Purpose This study aims to investigate correlation between metabolic risk factors and optic disc cupping and the development of glaucoma. Methods This study is a retrospective, cross-sectional study with over 20-year-old patients that underwent health screening examinations. Intraocular pressure (IOP), fundus photographs, Body Mass Index (BMI), waist circumference (WC), serum triglycerides, serum HDL cholesterol (HDL-C), serum LDL cholesterol (LDL-C), systolic blood pressure (BP), diastolic BP, and serum HbA1c were obtained to analyse correlation between metabolic risk factors and glaucoma. Eye with glaucomatous optic neuropathy(GON) was defined as having an optic disc with either vertical cup-to-disc ratio(VCDR) ≥ 0.7 or a VCDR difference ≥ 0.2 between the right and left eyes by measuring VCDR with deep learning approach. Results The study comprised 15,585 subjects and 877 subjects were diagnosed as GON. In univariate analyses, age, BMI, systolic BP, diastolic BP, WC, triglyceride, LDL-C, HbA1c, and IOP were significantly and positively correlated with VCDR in the optic nerve head. In linear regression analysis as independent variables, stepwise multiple regression analyses revealed that age, BMI, systolic BP, HbA1c, and IOP showed positive correlation with VCDR. In multivariate logistic analyses of risk factors and GON, higher age (odds ratio [OR], 1.054; 95% confidence interval [CI], 1.046–1.063), male gender (OR, 0.730; 95% CI, 0.609–0.876), more obese (OR, 1.267; 95% CI, 1.065–1.507), and diabetes (OR, 1.575; 95% CI, 1.214–2.043) remained statistically significant correlation with GON. Conclusions Among the metabolic risk factors, obesity and diabetes as well as older age and male gender are risk factors of developing GON. The glaucoma screening examinations should be considered in the populations with these indicated risk factors.
Abstract Artificial intelligence capabilities have, recently, greatly improved. In the past few years, one of the deep learning algorithms, the recurrent neural network (RNN), has shown an outstanding ability in sequence labeling and prediction tasks for sequential data. We built a reliable visual field prediction algorithm using RNN and evaluated its performance in comparison with the conventional pointwise ordinary linear regression (OLR) method. A total of 1,408 eyes were used as a training dataset and another dataset, comprising 281 eyes, was used as a test dataset. Five consecutive visual field tests were provided to the constructed RNN as input and a 6 th visual field test was compared with the output of the RNN. The performance of the RNN was compared with that of OLR by predicting the 6 th visual field in the test dataset. The overall prediction performance of RNN was significantly better than OLR. The pointwise prediction error of the RNN was significantly smaller than that of the OLR in most areas known to be vulnerable to glaucomatous damage. The RNN was also more robust and reliable regarding worsening in the visual field examination. In clinical practice, the RNN model can therefore assist in decision-making for further treatment of glaucoma.
AIM: To determine the Bruch’s membrane opening-minimum rim width (BMO-MRW) tipping point where corresponding visual field (VF) damages become detectable. METHODS: A total of 85 normal subjects and 83 glaucoma patients (one eye per participant) were recruited for the study. All of the patients had VF examinations and spectral-domain optical coherence tomography to measure the BMO-MRW. Total deviation values for 52 VF points were allocated to the corresponding sector according to the Garway-Heath distribution map. To evaluate the relationship between VF loss and BMO-MRW measurements, a “broken-stick” statistical model was used. The tipping point where the VF values started to sharply decrease as a function of BMO-MRW measurements was estimated and the slopes above and below this tipping point were compared. RESULTS: A 25.9% global BMO-MRW loss from normative value was required for the VF loss to be detectable. Sectorally, substantial BMO-MRW thinning in inferotemporal sector (33.1%) and relatively less BMO-MRW thinning in the superotemporal sector (8.9%) were necessary for the detection of the VF loss. Beyond the tipping point, the slopes were close to zero throughout all of the sectors and the VF loss was unrelated to the BMO-MRW loss. The VF loss was related to the BMO-MRW loss below the tipping point. The difference between the two slopes was statistically significant (P≤0.002). CONCLUSION: Substantial BMO-MRW loss appears to be necessary for VF loss to be detectable in patients with open angle glaucoma with standard achromatic perimetry.
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