Evaluation of the external validity of a joint structure-function model for monitoring glaucoma progression

Follow-up studies of the classical landmark studies in Glaucoma

PURPOSE OF REVIEW

It was not until the nineteen nineties that there was scientific evidence for the primary treatment concept of glaucoma, lowering intraocular pressure. The treatment concept of lowering intraocular pressure is now proven by randomized controlled clinical trials (landmark studies). This review provides an overview of the follow-up studies to these landmark studies from the last 18 months.

RECENT FINDINGS

The 20-year data of the ocular hypertension treatment study showed a 49% incidence of primary open-angle glaucoma. The data of this landmark study was used for developing different machine learning algorithms. Bilateral disease, disc hemorrhages, and higher intraocular pressure (IOP) were risk factors for visual field deterioration in the United Kingdom Glaucoma Treatment Study. The Advanced Glaucoma Intervention Trial and the Collaborative Initial Glaucoma Treatment Study identified demographic factors, comorbidity, and clinical variables associated with visual field damage. Analysis was performed on Chinese subsets from the Laser in Glaucoma and Ocular Hypertension Study (LiGHT). Looking at all the follow-up studies to the tube shunt landmark studies, preoperative IOP, neovascular glaucoma, Ahmed implantation, and younger age were predictors of failure. A follow-up study to the Effectiveness in Angle-Closure Glaucoma in Lens Extraction Study showed that patients were ten times more likely to maintain a drop-free target IOP after lens extraction than after laser iridotomy. A superior location for iridotomy results in significantly more significant angle widening than temporal or nasal locations, as shown by a follow-up study to the Zhongshan Angle Closure Prevention Trial using OCT and gonioscopy.

SUMMARY

The number of published follow-up studies to the landmark studies in glaucoma show the ongoing development of clinical questions in management of glaucoma.

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases

Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology.

The Usefulness of Assessing Glaucoma Progression With Postprocessed Visual Field Data

Purpose

Data postprocessing with statistical techniques that are less sensitive to noise can be used to reduce variability in visual field (VF) series. We evaluated the detection of glaucoma progression with postprocessed VF data generated with the dynamic structure–function (DSF) model and MM-estimation robust regression (MRR).

Method

The study included 118 glaucoma eyes with at least 15 visits selected from the Rotterdam dataset. The DSF and MRR models were each applied to observed mean deviation (MD) values from the first three visits (V_1–3) to predict the MD at V_4. MD at V₅ was predicted with data from V_1–4 and so on until the MD at V₉ was predicted, creating two additional datasets: DSF-predicted and MRR-predicted. Simple linear regression was performed to assess progression at the ninth visit. Sensitivity was evaluated by adjusting for false-positive rates estimated from patients with stable glaucoma and by using longer follow-up series (12th and 15th visits) as a surrogate for progression.

Results

For specificities of 80% to 100%, the DSF-predicted dataset had greater sensitivity than the observed and MRR-predicted dataset when positive rates were normalized with corresponding false-positive estimates. The DSF-predicted and observed datasets had similar sensitivity when the surrogate reference standard was applied.

Conclusions

Without compromising specificity, the use of DSF-predicted measurements to identify progression resulted in a better or similar sensitivity compared to using existing VF data.

Translational Relevance

The DSF model could be applied to postprocess existing visual field data, which could then be evaluated to identify patients at risk of progression.

A Case for The Use of Artificial Intelligence in Glaucoma Assessment

We hypothesize that artificial intelligence applied to relevant clinical testing in glaucoma has the potential to enhance the ability to detect glaucoma. This premise was discussed at the recent Collaborative Community for Ophthalmic Imaging meeting, "The Future of Artificial Intelligence-Enabled Ophthalmic Image Interpretation: Accelerating Innovation and Implementation Pathways," held virtually September 3-4, 2020. The Collaborative Community in Ophthalmic Imaging (CCOI) is an independent self-governing consortium of stakeholders with broad international representation from academic institutions, government agencies, and the private sector whose mission is to act as a forum for the purpose of helping speed innovation in healthcare technology. It was one of the first two such organizations officially designated by the FDA in September 2019 in response to their announcement of the collaborative community program as a strategic priority for 2018-2020. Further information on the CCOI can be found online at their website (https://www.cc-oi.org/about). Artificial intelligence for glaucoma diagnosis would have high utility globally, as access to care is limited in many parts of the world and half of all people with glaucoma are unaware of their illness. The application of artificial intelligence technology to glaucoma diagnosis has the potential to broadly increase access to care worldwide, in essence flattening the Earth by providing expert level evaluation to individuals even in the most remote regions of the planet.