Design and development of a noninvasive ocular pressure estimator.

SIGNIFICANCE: A snapshot intraocular pressure (IOP) is ineffective in identifying the IOP peak and fluctuation, especially during sleep. Because IOP variability plays a significant role in the progression of glaucoma, monitoring the IOP, especially during sleep, is essential to capture the dynamic nature of IOP. PURPOSE: We aimed to design an ocular pressure estimator (OPE) that can reliably and accurately measure the IOP noninvasively over closed-eyelid condition. METHODS: Ocular pressure estimator works on the principle that the external pressure applied by raising the IOP of the eyeball is transmitted through a compressible septum to the pressure sensor, thus recording the IOP. A fluid-filled pouch with a pressure sensor was placed over a rubber glove mimicking the eyelid (septum), covering the cornea of enucleated goat eyeballs. A pressure-controlled setup was connected to a goat cadaver eye, which was validated by a rebound tonometer. Cannulation of eyeballs through the lower limbus had the least difference from the control setup values documented using rebound tonometer, compared with cannulation through the optic nerve. Intraocular pressures ranging from 3 to 30 mmHg was induced, and the outputs recorded using OPE were amplified and recorded for 10 minutes (n = 10 eyes). We stratified the randomization of the number of times and the induced pressures. RESULTS: The measurements recorded were found to be linear when measured against an IOP range of 3 to 30 mmHg. The device has excellent reliability (intraclass correlation coefficient, 0.998). The repeatability coefficient and coefficient of variations were 4.24 (3.60 to 4.87) and 8.61% (7.33 to 9.90), respectively. The overall mean difference ± SD between induced IOP and the OPE was 0.22 ± 3.50 (95% confidence interval, -0.35 to 0.79) mmHg across all IOP ranges. CONCLUSIONS: Ocular pressure estimator offers a promising approach for reliably and accurately measuring IOP and its fluctuation noninvasively under a condition mimicking a closed eye.

Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks.

Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person's growth of this condition. Among the essential elements at the top of the list are anxiety and long-term diabetes. If not detected early, this illness might result in permanent eyesight loss. The damage can be reduced or avoided if it is recognized ahead of time. Unfortunately, due to the time and arduous nature of the diagnosing process, it is harder to identify the prevalence of this condition. Skilled doctors manually review digital color images to look for damage produced by vascular anomalies, the most common complication of diabetic retinopathy. Even though this procedure is reasonably accurate, it is quite pricey. The delays highlight the necessity for diagnosis to be automated, which will have a considerable positive significant impact on the health sector. The use of AI in diagnosing the disease has yielded promising and dependable findings in recent years, which is the impetus for this publication. This article used ensemble convolutional neural network (ECNN) to diagnose DR and DME automatically, with accurate results of 99 percent. This result was achieved using preprocessing, blood vessel segmentation, feature extraction, and classification. For contrast enhancement, the Harris hawks optimization (HHO) technique is presented. Finally, the experiments were conducted for two kinds of datasets: IDRiR and Messidor for accuracy, precision, recall, F-score, computational time, and error rate.