Vision loss detection and progression prediction

Vision loss detection and progression prediction

Background

The long onset period and unnoticed symptoms of many ocular diseases make it hard for patients and clinical practitioners to be aware of the functional impairments. For instance, a high prevalence ophthalmic disease—glaucoma—often starts with small scotomas (blind spots) in the periphery, and is easy to be ignored. Glaucoma usually affects the peripheral area of the retina at the early stage, which may affect functional vision so subtly during the initial period that it goes unnoticed in the patient’s daily life often for over many years. However, once it turns to the advanced stage, the central visual acuity is affected severely, leading to irreversible functional visual loss, rapid decrease of living quality, and even the loss of eyeball. Similarly, diabetic retinopathy is another ocular disease that is asymptomatic during the initial stage but will heavily affect visual function during the proliferative stage. The prevalence of diabetes and prediabetes in the United States are 14.6% and 37.5%[1] respectively. And 34.6%[2] of diabetic patients are estimated to have diabetic retinopathy but few of them are diagnosed and have a systemic clinical measurement. Diseases like glaucoma and diabetic retinopathy often go unnoticed until the moderate-to-advances stages of the disease. At the same time, many ocular diseases are irreversible, which makes early detection extremely important to timely initiate or adjust ocular therapy before diseases progress to stages that cause severe loss of living quality.

However, the diagnosis and detection of functional worsening of ocular disease are challenging, as measurements are noisy, and the differentiation between true disease progression and random fluctuations or measurement artifacts is difficult. Besides, the distinguishment between normal people and patients with ocular diseases is not that obvious in real life. People with ocular hypertension required long-term follow-up to prevent them to develop glaucoma. Meanwhile, people with normal-tension glaucoma are hard to be distinguished from normal people and need systemic detection to be found and diagnosed. How to develop an efficient detective method that meets both the screening and follow-up requirements of most ocular diseases is important and urgent.

What We do

Our team aims to better characterize the functional loss from ocular diseases like glaucoma with the goal of developing novel diagnostic methods and improving the prediction of how those diseases may progress in the future.

We developed and evaluated a novel scheme of representative patterns for glaucomatous vision loss based on unsupervised machine learning and explored the impact of individual anatomical parameters on it. These patterns help to distinguish true glaucomatous functional loss from the various and frequent measurement artifacts and from damages caused by other ocular diseases and contribute to the diagnosis of vision loss and its progression. Apart from the improvement of diagnostic and prognostic methods, our team also study the optimal scheduling of patients, i.e., the optimal time intervals for patient testing to maximize the information gain of each test.

The translational potential of this work related to diagnostic improvements is not only reflected by the publications, but also by the patents—namely one patent on modeling of glaucomatous visual field loss and another patent on an algorithm to determine the optimal scheduling intervals for patient testing.

Selected Publications

1. Saeedi O, Boland MV, D’Acunto L, Swamy R, Hegde V, Gupta S, Venjara A, Tsai J, Myers JS, Wellik SR, DeMoraes G, Pasquale LR, Shen LQ, Li Y, Elze T. Development and Comparison of Machine Learning Algorithms to Determine Visual Field Progression. Transl Vis Sci Technol. 2021 Jun 1;10(7):27. doi: 10.1167/tvst.10.7.27. PubMed PMID: 34157101; PubMed Central PMCID: PMC8237084.

2. Teng B, Li D, Choi EY, Shen LQ, Pasquale LR, Boland MV, Ramulu P, Wellik SR, De Moraes CG, Myers JS, Yousefi S, Nguyen T, Fan Y, Wang H, Bex PJ, Elze T, Wang M. Inter-Eye Association of Visual Field Defects in Glaucoma and Its Clinical Utility. Transl Vis Sci Technol. 2020 Nov;9(12):22. doi: 10.1167/tvst.9.12.22. eCollection 2020 Nov. PubMed PMID: 33244442; PubMed Central PMCID: PMC7683854.

3. Choi EY, Li D, Fan Y, Pasquale LR, Shen LQ, Boland MV, Ramulu P, Yousefi S, De Moraes CG, Wellik SR, Myers JS, Bex PJ, Elze T, Wang M. Predicting Global Test-Retest Variability of Visual Fields in Glaucoma. Ophthalmol Glaucoma. 2021 Jul-Aug;4(4):390-399. doi: 10.1016/j.ogla.2020.12.001. Epub 2020 Dec 11. PubMed PMID: 33310194; PubMed Central PMCID: PMC8192590.

4. Mengyu Wang, Lucy Q. Shen, Louis R. Pasquale, Michael V. Boland, Sarah R. Wellik, Carlos Gustavo De Moraes, Jonathan S. Myers, Thao D. Nguyen, Robert Ritch, Pradeep Ramulu, Hui Wang, Jorryt Tichelaar, Dian Li, Peter J. Bex and Tobias Elze, “Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma", Ophthalmology 127 (2020): 731-738.

5. Mengyu Wang, Jorryt Tichelaar, Louis R. Pasquale, Lucy Q. Shen, Michael V. Boland, Sarah R. Wellik, Carlos Gustavo De Moraes, Jonathan S. Myers, Pradeep Ramulu, MiYoung Kwon, Osamah J. Saeedi, Hui Wang, Neda Baniasadi, Dian Li, Peter J. Bex and Tobias Elze, “Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence", JAMA Ophthalmology 138 (2020): 190.

6. Mengyu Wang, Lucy Q. Shen, Louis R. Pasquale, Paul Petrakos, Sydney Formica, Michael V. Boland, Sarah R. Wellik, Carlos Gustavo De Moraes, Jonathan S. Myers, Osamah Saeedi, Hui Wang, Neda Baniasadi, Dian Li, Jorryt Tichelaar, Peter J. Bex and Tobias Elze, “An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis", Investigative Opthalmology & Visual Science 60 (2019): 365.

7. Mengyu Wang, Louis R. Pasquale, Lucy Q. Shen, Michael V. Boland, Sarah R. Wellik, Carlos Gustavo De Moraes, Jonathan S. Myers, Hui Wang, Neda Baniasadi, Dian Li, Rafaella Nascimento E. Silva, Peter J. Bex and Tobias Elze, “Reversal of Glaucoma Hemifield Test Results and Visual Field Features in Glaucoma", Ophthalmology 125 (2018): 352-360.

References

[1] Cheng YJ, Kanaya AM, Araneta MRG, et al. Prevalence of Diabetes by Race and Ethnicity in the United States, 2011-2016. JAMA 2019;322:2389.

[2] Vujosevic S, Aldington SJ, Silva P, et al. Screening for diabetic retinopathy: new perspectives and challenges. The Lancet Diabetes & Endocrinology 2020;8:337-347.