Summary of State-of-the-art Periorbital Distance Prediction and Disease Classification Using Periorbital Features, by George R. Nahass et al.
State-of-the-Art Periorbital Distance Prediction and Disease Classification Using Periorbital Features
by George R. Nahass, Ghasem Yazdanpanah, Madison Cheung, Alex Palacios, Jeffrey C. Peterson, Kevin Heinze, Sasha Hubschman, Chad A. Purnell, Pete Setabutr, Ann Q. Tran, Darvin Yi
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper presents three deep-learning methods for segmenting and predicting periorbital distances around the eyes and lids, which hold valuable information for disease quantification and monitoring. The methods are evaluated against the current state-of-the-art (SOTA) method for periorbital distance prediction, demonstrating improved performance on multiple datasets. Additionally, the paper shows that segmentation networks can be used as intermediary steps in classification models to increase their generalizability in ophthalmic plastic and craniofacial surgery by avoiding the out-of-distribution problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study develops new ways to measure distances around the eyes using artificial intelligence (AI). This information is important for doctors to track changes in patients’ health. The researchers created three AI models that can accurately predict these distances, which is faster and more accurate than humans doing it manually. They also tested their models against other top-performing methods and found they did better. Finally, the study shows how AI can be used to improve how well doctors diagnose certain eye problems. |
Keywords
» Artificial intelligence » Classification » Deep learning
