Summary of Predicting Depression and Anxiety Risk in Dutch Neighborhoods From Street-view Images, by Nin Khodorivsko et al.
Predicting Depression and Anxiety Risk in Dutch Neighborhoods from Street-View Images
by Nin Khodorivsko, Giacomo Spigler
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the relationship between neighborhood environments and depression and anxiety disorders by analyzing street-view images in the Netherlands. The authors refined two neural network architectures, DeiT Base and ResNet50, to predict neighborhood risk levels based on raw images. The results showed that both models achieved moderate accuracies, with a significant portion of errors being between adjacent risk categories. The authors also employed SHAP and gradient rollout methods to identify specific landscape attributes correlated with depression risk categories, but found unclear correlations. The study suggests the potential of these techniques in monitoring environmental risk factors for mental health issues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how neighborhood environments affect people’s mental health by using computer vision to analyze street-view images. Researchers used special computer models to predict which neighborhoods are more likely to have depression and anxiety problems. They found that their models were pretty good at predicting these risks, but not perfect. The team also tried some extra techniques to see what makes certain neighborhoods worse or better for people’s mental health, but they didn’t find any clear answers. This study could help us understand how our surroundings affect our mental well-being and maybe even create new ways to measure and improve neighborhood environments. |
Keywords
» Artificial intelligence » Neural network
