Summary of Machine Learning Algorithms to Predict the Risk Of Rupture Of Intracranial Aneurysms: a Systematic Review, by Karan Daga et al.
Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review
by Karan Daga, Siddharth Agarwal, Zaeem Moti, Matthew BK Lee, Munaib Din, David Wood, Marc Modat, Thomas C Booth
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
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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 Machine learning educators can expect a comprehensive evaluation of various models and methods used in predicting intracranial aneurysm rupture risk, as this systematic review assesses the performance of machine learning algorithms in identifying rupture-prone aneurysms. The review focuses on the clinical importance of accurately predicting aneurysm rupture risk, which is crucial for prophylactic treatment decisions. Notable models and methods discussed include [insert relevant model names or methods], with their performances benchmarked against [insert datasets or task-specific benchmarks]. This study’s findings will inform clinicians about the most effective approaches for estimating intracranial aneurysm rupture risk. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special kinds of computers (called machine learning) to help doctors predict if a certain kind of blood vessel in the brain might burst. When this happens, it can be very serious and even life-threatening. Doctors want to know which blood vessels are at risk so they can take precautions. The scientists did a big study to see how well these special computers do in predicting when a blood vessel might burst. |
Keywords
* Artificial intelligence * Machine learning
