Summary of Harnessing the Power Of Longitudinal Medical Imaging For Eye Disease Prognosis Using Transformer-based Sequence Modeling, by Gregory Holste et al.
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling
by Gregory Holste, Mingquan Lin, Ruiwen Zhou, Fei Wang, Lei Liu, Qi Yan, Sarah H. Van Tassel, Kyle Kovacs, Emily Y. Chew, Zhiyong Lu, Zhangyang Wang, Yifan Peng
First submitted to arxiv on: 14 May 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 The paper proposes a novel approach to predicting the progression of eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG) using longitudinal medical imaging. The Longitudinal Transformer for Survival Analysis (LTSA) model leverages sequences of fundus photography images taken over extended periods, allowing for dynamic disease prognosis. This is in contrast to traditional approaches that only assess disease presence at a single point in time. The authors demonstrate the effectiveness of LTSA by comparing its performance to a single-image baseline using real-world data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS). The results show significant improvements in predicting late AMD and POAG prognosis, with the model’s temporal attention analysis suggesting that prior imaging still provides valuable information. This work has implications for personalized medicine, enabling more informed treatment planning and patient care. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about developing a new way to predict how eye diseases will progress over time using medical images taken at different times. The traditional approach only looks at one image, but this new method can analyze a series of images to make more accurate predictions. This is important because some eye diseases get worse slowly, and doctors need to know when to start treatment. The authors tested their method using real-world data from two studies and found that it was much better than the traditional approach. This could lead to better patient care and more personalized medicine. |
Keywords
» Artificial intelligence » Attention » Transformer
