AI advancements may expedite depression treatment by predicting antidepressant efficacy

Antidepressants are a widely used treatment for moderate to severe depression. Regrettably, patients and healthcare practitioners must wait six to eight weeks to observe any potential improvements. If no benefits emerge, only then can an alteration in treatment proceed. However, scientists from Amsterdam University Medical Center (Amsterdam UMC) and Radboud University Medical Center in the Netherlands have utilized AI to devise a solution for shortening this waiting period.

The scientists concentrated on the application of sertraline, one of the most frequently prescribed drugs in the initial treatment of depression. Its efficacy, however, is sadly limited to approximately half of the patients. A waiting period of several weeks is necessary to evaluate the drug's effects and the patient's condition.

"Such cycles we refer to, can last up to six months," reveals Maarten Poirot, a PhD student at Amsterdam UMC and the study's primary author, to Euronews.

Poirot's research group gathered various data, including predictive MRI factors such as hippocampus volume and blood flow, in addition to relevant clinical information, and utilized this to develop a predictive algorithm. The researchers found that consolidation of such information could potentially result in a model capable of predicting treatment outcomes.

The World Health Organisation states that around 280 million individuals worldwide (equating to nearly 3.8% of the global population) are affected by depression. It afflicts 5.0% of adults and 5.7% of those aged 60 and over. Depressingly, an increasing proportion of children and teenagers are also affected. Therefore, early commencement of treatment and the efficacy of administered drugs are crucial. The longer the wait for positive results, the smaller the prospects of successfully treating someone suffering from depression.