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Using AI to Better Diagnose Disorders And Target Drug Treatment

Using AI to Better Diagnose Disorders And Target Drug Treatment

New research suggests machine learning can improve the diagnosis of complex mental health disorders and aid the selection of pharmacological therapy.

Experts welcome the new finding as mood disorders like major depressive disorder (MDD) and bipolar disorder are often complex and hard to diagnose. Moreover, this diagnostic challenge is often greatest among youth when the illness is just developing. Uncertainly over the diagnosis can make decisions about medication difficult.

In a collaborative study by Canada’s Lawson Health Research Institute, The Mind Research Network in New Mexico and the Brainnetome Center of the Chinese Academy of Sciences, researchers developed an artificial intelligence (AI) algorithm that analyzes brain scans to better classify illness in patients with a complex mood disorder and help predict their response to medication.

The study included 78 emerging adult patients from mental health programs at London Health Sciences Centre (LHSC), primarily from the First Episode Mood and Anxiety Program (FEMAP).

The first part of the study involved 66 patients who had already completed treatment for a clear diagnosis of either MDD or bipolar type I (bipolar I). Bipolar I is a form of bipolar disorder that features full manic episodes.

Researchers also followed an additional 33 research participants with no history of mental illness. Each individual participated in scanning to examine different brain networks using Lawson’s functional magnetic resonance imaging (fMRI) capabilities at St. Joseph’s Health Care London.

The research team analyzed and compared the scans of those with MDD, bipolar I and no history of mental illness, and found the three groups differed in particular brain networks.

Differences were noted in brain area called the default mode network — a set of regions thought to be important for self-reflection — as well as in the thalamus, a “gateway” that connects multiple cortical regions and helps control arousal and alertness.

The data was used by researchers to develop an AI algorithm that uses machine learning to examine fMRI scans to classify whether a patient has MDD or bipolar I. When tested against the research participants with a known diagnosis, the algorithm correctly classified their illness with 92.4 per cent accuracy.

The research team then performed imaging with 12 additional participants with complex mood disorders for whom a diagnosis was not clear. They used the algorithm to study a participant’s brain function to predict his or her diagnosis and, more importantly, examined the participant’s response to medication.

“Antidepressants are the gold standard pharmaceutical therapy for MDD while mood stabilizers are the gold standard for bipolar I,” said Dr. Elizabeth Osuch, a clinician-scientist at Lawson, medical director at FEMAP and co-lead investigator on the study.

“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?”

The research team hypothesized that participants classified by the algorithm as having MDD would respond to antidepressants while those classified as having bipolar I would respond to mood stabilizers. When tested with the complex patients, 11 out of 12 responded to the medication predicted by the algorithm.

“This study takes a major step towards finding a biomarker of medication response in emerging adults with complex mood disorders,” Osuch said. “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.”

Psychiatrists currently make a diagnosis based on the history and behavior of a patient. Medication decisions are based on that diagnosis. “This can be difficult with complex mood disorders and in the early course of an illness when symptoms may be less well-defined,” said Osuch.

“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.”

Source: Lawson Health Research Institute

Using AI to Better Diagnose Disorders And Target Drug Treatment

Rick Nauert PhD

Rick Nauert, PhDDr. Rick Nauert has over 25 years experience in clinical, administrative and academic healthcare. He is currently an associate professor for Rocky Mountain University of Health Professionals doctoral program in health promotion and wellness. Dr. Nauert began his career as a clinical physical therapist and served as a regional manager for a publicly traded multidisciplinary rehabilitation agency for 12 years. He has masters degrees in health-fitness management and healthcare administration and a doctoral degree from The University of Texas at Austin focused on health care informatics, health administration, health education and health policy. His research efforts included the area of telehealth with a specialty in disease management.

APA Reference
Nauert PhD, R. (2018). Using AI to Better Diagnose Disorders And Target Drug Treatment. Psych Central. Retrieved on December 17, 2018, from https://psychcentral.com/news/2018/08/09/using-ai-to-better-diagnose-disorders-and-target-drug-treatment/137708.html

 

Scientifically Reviewed
Last updated: 9 Aug 2018
Last reviewed: By John M. Grohol, Psy.D. on 9 Aug 2018
Published on PsychCentral.com. All rights reserved.