Mental health issues have come into a clearer focus amid the pandemic. Depression became endemic, but it still too often goes undetected. Even when it does, health care providers struggle to meet demand. Two women engineers — both of whom had experienced depression and had trouble finding therapy — thought the answer might be helping medical pros detect depression.
Kintsugi is a startup that wants to put technology to work on the problem. Co-founder and CEO Grace Chang saw this as an access issue: Both founders experienced bouts of depression and found it difficult to get clinicians to help, leading them to think about it from their perspective as engineers.
They figured that if it was possible to identify the people who need therapy the most, it would be easier to achieve the goal of directing those people to suitable treatment. So Chang and co-founder Rima Seiilova-Olson built an API to detect depression through voice.
“We saw this as an infrastructure problem where you have so many people trying to jam through that front door, but not a lot of visibility as to who is severely depressed and who is in this low to moderate phase. And if we can provide this information to those practitioners, we can really deeply affect the specific problem,” she said.
People who are feeling blue tend to have a flat voice, something that clinicians have observed for decades. This is true regardless of language or culture and appears to be a universal human reaction to depression, according to Seiilova-Olson.
“Psychomotor retardation is the process of slowing down of thought and muscle movements. And it’s universal no matter where you’re born or what language you speak,” she said.
Psychiatrists who observe severely depressed patients notice this symptom, Seiilova-Olson noted. Kintsugi is attempting to use technology to build a machine learning model with many more samples than any individual clinician could see in a lifetime. The solution measures the likelihood of depression on the GAD-7 (0-21) scale, with zero being fine and 21 being severely depressed. After a patient grants permission, the clinician can get immediate feedback based on the score. The score, which becomes part of the patient notes, is protected under doctor-patient privilege, according to the company.
“Our neural network model has been trained on tens of thousands of depressed voices. So it can be like a set of psychiatrists, but it’s much more sensitive. It can pick it up even when the depression is at mild or moderate levels,” she said.
Even before the pandemic, depression was rampant. The World Health Organization reports that 5% of adults worldwide suffer from clinical depression. That adds up to 280 million people. It is the leading cause of disability in the world, and it doesn’t have to be that way.
The WHO reports that all forms of depression — whether mild, moderate or severe — are treatable if detected. But too often those with depression suffer in silence and don’t seek help for their condition. A 2017 article published in the SSM Population Health Journal cites a 1999 study that found two-thirds of depression cases in the U.S. go undiagnosed.
This is even more tragic when you consider that 700,000 people take their own lives each year as a result of depression, according to the WHO. Among the problems with getting people into treatment is a lack of trained professionals to help diagnose it, and the fact that medical professionals tend to tackle this problem only when patients report symptoms, which can be unreliable.
Before Chang and Seiilova-Olson could build a model to detect depression through voice, they needed data. The first step involved interviewing around 200 psychologists, psychiatrists and clinicians. They learned through their research that journaling was a good way for people to sort out their feelings.
So the first thing they did was build a free voice journaling app, also called Kintsugi.
