millions battling major depression disorder (MDD) worldwide.
Published in the prestigious American Journal of Psychiatry, the study showcases how a creative system, coupled with brain scans and clinical data, can accurately forecast the response to antidepressant medication, specifically sertraline, one of the most commonly prescribed drugs globally.
Treating major depression disorder
Professor Liesbeth Reneman, an esteemed figure in neuroradiology at Amsterdam UMC, highlights the significance of this breakthrough for patients.
Usually, it takes between 6 to 8 weeks to determine whether an antidepressant will relieve symptoms. However, with this new approach, clinicians can determine effectiveness much faster, potentially removing crucial weeks from the treatment timeline.
Reneman explains, “With this method, we can already prevent two-thirds of erroneous prescriptions of sertraline, offering better quality of care and sparing patients from potential side effects.”
Analysing blood flow patterns
The success of this process lies in its ability to analyse blood flow patterns in the anterior cingulate cortex, an area of the brain crucial for emotion regulation.
After one week of treatment, the severity of symptoms acted as a significant predictor of therapeutic response, as underlined by psychiatrist Eric Ruhé from Radboudumc.
Tailoring antidepressant treatment
This breakthrough holds promise for tailoring antidepressant treatment to individual patients, revolutionising the current trial-and-error approach. No precise prediction tool exists, leaving patients uncertain and potentially causing side effects.
By speeding up the identification of effective antidepressants, this approach not only helps patient suffering but also reduces societal costs associated with prolonged disability and decreased productivity.
Researchers are committed to refining the process further by incorporating additional data, aiming to enhance its predictive accuracy and applicability.
