Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5963-6. doi: 10.1109/EMBC.2012.6347352.

Glaucoma risk assessment based on clinical data and automated nerve fiber layer defects detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

Yuji Hatanaka, Chisako Muramatsu, Akira Sawada, Takeshi Hara, Tetsuya Yamamoto, Hiroshi Fujita

PMID: 23367287 DOI: 10.1109/EMBC.2012.6347352

Abstract

Glaucoma is the first leading cause of vision loss in Japan, thus developing a scheme for helping glaucoma diagnosis is important. For this problem, automated nerve fiber layer defects (NFLDs) detection method was proposed, but glaucoma risk assessment using this method was not evaluated. In this paper, computerized risk assessment for having glaucoma was attempted by use of the patients' clinical information, and the performances of the NFLDs detection and the glaucoma risk assessment were compared. The clinical data includes the systemic data, ophthalmologic data, and right and left retinal images. Glaucoma risk assessment was built by using machine learning technique, which were artificial neural network, radial basis function (RBF) network, k-nearest neighbor algorithm, and support vector machine. The inputting parameter was ten clinical ones with/without the results of NFLDs detection. As a result, proposed glaucoma risk assessment showed the higher performance than the NFLD detection. The result of the glaucoma risk assessment indicates that the computerized assessment may be useful for the determination of glaucoma risk.

MeSH terms

• Glaucoma / classification
• Glaucoma / epidemiology
• Humans
• Risk Assessment

Publication Types

• Journal Article
• Research Support, Non-U.S. Gov't