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For patients to be diagnosed with Major Depressive Disorder, which is thought to be the result of a blend of genetic, environmental, and psychological factors, they have to display several of a long list of symptoms, such as fatigue or lack of concentration. Once diagnosed, they may receive cognitive behavioral therapy or medication to help ease their condition. But not every treatment works for every patient, as symptoms can vary widely.

Recently, many artificial intelligence researchers have begun to develop ways to apply machine learning to medical situations. Such approaches are able to spot trends and details across huge data sets that humans would never be able to, teasing out results that can be used to diagnose other patients. The New Yorker recently ran a particularly interesting essay about using the technique to make diagnoses from medical scans.

Similar approaches are being used to shed light on depression. A study published in Psychiatry Research earlier this year showed that MRI scans can be analyzed by machine-learning algorithms to establish the likelihood of someone suffering from the condition. By identifying subtle differences in scans of people who were and were not sufferers, the team found that they were able to identify which unseen patients were suffering with major depressive disorder from MRI scans with roughly 75 percent accuracy.

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MIT Technology Review

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