3D Visualization of Depression and Anxiety

Abstract

We present a novel and technologically enhanced teaching tool geared towards both patients, students, and clinicians with the primary goal of educating these groups about the anatomical and biochemical implications of various syndromes involving depression and anxiety. Our secondary goal is to inform patients, students, and clinicians about the most common treatment methods used to treat these syndromes. We achieved this by mapping the mesolimbic system, otherwise known as the “reward pathway,” and other relevant neuroanatomical structures using a high‐resolution cryosection dataset made available by the Big Brain project at McGill University and constructing 3D visualized models using 3D analysis software tools. The results of this project were compiled into a series of three interactive educational modules serving each of our goals. The first module described the neuroanatomical structures involved in syndromes of depression and anxiety along with their functions. The second module describes various forms of clinical depression and treatment options, including the mechanism of action and how it applies to the underlying disease process. The third module did the same but described anxiety rather than depression. This tool is valuable to patients and care providers alike because it explains neuroanatomical and biochemical aspects of these syndromes in a straight‐forward and easy‐to‐understand visual manner. It also describes the context of the best pharmacological interventions for each syndrome, which is a substantial step to help engage patients in their personalized mental health treatment plan. Support or Funding Information Thank you to Dr. David Dufeau and Marian University College of Osteopathic Medicine for the resources and guidance throughout this research project.

English Language Intelligent Translation System Based on 3D Visualization Technology

“…e Transformer model has three attention mechanisms, including the encoder multihead attention mechanism, the decoder mask multihead attention mechanism, and the encoder-decoder multihead attention mechanism [19]. e neural machine translation system based on the Transformer's structure not only improves the training speed but also achieves excellent results in translation tasks.…”