Artificial intelligence for classifying uncertain images by humans in determining choroidal vascular running pattern and comparisons with automated classification between artificial intelligence.

PURPOSE: Abnormalities of the running pattern of choroidal vessel have been reported in eyes with pachychoroid diseases. However, it is difficult for clinicians to judge the running pattern with high reproducibility. Thus, the purpose of this study was to compare the degree of concordance of the running pattern of the choroidal vessels between that determined by artificial intelligence (AI) to that determined by experienced clinicians. METHODS: The running pattern of the choroidal vessels in en face images of Haller's layer of 413 normal and pachychoroid diseased eyes was classified as symmetrical or asymmetrical by human raters and by three supervised machine learning models; the support vector machine (SVM), Xception, and random forest models. The data from the human raters were used as the supervised data. The accuracy rates of the human raters and the certainty of AI's answers were compared using confidence scores (CSs). RESULTS: The choroidal vascular running pattern could be determined by each AI model with an area under the curve better than 0.94. The random forest method was able to discriminate with the highest accuracy among the three AIs. In the CS analyses, the percentage of certainty was highest (66.4%) and that of uncertainty was lowest (6.1%) in the agreement group. On the other hand, the rate of uncertainty was highest (27.3%) in the disagreement group. CONCLUSION: AI algorithm can automatically classify with ambiguous criteria the presence or absence of a symmetrical blood vessel running pattern of the choroid. The classification was as good as that of supervised humans in accuracy and reproducibility.

Structural Characterization of Glaucoma Patients with Low Ocular Blood Flow.

Purpose: There is an unclear relationship between ocular blood flow (OBF) and the structural characteristics of the optic nerve head (ONH) in glaucoma, a multifactorial disease. This study used laser speckle flowgraphy (LSFG) to identify low-OBF glaucoma patients and investigated the ONH in these patients. Materials and Methods: In 533 eyes with glaucoma, we determined confounding factors for LSFG-measured OBF (tissue-area mean blur rate: MT) and corrected MT with a linear mixed-effects model (LMM). Structural ONH data (from fundus stereo photography), OCT data, and clinical characteristics were then compared in patients with corrected MT in the upper and lower quartiles using the LMM. Results: Single regression showed significant correlations between MT and age, spherical equivalent (SE), central corneal thickness (CCT), and a weighted count of retinal ganglion cells (wRGC), but not axial length or systemic blood pressure. Gender also significantly influenced MT; MT was corrected for these correlated factors and also glaucoma type with the LMM. The lower-quartile MT group had a significantly larger cup area and cup-disc area ratio and lower temporal quadrant circumpapillary retinal nerve fiber layer thickness (cpRNFLT) and macular ganglion cell complex (GCC) than the upper-quartile group. Conclusions: Low-OBF glaucoma patients were characterized by a larger cup-disc ratio, and higher susceptibility to damage in the temporal disc and the macular area.