Russia Creates Neural Network for Depression Diagnosis
Russian scientists developed a neural network-based model to detect depression by integrating genetic, neurological, and behavioral data. The model achieved an accuracy of over 96% in distinguishing between those with depression and healthy individuals, compared to about 86% in previous models. The research was based on data from more than 3,000 people from Siberia, including electroencephalogram measurements, psychological questionnaires, blood sample and cheek swab analyses, as well as the examination of 164 genetic loci to identify genomic deviations. The researchers reported that the model produced fewer false negative results, which is particularly important in initial diagnosis, as failing to detect depression in an affected individual is more dangerous than a false positive in a healthy person.
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