Glaucoma Detection Using Support Vector Machine Method Based on Spectralis OCT

Abstract

Spectralis optical coherence tomography (OCT) provided more detailed parameters in the peripapillary and macular areas among the OCT machines, but it is not easy to understand the enormous information (114 features) generated from Spectralis OCT in glaucoma assessment. Machine learning methodology has been well-applied in glaucoma detection in recent years and has the ability to process a large amount of information at once. Here we aimed to analyze the diagnostic capability of Spectralis OCT parameters on glaucoma detection using Support Vector Machine (SVM) classification method in our population. Our results showed that applying all OCT features with the SVM method had good capability in the detection of glaucomatous eyes (area under curve (AUC) = 0.82), as well as discriminating normal eyes from early, moderate, or severe glaucomatous eyes (AUC = 0.78, 0.89, and 0.93, respectively). Apart from using all OCT features, the minimum rim width (MRW) may be good feature groups to discriminate early glaucomatous from normal eyes (AUC = 0.78). The combination of peripapillary and macular parameters, including MRW_temporal inferior (TI), MRW_global (G), ganglion cell layer (GCL)_outer temporal (T2), GCL_inner inferior (I1), peripapillary nerve fiber layer thickness (ppNFLT)_temporal superior (TS), and GCL_inner temporal (T1), provided better results (AUC = 0.84). This study showed promise in glaucoma management in the Taiwanese population. However, further validation study is needed to test the performance of our proposed model in the real world.

Early Detection of Primary Open Angle, Angle Closure, and Normal Tension Glaucoma in an Asian Population Using Optical Coherence Tomography

“…Therefore, the diagnostic value of these new parameters must be explored, particularly in the detection of early glaucoma. As we reported previously, through machine learning, Spectralis OCT exhibits a good differentiation ability for various stages of glaucoma among the Chinese population in Taiwan 31,32. Using Spectralis OCT in the present study, we evaluated the diagnostic capacity of its various individual parameters for different subtypes of early glaucoma in the Chinese population in Taiwan.…”

“…As we reported previously, through machine learning, Spectralis OCT exhibits a good differentiation ability for various stages of glaucoma among the Chinese population in Taiwan. 31,32 Using Spectralis OCT in the present study, we evaluated the diagnostic capacity of its various individual parameters for different subtypes of early glaucoma in the Chinese population in Taiwan. Furthermore, we attempted to enhance the diagnostic capacity of the individual parameters by using multiple logistic regression models developed using all parameters.…”

Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis

“…The detailed study characteristics are presented in Table 1. A total of 20 studies [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46] were included in the article which comprises 51 models. The studies were published in years from 2014 to 2023 and involved 13 diverse countries/regions [Australia, Brazil, China, India, Japan, Korea, Pakistan, Singapore, Romania, Spain, Taiwan (China), United States, and Nepal], two of which were multicenter surveys.…”

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

“…Chao-Wei et al [106] aimed to evaluate performance of Spectral is OCT parameters on glaucoma detection using the SVM classification approach. Our results showed that the SVM technique combined with all OCT features had good performance in detecting the area under the curve of glaucomatous eyes as well as discriminating early, moderate, or severe glaucoma and normal eyes.…”