A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform

Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetics. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today’s growing medical field presents a more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model’s capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image is preprocessed using contrast-limited adaptive histogram equalization and the discrete wavelet transform. The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy and the Asia-Pacific Tele-Ophthalmology Society. The CNN model is tuned in with different learning rates and optimizers. An accuracy of 72% and an area under curve score of 0.90 was achieved by the CNN model with the Adam optimizer. The recommended study results may reduce diabetes-related vision impairment by early identification of DR severity

Results of screening for retinopathy of prematurity in a large nursery in Kuwait: Incidence and risk factors

Aims: The aim of the study was to report the incidence of retinopathy of prematurity (ROP) and severe ROP and identify the risk factors for their development in a large nursery in Kuwait. Materials and Methods: This was a retrospective, interventional, non-comparative, hospital-based study. Retrospective review of ROP records of premature babies having either birth weight of less than 1501 g or gestational age at birth of 34 weeks or less and born between January 2001 and August 2003. Statistical Analysis: By univariate and multivariate logistic regression analysis. Results: Out of the 599 babies studied, 38.9% developed ROP and 7.8% needed treatment for severe ROP. Multivariate analysis showed low birth weight (OR 13.753, 95% CI 3.66-51.54; ( P < 0.001), gestational age (OR 13.75, 95% CI 3.66-51.54; P < 0.001), surfactant (OR 1.72, 95% CI 1.04-2.83; P = 0.032) and stay in the intensive care unit for longer than 15 days (OR 2.25, 95% CI 1.05-4.85; P = 0.033) to be significant for the development of any ROP. Low birth weight (OR 22.86, 95% CI 3.86-134.82; P = 0.001), bacterial sepsis (OR 3.27, 95% CI 1.51-7.05; P = 0.002) and need for surfactant (OR 4.41, 95% CI 0.94 -20.56; P = 0.059) were found to be the risk factors for severe ROP needing laser treatment. Conclusion: The incidence of both any ROP and ROP needing treatment are comparable to other studies. Low birth weight is the most important risk factor for both any ROP and severe ROP

Pars plana vitrectomy versus three intravitreal injections of bevacizumab for nontractional diabetic macular edema. A prospective, randomized comparative study

Background: The aim of this study was to compare the effectiveness of pars plana vitrectomy (PPV) and removal of the internal limiting membrane (ILM) with three, monthly, intravitreal bevacizumab (IVB) injections for refractory diabetic macular edema. Materials and Methods: This was a prospective, randomized, comparative, interventional study. Forty-four patients were enrolled and randomized in two groups. Twenty-two eyes enrolled in Group I received three IVB injections at monthly interval. Twenty-two eyes were enrolled in Group II which underwent PPV with ILM removal. The primary outcomes measured were: (1) Best corrected logMAR visual acuity (BCVA) using Snellen′s visual acuity chart. (2) Central macular thickness (CMT) on optical coherence tomography. The secondary outcome measures were: Complication rates like (1) progression of lens opacities, (2) high intraocular pressure needing further treatment/procedure, (3) development of vitreous hemorrhage related to the procedure employed, (4) retinal detachment and (5) severe inflammation/endophthalmitis. Results: In Group I (IVB): 3 (13.6%) eyes showed no change in BCVA; 3 (13.6%) eyes reported decrease in BCVA and 16 (72.8%) eyes showed improvement in BCVA; (P = 0.0181). In Group II (PPV): 4 (18.2%) eyes showed no change in BCVA; 5 (22.7%) eyes showed decrease and 13 (59.1%) eyes showed improvement in BCVA (P = 0.0281). Mean decrease in CMT in IVB group was 108.45 μ, whereas mean decrease in CMT in PPV group was 161.36 μ. No major complications were seen in either group. Conclusion: Posttreatment decrease in CMT was more in PPV group and vision improvement more in IVB group. However, no statistically significant difference between the two methods was found