Abstract
Aim To establish a classification tree model in DR screening and to compare the DR screening accuracy between the classification tree model and the logistic regression model in type 2 diabetes mellitus (T2DM) patients based on OCTA variables. Methods Two hundred forty-one eyes of 241 T2DM patients were included and divided into two groups: the development cohort and the validation cohort. Optical coherence tomography angiography (OCTA) images were acquired in these patients. The data of foveal avascular zone area, superficial capillary plexus (SCP) density, and deep capillary plexus (DCP) density were exported after automatically analyzing the macular 6 × 6 mm OCTA images, while the data of radial peripapillary capillary plexus (RPCP) density was exported after automatically analyzing the optic nerve head 4.5 × 4.5 mm OCTA images. These OCTA variables were adopted to establish and validate the logistic regression model and the classification tree model. The area under the curve (AUC), sensitivity, specificity, and statistical power for receiver operating characteristic curves of two models were calculated. Results In the logistic regression model, best-corrected visual acuity (BCVA) (LogMAR) and SCP density were entered (BVCA : OR= 60.30, 95% CI= [2.40, 1513.82], p = 0.013; SCP density: OR= 0.86, 95% CI= [0.78, 0.96], p = 0.006). The AUC, sensitivity, and specificity for detecting early-stage DR (mild to moderate NPDR) in the development cohort were 0.75 (95% CI: [0.66, 0.85]), 63%, and 83%, respectively. The AUC, sensitivity, and specificity in the validation cohort were 0.75 (95% CI: [0.66, 0.84]), 79%, and 72%, respectively. In the classification tree model, BVCA (LogMAR), DM duration, SCP density, and DCP density were entered. The AUC, sensitivity, and specificity for detecting early-stage DR were 0.72 (95% CI: [0.60, 0.84]), 66%, and 76%, respectively. The AUC, sensitivity, and specificity in the validation cohort were 0.74 (95% CI: [0.65, 0.83]), 74%, and 72%, respectively. The statistical power of the development and validation cohorts in two models was all more than 99%. Conclusions Compared to the logistic regression model, the classification tree model has similar accuracy in predicting early-stage DR. The classification tree model with OCTA variables may be a simple tool for clinical practitioners to identify early-stage DR in T2DM patients. Moreover, SCP density is significantly reduced in mild-to-moderate NPDR eyes and might be a biomarker in early-stage DR detection. Further improvement and validation of the DR diagnostic model are awaiting to be performed.
Highlights
Diabetic retinopathy (DR) is a serious ocular complication of diabetes mellitus (DM) that may cause irreversible blindness among the working population worldwide [1,2]
Erefore, the purpose of this study was to establish a classification tree model in DR screening and to compare the DR screening accuracy of two models, the classification tree model and the logistic regression model, in type 2 diabetes mellitus (T2DM) patients based on optical coherence tomography angiography (OCTA) variables
A 6 × 6 mm area centered on the fovea and a 4.5 × 4.5 mm area centered on the optic disc were captured (Figure 1). e data of FAZ area, superficial capillary plexus (SCP) density, and deep capillary plexus (DCP) density were exported after automatically analyzing the macular 6 × 6 mm OCTA images, while the data of radial peripapillary capillary plexus (RPCP) density was exported after automatically analyzing the ONH 4.5 × 4.5 mm OCTA images
Summary
Diabetic retinopathy (DR) is a serious ocular complication of diabetes mellitus (DM) that may cause irreversible blindness among the working population worldwide [1,2]. Diabetic macular edema (DME), retinal neovascularization, and tractional retinal detachment are common and severe complications of DR. Antivascular endothelial growth factor (anti-VEGF) and dexamethasone implants treatment are useful treatments for DME [4,5]. Anti-VEGF has proved to be effective in retinal neovascularization [1]. The prognosis of these severe complications of DR is relatively poor. Manual fundus examination and fundus photography are traditional and useful methods to detect DR, but they are not quantitative and can only be determined by ophthalmologists [7]. Many artificial intelligence (AI) products have been developed based on fundus photography, their practical uses are limited because of their relatively narrow application, complicated manipulations, and expense [8,9]. In our previous studies and other researchers’ studies [4,11,12,13], the vessel densities decreased more significantly as the severity degree became higher, which meant the vessel density alterations between nondiabetic retinopathy (NDR) and early-stage DR (mild and moderate non-proliferative diabetic retinopathy (NPDR)) are less obvious than those between NDR and severe NPDR or proliferative diabetic retinopathy (PDR)
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