Learning from healthy and stable eyes: a new approach for detection of glaucomatous progression

Conclusion: The use of the dependency measurement in the SVDD framework increased the robustness of the proposed change-detection scheme with comparison to the classical support vector machine and SVDD methods. The validation using clinical data of the proposed approach has shown that the use of only healthy and non-progressing eyes to train the algorithm led to a high diagnostic accuracy for detecting glaucoma progression compared to other methods.
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research