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Generating a Glaucoma Diagnosis Report using Deep Learning and Humphrey Visual Field Data

by Tasneem Abdalgadir, Salsabil A. El-Regaily, Thanaa H. Mohamed, El-Sayed M. El-Horbaty
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 26
Year of Publication: 2025
Authors: Tasneem Abdalgadir, Salsabil A. El-Regaily, Thanaa H. Mohamed, El-Sayed M. El-Horbaty
10.5120/ijca2025925473

Tasneem Abdalgadir, Salsabil A. El-Regaily, Thanaa H. Mohamed, El-Sayed M. El-Horbaty . Generating a Glaucoma Diagnosis Report using Deep Learning and Humphrey Visual Field Data. International Journal of Computer Applications. 187, 26 ( Jul 2025), 50-59. DOI=10.5120/ijca2025925473

@article{ 10.5120/ijca2025925473,
author = { Tasneem Abdalgadir, Salsabil A. El-Regaily, Thanaa H. Mohamed, El-Sayed M. El-Horbaty },
title = { Generating a Glaucoma Diagnosis Report using Deep Learning and Humphrey Visual Field Data },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2025 },
volume = { 187 },
number = { 26 },
month = { Jul },
year = { 2025 },
issn = { 0975-8887 },
pages = { 50-59 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number26/generating-a-glaucoma-diagnosis-report-using-deep-learning-and-humphrey-visual-field-data/ },
doi = { 10.5120/ijca2025925473 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-07-31T02:40:11+05:30
%A Tasneem Abdalgadir
%A Salsabil A. El-Regaily
%A Thanaa H. Mohamed
%A El-Sayed M. El-Horbaty
%T Generating a Glaucoma Diagnosis Report using Deep Learning and Humphrey Visual Field Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 26
%P 50-59
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Glaucoma remains a leading cause of irreversible blindness worldwide, emphasizing the need for early and accurate diagnosis. This study presents an automated system for evaluating visual field data using the Humphrey Field Analyzer. By integrating deep learning with Optical Character Recognition (OCR), the proposed model extracts critical clinical parameters from visual field reports, processes them through a trained neural network, and generates structured diagnostic reports. The system was trained on a dataset of Humphrey Visual Field (HVF) images, where key features such as Age, Central 5 threshold values, Mean Deviation (MD), and Pattern Standard Deviation (PSD) were used for classification. Experimental results demonstrated that the proposed model achieved an accuracy of 97.8%, surpassing both traditional manual interpretation (85%) and convolutional neural network-based image classification (93.7%). The system enhances diagnostic consistency and reduces interobserver variability, making it a reliable alternative to conventional methods. However, its performance is influenced by OCR accuracy and variations in test conditions, which may introduce errors in data extraction. The findings highlight the potential of AI-driven systems in clinical ophthalmology, offering a scalable and efficient approach for automated glaucoma assessment and personalized treatment planning.

References
  1. T. Shyamalee, D. Meedeniya, G. Lim, and M. Karunarathne, “Automated Tool Support for Glaucoma Identification With Explainability Using Fundus Images,” IEEE Access, vol. 12, no. January, pp. 17290–17307, 2024, doi: 10.1109/ACCESS.2024.3359698.
  2. J. Kruper, A. Richie-Halford, N. Benson, S. Caffarra, J. Owen, Y. Wu, C. Egan, A. Lee, C. Lee, J. Yeatman, A. Rokem, UK Biobank Eye and Vision Consortium,“Convolutional neural network-based classification of glaucoma using optic radiation tissue properties,” Commun. Med., vol. 4, no. 1, 2024, doi: 10.1038/s43856-024-00496-w.
  3. L. B. Merabet and A. Pascual-Leone, “Neural reorganization following sensory loss: The opportunity of change,” Nat. Rev. Neurosci., vol. 11, no. 1, pp. 44–52, 2010, doi: 10.1038/nrn2758.
  4. D. Bavelier and H. J. Neville, “Cross-modal plasticity: Where and how?,” Nat. Rev. Neurosci., vol. 3, no. 6, pp. 443–452, 2002, doi: 10.1038/nrn848.
  5. J. Caprioli, “Glaucoma: A disease of early cellular senescence,” Investig. Ophthalmol. Vis. Sci., vol. 54, no. 14, 2013, doi: 10.1167/iovs.13-12716.
  6. G. A. Lee, G. Y. X. Kong, and C. H. Liu, “Visual fields in glaucoma: Where are we now?,” Clin. Exp. Ophthalmol., vol. 51, no. 2, pp. 162–169, 2023, doi: 10.1111/ceo.14210.
  7. B. E. Prum, L. F. Rosenb‎erg, S. J. Gedde, S. L. Mansberger, J. D. Stein, S. E. Moroi, L. W. Herndon, M. C. Lim, R. D. Williams,“Primary Open-Angle Glaucoma,” Ophthalmology, vol. 123, no. 1, pp. P41–P111, 2016, doi: 10.1016/j.ophtha.2015.10.053.
  8. J. Phu, A. Agar, H. Wang, K. Masselos, and M. Kalloniatis, “Management of open‐angle glaucoma by primary eye‐care practitioners: toward a personalised medicine approach,” Clin. Exp. Optom., vol. 104, no. 3, pp. 367–384, 2021, doi: 10.1111/cxo.13114.
  9. B. Gesslein, M. Naumovska, O. Neumann, D. Bizios, B. Bengtsson, P. Siesjö, E. Uvelius, B. Hammar, R. Sheikh, “Comparison of perimetric 24- ­ 2 test patterns in detecting visual field defects in patients with tumours in the pituitary region,” Acta Ophthalmol., no. June 2023, pp. 326–333, 2024, doi: 10.1111/aos.15731.
  10. H. D. Jampel, K. Singh, S. C. Lin, T. C. Chen, B. A. Francis, E. Hodapp, J. R. Samples, S. D. Smith, “Assessment of visual function in glaucoma: A report by the American academy of ophthalmology,” Ophthalmology, vol. 118, no. 5, pp. 986–1002, 2011, doi: 10.1016/j.ophtha.2011.03.019.
  11. J. Phu, S. K. Khuu, M. Yapp, N. Assaad, M. P. Hennessy, and M. Kalloniatis, “The value of visual field testing in the era of advanced imaging: clinical and psychophysical perspectives,” Clin. Exp. Optom., vol. 100, no. 4, pp. 313–332, 2017, doi: 10.1111/cxo.12551.
  12. U. Schiefer, E. Papageorgiou, P. A. Sample, J. P. Pascual, B. Selig, E. Krapp, J. Paetzold, “Spatial pattern of glaucomatous visual field loss obtained with regionally condensed stimulus arrangements,” Investig. Ophthalmol. Vis. Sci., vol. 51, no. 11, pp. 5685–5689, 2010, doi: 10.1167/iovs.09-5067.
  13. J. M. Khoury, S. P. Donahue, P. J. Lavin, and J. C. Tsai, “Comparison of 24-2 and 30-2 perimetry in glaucomatous and nonglaucomatous optic neuropathies.,” J. neuro-ophthalmology Off. J. North Am. Neuro-Ophthalmology Soc., vol. 19, no. 2, pp. 100–108, Jun. 1999.
  14. J. Phu, S. K. Khuu, A. Agar, and M. Kalloniatis, “Clinical Evaluation of Swedish Interactive Thresholding Algorithm–Faster Compared With Swedish Interactive Thresholding Algorithm–Standard in Normal Subjects, Glaucoma Suspects, and Patients With Glaucoma,” Am. J. Ophthalmol., vol. 208, pp. 251–264, 2019, doi: 10.1016/j.ajo.2019.08.013.
  15. A. Heijl, V. M. Patella, L. X. Chong, A. Iwase, C. K. Leung, A. Tuulonen, G. C. Lee, T. Callan, B. Bengtsson, “A New SITA Perimetric Threshold Testing Algorithm: Construction and a Multicenter Clinical Study,” Am. J. Ophthalmol., vol. 198, pp. 154–165, 2019, doi: 10.1016/j.ajo.2018.10.010.
  16. Y. Sun, C. Lin, M. Waisbourd, F. Ekici, E. Erdem, S. S. Wizov, L. A. Hark, G. L. Spaeth, “The Impact of Visual Field Clusters on Performance-Based Measures and Vision- Related Quality of Life in Patients with Glaucoma,” Am. J. Ophthalmol., 2016, doi: 10.1016/j.ajo.2015.12.006.
  17. D. M. Blumberg, C. G. D. Moraes, A. J. Prager, Q. Yu, L. Al-aswad, G. A. Cioffi, J. M. Liebmann, D. C. Hood, “Association Between Undetected 10-2 Visual Field Damage and Vision-Related Quality of Life in Patients With Glaucoma,” pp. 1–6, 2017, doi: 10.1001/jamaophthalmol.2017.1396.
  18. Y. Yamazaki, K. Sugisaki, M. Araie, H. Murata, and A. Kanamori, “Relationship between Vision- Related Quality of Life and Central 10 ° of the Binocular Integrated Visual Field in Advanced Glaucoma,” Sci. Rep., pp. 1–9, 2019, doi: 10.1038/s41598-019-50677-0.
  19. A. W. Franzco, I. Goldberg, and A. M. Franzco, “Guidelines for the collaborative care of glaucoma patients and suspects by ophthalmologists and optometrists in Australia,” no. October 2013, pp. 107–117, 2020, doi: 10.1111/ceo.12270.
  20. G. Suspects, O. Hypertensives, and E. Glaucoma, “24-2 Visual Fields Miss Central Defects Shown on 10-2 Tests in Glaucoma Suspects, Ocular Hypertensives, and Early Glaucoma,” vol. 124, no. 10, pp. 1449–1456, 2018, doi: 10.1016/j.ophtha.2017.04.021.24-2.
  21. R. Ritch, D. C. Hood, and D. Ph, “The Prevalence and Nature of Early Glaucomatous Defects in the Central 10° of the Visual Field,” vol. 132, no. 3, pp. 291–297, 2015, doi: 10.1001/jamaophthalmol.2013.7656.The.
  22. J. Phu and M. Kalloniatis, “Ability of 24-2C and 24-2 Grids to Identify Central Visual Field Defects and Structure-Function Concordance in Glaucoma and Suspects,” Am. J. Ophthalmol., vol. 219, pp. 317–331, 2020, doi: 10.1016/j.ajo.2020.06.024.
  23. M. Sullivan-mee, M. Tho, K. Tran, D. Pensyl, G. Tsan, and S. Katiyar, “Prevalence, features, and severity of glaucomatous visual field loss measured with the 10-2 achromatic threshold visual field test Short,” Am. J. Ophthalmol., 2016, doi: 10.1016/j.ajo.2016.05.003.
  24. M. E. West, G. P. Sharpe, D. M. Hutchison, E. Paul, L. M. Shuba, M. T. Nicolela, R. Jayme, “Utility of 10-2 Visual Field Testing in Glaucoma Patients with Early 24-2 Visual Field Loss,” Ophthalmology, 2020, doi: 10.1016/j.ophtha.2020.08.033.
  25. S. Welsbie, F. A. Medeiros, and C. A. Girkin, “Qualitative Evaluation of the 10-2 and 24-2 Visual Field Tests for Detecting Central Visual Field Abnormalities in Glaucoma,” pp. 26–33, 2022, doi: 10.1016/j.ajo.2021.02.015.Qualitative.
  26. J. Phu and M. Kalloniatis, “Comparison of 10-2 and 24-2C Test Grids for Identifying Central Visual Field Defects in Glaucoma and Suspect Patients,” Ophthalmology, vol. 128, no. 10, pp. 1405–1416, 2021, doi: 10.1016/j.ophtha.2021.03.014.
  27. J. Phu and M. Kalloniatis, “A Strategy for Seeding Point Error Assessment for Retesting (SPEAR) in Perimetry Applied to Normal Subjects, Glaucoma Suspects, and Patients With Glaucoma,” Am. J. Ophthalmol., vol. 221, pp. 115–130, 2020, doi: 10.1016/j.ajo.2020.07.047.
  28. J. Phu and M. Kalloniatis, “Viability of Performing Multiple 24-2 Visual Field Examinations at the Same Clinical Visit: The Frontloading Fields Study (FFS),” Am. J. Ophthalmol., vol. 230, pp. 48–59, 2021, doi: 10.1016/j.ajo.2021.04.019.
  29. D. T. Hogarty, D. A. Mackey, and A. W. Hewitt, “Current state and future prospects of artificial intelligence in ophthalmology: a review,” Clin. Exp. Ophthalmol., vol. 47, no. 1, pp. 128–139, 2019, doi: 10.1111/ceo.13381.
  30. M. M. Hasan, J. Phu, A. Sowmya, E. Meijering, and M. Kalloniatis, “Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases,” Clin. Exp. Optom., vol. 107, no. 2, pp. 130–146, 2024, doi: 10.1080/08164622.2023.2235346.
  31. R. Asaoka, H. Murata, K. Hirasawa, Y. Fujino, M. Matsuura, A. Miki, T. Kanamoto, Y. Ikeda, K. Mori, A. Iwase, N. Shoji, K. Inoue, J. Yamagami, M. Araie, "Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images", American Journal of Ophthalmology, vol. 198. Elsevier Inc., 2019. doi: 10.1016/j.ajo.2018.10.007.
  32. Jack Phu, Sieu K Khuu,Michael Yapp, Nagi Assaad, Michael P Hennessy, Michael Kalloniatis, "The value of visual field testing in the era of advanced imaging: clinical and psychophysical perspectives". Clin Exp Optom . 2017;100(4):313–332.
  33. Jack Phu, Sieu K Khuu,Michael Yapp, Nagi Assaad, Michael P Hennessy, Michael Kalloniatis, "The value of visual field testing in the era of advanced imaging: clinical and psychophysical perspectives". Clin Exp Optom . 2017;100(4):313–332.
  34. S. Yi, G. Zhang, C. Qian, Y. Q. Lu, H. Zhong, and J. He, “A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep Learning,” Front. Neurosci., vol. 16, no. June, 2022, doi: 10.3389/fnins.2022.939472.
  35. J. C. Wen, C. S. Lee, P. A. Keane, S. Xiao, A. S. Rokem, P. P. Chen, Y. Wu, A. Y. Lee, “Forecasting future humphrey visual fields using deep learning,” PLoS One, vol. 14, no. 4, Apr. 2019, doi: 10.1371/journal.pone.0214875.
  36. Z. Zhou, B. Li, J. Su, X. Fan, L. Chen, S. Tang, J. Zheng, T. Zhang, Z. Meng, Z. Chen, H. Deng, J. Hu, J. Zhao, “An artificial intelligence model for the simulation of visual effects in patients with visual field defects,” Ann. Transl. Med., vol. 8, no. 11, pp. 703–703, Jun. 2020, doi: 10.21037/atm.2020.02.162.
  37. M. Saifee, J. Wu, Y. Liu, P. Ma, J. Patlidanon, Y. Yu, G. S. Ying, Y. Han, “Development and Validation of Automated Visual Field Report Extraction Platform Using Computer Vision Tools,” Front. Med., vol. 8, Apr. 2021, doi: 10.3389/fmed.2021.625487.
  38. C. Bowd, L. M. Zangwill, C. C. Berry, E. Z. Blumenthal, C. Vasile, C. Sanchez-Galeana, C. F. Bosworth, P. A. Sample, R. N. Weinreb, “Detecting early glaucoma by assessment of retinal nerve fiber layer thickness and visual function,” Investig. Ophthalmol. Vis. Sci., vol. 42, no. 9, pp. 1993–2003, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

Glaucoma Optical Character Recognition Humphrey Visual Field Deep Learning Visual Field

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