Open Access Open Access  Restricted Access Subscription or Fee Access

GLAUCOMA DETECTION TECHNIQUES: A REVIEW

Sanjeev Indora, Dinesh Kumar Atal, Supiksha Jain

Abstract


The eye is the second most complex and valuable organ after the brain. The second most common among eye conditions is glaucoma, an eye disease that is widespread and has the potential to cause neurodegeneration. The main cause of Glaucoma is abnormal pressure within the eye, which can be challenging to detect in the early stages since there are no clear symptoms. A timely diagnosis plays a critical role in achieving a favorable prognosis and enhancing the quality of life for the patient. Although manual eye examination is a valid method, it requires significant human effort. Glaucoma is relatively common in all races, but it is more prevalent in Africans, Americans, and Afro-Caribbeans. The aim of this review is to present a thorough examination of the various categories of glaucoma, their underlying causes, potential treatment options, publicly accessible image benchmarks, performance metrics, and different methodologies, along with their corresponding advantages and disadvantages. The results of each category are summarized using tabular representations.

Keywords


Glaucoma, Intraocular pressure, Convolutional Neural Network

Full Text:

PDF

References


F. Abdullah et al., “A Review on Glaucoma Disease Detection Using Computerized Techniques,” IEEE Access, vol. 9, pp. 37311–37333, 2021, doi: 10.1109/ACCESS.2021.3061451.

G. L. Traber, “New Technologies for Outcome Measures in Glaucoma : Review by the European Vision Institute Special Interest Focus Group,” 2020, doi: 10.1159/000504892.

“Glaucoma Week 2020 | National Health Portal Of India.” https://www.nhp.gov.in/glaucoma-week-2020_pg (accessed Nov. 12, 2021).

Y. Tham et al., “Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040 A Systematic Review and Meta-Analysis,” Ophthalmology, pp. 1–10, 2020, doi: 10.1016/j.ophtha.2014.05.013.

M. Schulzer, “Intraocular Pressure Reduction in Normal,tension Glaucoma Patients,” 1992, doi: 10.1016/S0161-6420(92)31782-8.

M. A. Kass, “The ocular hypertension treatment study,” J. Glaucoma, vol. 3, no. 2, pp. 97–100, 1994, doi: 10.1097/00061198-199400320-00001.

A. S. Khouri et al., “Once-Daily Netarsudil Versus Twice-Daily Timolol in Patients With Elevated Intraocular Pressure: The Randomized Phase 3 ROCKET-4 Study,” Am. J. Ophthalmol., vol. 204, pp. 97–104, 2019, doi: 10.1016/j.ajo.2019.03.002.

N. A. Mehran, S. Sinha, and R. Razeghinejad, “New glaucoma medications: latanoprostene bunod, netarsudil, and fixed combination netarsudil-latanoprost,” Eye, vol. 34, no. 1, pp. 72–88, 2020, doi: 10.1038/s41433-019-0671-0.

“Five Common Glaucoma Tests | Glaucoma Research Foundation.” https://www.glaucoma.org/glaucoma/diagnostic-tests.php (accessed Nov. 12, 2021).

K. Aziz and D. S. Friedman, “Tonometers - Which one should i use?,” Eye, vol. 32, no. 5, pp. 931–937, May 2018, doi: 10.1038/S41433-018-0040-4.

“5 Common but Important Tests for Glaucoma | Centre For Sight.” https://www.centreforsight.net/blog/5-common-diagnostic-tests-for-glaucoma/ (accessed Nov. 12, 2021).

“Glaucoma Tests - The Glaucoma Community - Responsum Health.” https://responsumhealth.com/glaucoma/tests/ (accessed Nov. 12, 2021).

“6 Tests for Diagnosing Glaucoma.” https://www.verywellhealth.com/six-tests-for-glaucoma-3421935 (accessed Nov. 13, 2021).

“Glaucoma Tests: MedlinePlus Medical Test.” https://medlineplus.gov/lab-tests/glaucoma-tests/ (accessed Nov. 13, 2021).

Y. Hagiwara et al., “Computer-aided diagnosis of glaucoma using fundus images: A review,” Comput. Methods Programs Biomed., vol. 165, pp. 1–12, 2018, doi: 10.1016/j.cmpb.2018.07.012.

T. Saba, S. T. F. Bokhari, M. Sharif, M. Yasmin, and M. Raza, “Fundus image classification methods for the detection of glaucoma: A review,” Microsc. Res. Tech., vol. 81, no. 10, pp. 1105–1121, 2018, doi: 10.1002/jemt.23094.

“Are You at Risk For Glaucoma? | Glaucoma Research Foundation.” https://www.glaucoma.org/glaucoma/are-you-at-risk-for-glaucoma.php (accessed Nov. 14, 2021).

C. Review, “The Pathophysiology and Treatment of Glaucoma A Review,” vol. 311, no. 18, pp. 1901–1911, 2015, doi: 10.1001/jama.2014.3192.

“Glaucoma: Causes, Symptoms, Types, Treatment & Prevention.” https://my.clevelandclinic.org/health/diseases/4212-glaucoma (accessed Nov. 29, 2021).

“Glaucoma: Causes, Types, Symptoms, Diagnosis, and Treatment.” https://www.webmd.com/eye-health/glaucoma-eyes (accessed Nov. 29, 2021).

J. H. et al. Boland MV, Ervin A-M, Friedman DS, “Comparative Effectiveness of Pharmacologic Treatments to,” Ann. Intern. Med., vol. 158, no. 12, pp. 271–279, 2013.

C. Review, “Glaucoma and its treatment,” Hosp. Pharm., vol. 14, no. 2, pp. 90–101, 1979.

K. T. Wong et al., “Thin-section CT of severe acute respiratory syndrome: Evaluation of 73 patients exposed to or with the disease,” Radiology, vol. 228, no. 2, pp. 395–400, 2003, doi: 10.1148/radiol.2283030541.

K. R. Sung, J. S. Kim, G. Wollstein, L. Folio, M. S. Kook, and J. S. Schuman, “Imaging of the retinal nerve fibre layer with spectral domain optical coherence tomography for glaucoma diagnosis,” Br. J. Ophthalmol., vol. 95, no. 7, pp. 909–914, 2011, doi: 10.1136/bjo.2010.186924.

M. E. Karlen, E. Sanchez, C. C. Schnyder, M. Sickenberg, and A. Mermoud, “Deep sclerectomy with collagen implant: Medium term results,” Br. J. Ophthalmol., vol. 83, no. 1, pp. 6–11, 1999, doi: 10.1136/bjo.83.1.6.

A. Ben-hur and D. Horn, “10.1162/15324430260185565,” CrossRef List. Deleted DOIs, vol. 1, no. May 2014, 2000, doi: 10.1162/15324430260185565.

K. R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf, “An introduction to kernel-based learning algorithms,” IEEE Trans. Neural Networks, vol. 12, no. 2, pp. 181–201, 2001, doi: 10.1109/72.914517.

VAPNIK and V. N., “The Nature of Statistical Learning,” Theory. p. 334, 1995, [Online]. Available: https://ci.nii.ac.jp/naid/10020951890. [29] K. Sta̧por, “Support vector clustering algorithm for identification of glaucoma in ophthalmology,” Bull. Polish Acad. Sci. Tech. Sci., vol. 54, no. 1, pp. 139–142, 2006.

J. S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665–685, 1993, doi: 10.1109/21.256541.

K. Balasubramanian and N. P. Ananthamoorthy, “Improved adaptive neuro-fuzzy inference system based on modified glowworm swarm and differential evolution optimization algorithm for medical diagnosis,” Neural Comput. Appl., vol. 33, no. 13, pp. 7649–7660, 2021, doi: 10.1007/s00521-020-05507-0.

M. L. Huang, H. Y. Chen, and J. J. Huang, “Glaucoma detection using adaptive neuro-fuzzy inference system,” Expert Syst. Appl., vol. 32, no. 2, pp. 458–468, 2007, doi: 10.1016/j.eswa.2005.12.010.

X. Chen, Y. Xu, D. Wing, and K. Wong, “Glaucoma Detection based on Deep Convolutional Neural Network,” pp. 715–718, 2015.




DOI: https://doi.org/10.37591/rrjomst.v12i2.3236

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Research & Reviews: Journal of Medical Science and Technology

This Journal archive has been shifted to: https://journals.stmjournals.com/archive/rrjomst/