1.
A Diagnostic Model for Screening Diabetic Retinopathy Using the Hand-Held Electroretinogram Device RETeval.
Deng, X, Li, Z, Zeng, P, Wang, J, Liang, J, Lan, Y
Frontiers in endocrinology. 2021;:632457
Abstract
PURPOSE To construct a proper model to screen for diabetic retinopathy (DR) with the RETeval. METHOD This was a cross-sectional study. Two hundred thirty-two diabetic patients and seventy controls were recruited. The DR risk assessment protocol was performed to obtain subjects' DR risk score using the RETeval. Afterwards, the receiver operating characteristic (ROC) curve was used to determine the best cutoff for diagnosing DR. Random forest and decision tree models were constructed. RESULTS With increasing DR severity, the DR score gradually increased. When the DR score was used to diagnose DR, the ROC curve had an area under the curve of 0.881 (95% confidence interval: 0.836-0.927, P < 0.001), with a best cutoff value of 22.95, a sensitivity of 74.3% (95 CI: 66.0%~82.6%), and a specificity of 90.6% (95 CI: 83.7% ~94.8%). The top four risk factors selected by the random forest were used to construct the decision tree for diagnosing DR, which had a sensitivity of 93.3% (95% CI: 86.3%~97.0%) and a specificity of 80.3% (95% CI: 72.1% ~86.6%). CONCLUSIONS The DR risk assessment protocol combined with the decision tree model was innovatively used to evaluate the risk of DR, improving the sensitivity of diagnosis, which makes this method more suitable than the current protocol for DR screening.
2.
Association between Normal Thyroid Hormones and Diabetic Retinopathy in Patients with Type 2 Diabetes.
Zou, J, Li, Z, Tian, F, Zhang, Y, Xu, C, Zhai, J, Shi, M, Wu, G, Zhang, Z, Yang, C, et al
BioMed research international. 2020;:8161797
Abstract
The relationship between normal thyroid function and type 2 diabetes mellitus (T2DM) has been a particular focus for concern. The present study determined the relationship between thyroid hormone levels and the prevalence of diabetic retinopathy (DR) in T2DM patients. A cross-sectional study (n = 633) was performed in Xi'an, Shaanxi Province, China. Subjects were evaluated for anthropometric measurements, thyroid function, and diabetic retinopathy. Logistic regression models were used to assess the relationships between thyroid hormones and DR. Of 633 patients, 243 (38.4%) patients suffered from DR. The prevalence of DR showed a significantly decreasing trend across the quartiles based on free triiodothyronine (FT3) (FT3 quartile 1 group [FT3-Q1] <4.35 pmol/L, FT3 quartile 2 group [FT3-Q2] 4.35-4.70 pmol/L, FT3 quartile 3 group [FT3-Q3] 4.70-5.08 pmol/L, and FT3 quartile 4 group [FT3-Q4] ≥5.08 pmol/L) (56.7%, 42.5%, 33.1%, 23.8%, P < 0.001). In comparison with all participants categorized in FT3-Q1, the multivariable adjusted odds ratios (95% confidence interval) of DR in FT3-Q2, FT3-Q3, and FT3-Q4 were 0.587 (0.340-1.012), 0.458 (0.258-0.813), and 0.368 (0.201-0.673), (P = 0.055, P = 0.008, P = 0.001), respectively. FT3 levels within the normal range are negatively associated with DR in euthyroid patients with type 2 diabetes. Further studies should be aimed at clarifying the relationship between thyroid hormones and T2DM.
3.
Can Artificial Intelligence Make Screening Faster, More Accurate, and More Accessible?
Li, Z, Keel, S, Liu, C, He, M
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.). 2018;(6):436-441
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Abstract
Diabetic retinopathy, glaucoma, and age-related macular degeneration are leading causes of vision loss and blindness worldwide. They tend to be asymptomatic in the early phase of disease and therefore require active screening programs to identify the patients requiring referral and treatment. Deep learning-based artificial intelligence technology has recently become a major topic in the field of ophthalmology. This paper aimed to provide a general view of the major findings on the application of deep learning for the classification of eye diseases from common imaging modalities. In the future, it is expected that these technologies will be applied in real-world screening programs to improve their efficiency and affordability.
4.
Relationship between C-Reactive Protein Level and Diabetic Retinopathy: A Systematic Review and Meta-Analysis.
Song, J, Chen, S, Liu, X, Duan, H, Kong, J, Li, Z
PloS one. 2015;(12):e0144406
Abstract
OBJECTIVES To date, the relationship between C-reactive protein (CRP) level and diabetic retinopathy (DR) remains controversial. Therefore, a systematic review and meta-analysis was used to reveal the potential relationship between CRP level and DR. METHODS A systematic search of PubMed, Embase.com, and Web of Science was performed to identify all comparative studies that compared the CRP level of two groups (case group and control group). We defined that diabetic patients without retinopathy and/or matched healthy persons constituted the control group, and patients with DR were the case group. RESULTS Two cross sectional studies and twenty case control studies including a total of 3679 participants were identified. After pooling the data from all 22 studies, obvious heterogeneity existed between the studies, so a subgroup analysis and sensitivity analysis were performed. Removing the sensitivity studies, the blood CRP levels in the case group were observed to be higher than those in the control group [SMD = 0.22, 95% confidence interval (CI), 0.11-0.34], and the blood CRP levels in the proliferative diabetic retinopathy (PDR) group were also higher than those in the non-proliferative diabetic retinopathy (NPDR) group [SMD = 0.50, 95% CI, 0.30-0.70]. CONCLUSIONS The results from this current meta-analysis indicate that the CRP level might be used as a biomarker to determine the severity of DR.