Summary of Rlperi: Accelerating Visual Perimetry Test with Reinforcement Learning and Convolutional Feature Extraction, by Tanvi Verma et al.
RLPeri: Accelerating Visual Perimetry Test with Reinforcement Learning and Convolutional Feature Extraction
by Tanvi Verma, Linh Le Dinh, Nicholas Tan, Xinxing Xu, Chingyu Cheng, Yong Liu
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel approach is proposed to improve the accuracy and efficiency of visual perimetry tests, a crucial diagnostic tool for detecting vision problems. The method leverages advancements in machine learning and computer vision to analyze patient responses to varying light stimuli. By developing a more accurate and robust model for determining visual field mapping and sensitivity, the study aims to reduce examination times and improve diagnosis outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual perimetry is an important test that helps doctors detect eye problems. The test works by shining different levels of light in front of someone’s eyes while they look at a fixed point. The person tells the doctor what they see, which helps create a map of their visual field and how sensitive it is. But sometimes patients have trouble staying focused during the test, making it take longer and less accurate. |
Keywords
» Artificial intelligence » Machine learning
