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Depression and IA

AI predicts effective depression treatment based on brainwave patterns

Tracking brainwave patterns and symptoms in patients with depression, researchers used artificial intelligence to predict best treatment options.

Current methods used to diagnose and treat depression are imprecise at best, relying largely on subjective answers to survey questions, said Leanne Williams, PhD, Stanford professor of psychiatry. At worse, these approaches can result in treatment choices that further postpone a patient's recovery as the disease progresses. 

"Currently treatments are a trial and error process," Williams said. "It's one size fits all. If the first treatment doesn't work, a second gets tried. We need a more precise tool to pick the best treatment option first."

Williams and her collaborators set out to define a more effective model, which they hope can be used in clinics soon. In a recent study, they deployed an algorithm to interpret brainwave patterns unique to individuals with depression, with the goal of better pinpointing which symptoms change with treatment.

"We know that depression is very heterogenous, and that there are at least 1,000 unique combinations of symptoms that can be diagnosed as depression," said Williams, who is the director of Stanford's Center for Precision Mental Health and Wellness. "We've found that brainwave measurements can be used to help identify which particular symptoms change with antidepressant treatment and which do not."

Predicting which symptoms improve

Major depression is the most common mental disorder in the United States, affecting about 7% of adults in 2017, according to the National Institute of Mental Health. Among those, about half never get diagnosed; and for those who do, finding the right treatment can take years with the current trial and error process.

For Williams' study, data was collected from 518 patients diagnosed with depression randomized to eight weeks of treatment with one of three different antidepressants. Based on brainwave data, the algorithm successfully predicted which symptoms improved with treatment, with highest performance for seven symptoms including insight and loss of weight.

Creating new, objective, high-tech lab tests to help diagnose mental disorders has long been the goal of Williams and other translational neuroscientists. Instead of running blood tests or using measurements taken from heart monitors, clinicians currently rely on a survey: Either the patient or the physician lists the symptoms themselves. If a patient has a certain number of a wide variety of different symptoms --  among them low mood, appetite changes, loss of insight, loss of energy and poor concentration --  they receive a broad  diagnosis of clinical depression. 

Complex relationships in data

For the new model, Williams collaborated with researchers at Stanford's AI for Healthcare Bootcamp in a group led by Andrew Ng, PhD, adjunct professor of computer science. The team set out to design an algorithm able to predict improvement of various depressive symptoms with antidepressant treatment. Individual symptom data were combined with individual recordings from electroencephalography (EEG) tests which monitored electrical activity in the brains of the participants.

"We can apply artificial intelligence to learn complex relationships in data," said Pranav Rajpurkar, a PhD student in computer science, who shared lead authorship of the study with Jingbo Yang, a master's student. "We are able to learn and discover interesting relationships between a patient's depression symptoms -- and EEG readings -- at start of treatment, and their depression symptoms eight weeks in."

The algorithm was also able to identify individuals with clinical symptoms associated with a higher risk of poor outcomes, such as suicide, Rajpurkar said. These symptoms may otherwise have been missed due to the subjective nature of diagnosis. For example, the symptom labeled 'poor insight', which means that the patient may not be able to realize the extent of their illness, often gets overlooked.

"We need new models, such as this one, to provide objective measures of these depression risk factors to identify people who may benefit from more intensive treatments, or treatments other than antidepressants, with the goal of getting the best treatment fast," she said.

Image by Andrii

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