Mental health is of increasing concern across society, and so it stands to reason that we want any treatments we give for mental health issues to be as successful as possible. There has been a fascinating growth in AI-based applications to help professionals gauge whether a treatment will be successful or have damaging side effects.
For instance, I recently wrote about one such project that was using AI to understand whether anti-depressent medications would help or hinder the patient.
A second study has recently been published, by Lawson Health Research Institute, The Mind Research Network and Brainnetome Center, in which the team develop an AI system that analyzes brain scans in order to better classify the precise illness in patients with complex mood disorders.
The right treatment
The study recruited a number of patients with either major depressive disorder (MDD), bipolar disorder, or no mental health issues from the London Health Sciences Centre (LHSC). Each participant had their brain scanned using an fMRI machine.
When the scans were analyzed, clear differences emerged in specific parts of the brain, including the default mode network and the thalamus. This data was then used to train an AI system to identify signs of either MDD or bipolar, and when this system was tested on people with a known diagnosis, it achieved an accuracy rate of 92.4%.
The system was then tested on a dozen patients whose diagnosis was much less clear. The algorithm studied the brain of each participant to predict the diagnosis, and indeed whether they would respond to certain treatments.
“Antidepressants are the gold standard pharmaceutical therapy for MDD while mood stabilizers are the gold standard for bipolar I,” the researchers say. “But it becomes difficult to predict which medication will work in patients with complex mood disorders when a diagnosis is not clear. Will they respond better to an antidepressant or to a mood stabilizer?”
Interestingly, 11 out of the 12 patients responded well to the medication predicted by the algorithm, suggesting that the work is a major step towards being able to use technology to help people with complex mood disorders.
“It also suggests that we may one day have an objective measure of psychiatric illness through brain imaging that would make diagnosis faster, more effective and more consistent across health care providers,” the team say.
It offers the prospect of an improved diagnosis process than the current method, which often struggles to understand complex mood disorders, especially during the early stages when symptoms are less well-defined.
“Patients may also have more than one diagnosis, such as a combination of a mood disorder and a substance abuse disorder, further complicating diagnosis. Having a biological test or procedure to identify what class of medication a patient will respond to would significantly advance the field of psychiatry,” the team conclude.
