Detecting Depression Using a Framework Combining Deep Multimodal Neural Networks With a Purpose-Built Automated Evaluation

Machine learning (ML) has been introduced into the medical field as a means to provide diagnostic tools capable of enhancing accuracy and precision while minimizing laborious tasks that require human intervention. There is mounting evidence that the technology fueled by ML has the potential to detect and substantially improve treatment of complex mental disorders such as depression. We developed a framework capable of detecting depression with minimal human intervention: artificial intelligence mental evaluation (AiME). This framework consists of a short human-computer interactive evaluation that utilizes artificial intelligence, namely deep learning, and can predict whether the participant is depressed or not with satisfactory performance. Because of its ease of use, this technology can offer a viable tool for mental health professionals to identify symptoms of depression, thus enabling a faster preventative intervention. Furthermore, it may alleviate the challenge of observing and interpreting highly nuanced physiological and behavioral biomarkers of depression by providing a more objective evaluation.