Towards a Connected Mobile Cataract Screening System: A Future Approach

Optical coherence tomography biomarkers DROL, PROS, SND, hyperreflective walls of foveal cystoid spaces as predictors of central macular thickness and visual acuity in diabetic macular edema treated with intravitreal ranibizumab

PURPOSE

This study aims to establish DROL (disruption of retinal outer layers), PROS (photoreceptor outer segment length), SND (subfoveal neuroretinal detachment), and hyperreflective walls of foveal cystoid spaces (HRW) as optical coherence tomography (OCT) biomarkers and predictors of central macular thickness (CMT) and visual acuity in diabetic macular edema (DME) treated with intravitreal ranibizumab (IVR).

METHODS

In this prospective, interventional study performed at a tertiary care center over a span of 1 year from December 2021 to December 2022, 50 eyes of 46 patients of DME were included. Visual acuity and spectral domain OCT imaging were performed at baseline. Using inbuilt calipers on SD-OCT, the horizontal extent of DROL and the vertical extent of PROS were measured manually. SND and HRW were assessed qualitatively. IVR was administered and patients were followed up at 4, 8, and 12 weeks.

RESULTS

The eyes without DROL had statistically significant (P < 0.05) lesser CMT and better BCVA (best-corrected visual acuity) (P < 0.05) after pro re nata injection of IVR. There was a positive correlation between the extent of baseline DROL with final CMT (P < 0.05) and final logMAR BCVA (P > 0.05), whereas negative correlation with the extent of baseline PROS with final CMT (P < 0.05) and final logMAR BCVA (P > 0.05). The presence of HRW and SND predicted non-resolution of CMT and worse visual acuity after treatment with IVR in DME.

CONCLUSION

DROL, PROS, SND, and hyperreflective walls of foveal cystoid spaces may be utilized as qualitative as well as quantitative biomarkers to predict the post-treatment CMT and visual acuity in DME.

Smartphone Eye Examination: Artificial Intelligence and Telemedicine

Background: The current medical scenario is closely linked to recent progress in telecommunications, photodocumentation, and artificial intelligence (AI). Smartphone eye examination may represent a promising tool in the technological spectrum, with special interest for primary health care services. Obtaining fundus imaging with this technique has improved and democratized the teaching of fundoscopy, but in particular, it contributes greatly to screening diseases with high rates of blindness. Eye examination using smartphones essentially represents a cheap and safe method, thus contributing to public policies on population screening. This review aims to provide an update on the use of this resource and its future prospects, especially as a screening and ophthalmic diagnostic tool. Methods: In this review, we surveyed major published advances in retinal and anterior segment analysis using AI. We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for published literature without a deadline. We included studies that compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting prevalent diseases with an accurate or commonly employed reference standard. Results: There are few databases with complete metadata, providing demographic data, and few databases with sufficient images involving current or new therapies. It should be taken into consideration that these are databases containing images captured using different systems and formats, with information often being excluded without essential detailing of the reasons for exclusion, which further distances them from real-life conditions. The safety, portability, low cost, and reproducibility of smartphone eye images are discussed in several studies, with encouraging results. Conclusions: The high level of agreement between conventional and a smartphone method shows a powerful arsenal for screening and early diagnosis of the main causes of blindness, such as cataract, glaucoma, diabetic retinopathy, and age-related macular degeneration. In addition to streamlining the medical workflow and bringing benefits for public health policies, smartphone eye examination can make safe and quality assessment available to the population.

Accuracy of an artificial intelligence-based mobile application for detecting cataracts: Results from a field study

Purpose

To assess the accuracy of e-Paarvai, an artificial intelligence-based smartphone application (app) that detects and grades cataracts using images taken with a smartphone by comparing with slit lamp-based diagnoses by trained ophthalmologists.

Methods

In this prospective diagnostic study conducted between January and April 2022 at a large tertiary-care eye hospital in South India, two screeners were trained to use the app. Patients aged >40 years and with a best-corrected visual acuity <20/40 were recruited for the study. The app is intended to determine whether the eye has immature cataract, mature cataract, posterior chamber intra-ocular lens, or no cataract. The diagnosis of the app was compared with that of trained ophthalmologists based on slit-lamp examinations, the gold standard, and a receiver operating characteristic (ROC) curve was estimated. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were computed.

Results

The two screeners used the app to screen 2,619 eyes of 1,407 patients. In detecting cataracts, the app showed high sensitivity (96%) but low specificity (25%), an overall accuracy of 88%, a PPV of 92.3%, and an NPV of 57.8%. In terms of cataract grading, the accuracy of the app was high in detecting immature cataracts (1,875 eyes, 94.2%), but its accuracy was poor in detecting mature cataracts (73 eyes, 22%), posterior chamber intra-ocular lenses (55 eyes, 29.3%), and clear lenses (2 eyes, 2%). We found that the area under the curve in predicting ophthalmologists' cataract diagnosis could potentially be improved beyond the app's diagnosis based on using images only by incorporating information about patient sex and age (P < 0.0001) and best-corrected visual acuity (P < 0.0001).

Conclusions

Although there is room for improvement, e-Paarvai app is a promising approach for diagnosing cataracts in difficult-to-reach populations. Integrating this with existing outreach programs can enhance the case detection rate.

The Role of Oxidative Stress in the Aging Eye

Given the expanding elderly population in the United States and the world, it is important to understand the processes underlying both natural and pathological age-related changes in the eye. Both the anterior and posterior segment of the eye undergo changes in biological, chemical, and physical properties driven by oxidative stress. With advancing age, changes in the anterior segment include dermatochalasis, blepharoptosis, thickening of the sclera, loss of corneal endothelial cells, and stiffening of the lens. Changes in the posterior segment include lowered viscoelasticity of the vitreous body, photoreceptor cell loss, and drusen deposition at the macula and fovea. Age-related ocular pathologies including glaucoma, cataracts, and age-related macular degeneration are largely mediated by oxidative stress. The prevalence of these diseases is expected to increase in the coming years, highlighting the need to develop new therapies that address oxidative stress and slow the progression of age-related pathologies.

Frontiers in Retinal Image Processing

Visual impairment is considered as a primary global challenge in the present era [...]