Summary of Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal Oct, by Arunava Chakravarty et al.
Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCT
by Arunava Chakravarty, Taha Emre, Dmitrii Lachinov, Antoine Rivail, Hendrik Scholl, Lars Fritsche, Sobha Sivaprasad, Daniel Rueckert, Andrew Lotery, Ursula Schmidt-Erfurth, Hrvoje Bogunović
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research proposes a novel deep learning method for predicting the risk of late dry Age-related Macular Degeneration (dAMD) onset from retinal OCT scans. The approach jointly predicts a risk score inversely related to time-to-conversion and the probability of conversion within a specified time interval. It uses parallel hyperplanes parameterized by the bias term as a function of time, ensuring that risk scores increase over time and future conversion predictions are consistent with AMD stage prediction using actual scans from future visits. The method is designed to handle patient heterogeneity and subtle or unknown imaging biomarkers, and it can be fine-tuned on new datasets acquired with different scanners. The proposed approach achieves high mean AUROCs of 0.82 for Dataset-1 and 0.83 for Dataset-2 across prediction intervals of 6,12, and 24 months. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to predict the risk of age-related macular degeneration (AMD) from medical images. The researchers use special computer programs called deep learning methods that can analyze lots of data at once. They want to make sure their method works well on different kinds of scanners and with people who have different characteristics. To do this, they create a way to fine-tune their model using new data from other scanners. This helps the model be more accurate when it’s used on real patients. The results show that the method is good at predicting when someone will develop AMD, with accuracy scores of 0.82 and 0.83. |
Keywords
» Artificial intelligence » Deep learning » Probability
