The Role of Artificial Intelligence in Ophthalmic Anterior Segment Disorders
Main Article Content
Abstract
Artificial intelligence involves machines that can synthesize data on a scale that exceeds human ability, the capacity to analyze, learn, predict, and reason using algorithms that have the potential to improve over time. Artificial intelligence is beneficial in accuracy, speed, ability to analyze vast amounts of data, automating workflow, and reducing the need for repetitive tasks, and reducing human error. These tasks are particularly important for speech and image recognition, analyzing data, and creating predictive models. In health care, artificial intelligence can help guide diagnosis, treatment options, compliance, teaching, and administration activities. These activities have been demonstrated in many areas of medicine including Ophthalmology and in particular the retina and posterior segment subspecialty. This paper is a comprehensive review of the current applications of artificial intelligence in anterior segment specialties of Ophthalmology. This paper will demonstrate the applications of artificial intelligence in 1) Glaucoma to predict progression of disease, need for surgery, and who may develop acute angle closure glaucoma, 2) Keratoconus to identify early or subclinical keratoconus and predict who may experience progressive disease, 3) Keratitis to predict causation and which cases are more prone to rapidly progress, 4) Cataract to detect and give diagnostic objectivity, to calculate IOL power with more precision, to create smart surgery operating theaters, to aid in surgical training and to assess post-operative healing.
Article Details
How to Cite
DEBROFF, Brian M; AKBOUR, Yahya.
The Role of Artificial Intelligence in Ophthalmic Anterior Segment Disorders.
Medical Research Archives, [S.l.], v. 12, n. 11, nov. 2024.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/6034>. Date accessed: 12 dec. 2024.
doi: https://doi.org/10.18103/mra.v12i11.6034.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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31. Bodmer NS, Christensen DG, Bachmann LM, et al. Deep learning models used in the diagnostic workup of keratoconus: a systematic review and exploratory meta-analysis. Cornea. 2024;43(7):916-931.
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33. Goodman D and Zhu AY. Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review. Frontiers in Ophthalm. 2024;4:1380701.
34. Afifah A, Syarfira F, Afladhanti PM and Dharmawidiarini D. Artificial intelligence as diagnostic modality for keratoconus: A systematic review and meta-analysis. J Taibah Univ Med Sci. 2024;19(2);296-303.
35. Al-Timemy AH, Mosa ZM, Alyasseri Z, et al. A hybrid deep learning construct for detecting keratoconus from corneal maps. Transl Vis Sci Technol. 2021;10(14):16.
36. Kindu G, Shetty N, Shetty R, et al. Artificial intelligence-based stratification of demographic, ocular surface high-risk factors in progression of keratoconus. Indian J Ophthalmol. 2023;71:1882-88.
37. Jimenez-Garcia M, Issarti I, Kreps EO, et al. Forecasting progressive trends in keratoconus by means of a time delay network. 2021;10(15):3238.
38. Gu H, Guo Y, Gu L, et al. Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs. Scientific Reports. 2020;10: 17851.
39. Hu S, Sun Y, Li J, et al. Automatic diagnosis of infectious kertatitis based on slit lamp images analysis. J Pers Med. 2023;13(3):519.
40. Lv J, Zhang K, Chen Q, et al. Deep learning-based automated diagnosis of fungal keratitis with in vivo confocal microscopy images. Annals of Translational Med. 2020;8(11):706.
41. Jiang J, Liu W, Pei M, et al. Automatic diagnosis of keratitis using object localization combined with cost-sensitive deep attention convolutional neural network. Journal of Big Data. 2023;10:121.
42. Sarayar R, Lestari YD, and Setio AAA. Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysis. Front Public Health. 2023;11:1239231.
43. Kuo MT, Wie-Yun B, Yu-Kai Y, et al. A deep learning approach in diagnosing fungal keratitis based on corneal photographs. Scientific Reports. 2020;10:14424.
44. Hung N, Shih AKY, Lin C, et al. Using slit-lamp images for deep learning-based identification of bacterial and fungal keratitis: model development and validation with different convolutional neural networks. Diagnostics. 2021;11(7):1246.
45. Ghosh AK, Thammasudjarit R, and Jongkhajornpong P. A deep learning for discrimination between fungal keratitis and bacterial keratitis: deepkeratitis. Cornea. 2022;41(5):616.
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51. Ladas JG, Siddiqui AA, Devgan U, and Jun AS. A 3-D “super surface” combining modern intraocular lens formulas to generate a “super formula” and maximize accuracy. JAMA Ophthalmol. 2015; 133(12):1431–1436.
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59. Davenport TH, Glaser JP. Factors governing the adoption of artificial intelligence in healthcare providers. Discov Health Syst. 2022;1(1):4.
2. Guishan V, Peng L, Coram M, Stumpe MC et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316:2402-10.
3. Li Z, Guo C, Nie D et al. Automated detection of retinal exudates and drusen in ulta-widefield fundus images based on deep learning. Eye 2022;36:1681-86.
4. Lee JWY, Chan PP, Zhang XJH, et al. Latest developments in normal-pressure glaucoma: diagnosis, epidemiology, genetics, etiology, causes and mechanisms to management. Asia Pac J Ophth. 2019;8(6):457-68.
5. Weinreb RN, Khaw PT. Primary open-angle glaucoma. Lancet. 2004;363(9422): 1711-1720.
6. Yousefi S, Kiwaki T, Zheng Y et al. Am J Ophth. 2018;193:71-79.
7. Sidhu Z and Mansoori T. Artificial Intelligence in glaucoma detection using color fundus photographs. Indian J Ophth. 2024;3(1):408-11.
8. Li Z, He Y, Keel S et al. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology. 2018;125(8):1199-1206.
9. Li F, Yan L, Wang Y et al. Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs. Graefes Arch Clin Exp Ophth. 2020;258(4):851-867.
10. Coan LJ, Williams BM, Krishna AV et al. Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review. Surv Ophth. 2023;68(1):17-41.
11. Inoue R, Hangai M, Kotera Y et al. Three-dimensional high-speed optical coherence tomography imaging of lamina cribosa in glaucoma. Ophthalmology. 2009;116(2): 214-222.
12. Kang JM, Tanna AP. Glaucoma. Med Clin N Am. 2021;105(3):493-510.
13. Ran AR, Cheung CY, Wang X et al. Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis. Digit Health. 2019;1:172-182.
14. Shi N, Li Jing, Liu G, and Cao M. Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis. Int J Ophthalmol. 2024;17(3):408-19.
15. Shigueoka LS, Vasconcellos JPC, Schimiti RB, et al. Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma. PLoS One. 2018;13(12) :e0207784
16. Li F, Wang Z, Qu G, et al. Automatic differentiation of glaucoma visual field from non-glaucoma visual field using deep convolutional neural network. BMC Med Imaging. 2018;18(1):35.
17. Yousefi S, Kiwaki T, Zheng Y et al. Detection of longitudinal visual field progression in glaucoma using machine learning. Am J Ophth. 2018;193:71-79.
18. Dixit A, Yohannan J, and Boland MV. Assessing glaucoma progression using machine learning trained on longitudinal visual field and clinical data. Ophthalmology. 2021:128(7):1016-26.
19. Oh E, Yoo TK, Hong S. Artificial neural network approach for differentiating open-angle glaucoma from glaucoma suspect without a visual field test. Invest Ophthalmol Vis Sci. 2015:56(6):3 957-66.
20. Fu H, Baskaran M, Xu Y et al. A deep learning system for automated Angle-Closure detection in anterior segment optical coherence tomography images. Am J Ophth. 2019;193:71-79.
21. Fu H, Xu Y, Lin S, et al. Segmentation and quantification for angle-closure glaucoma assessment in anterior segment OCT. IEEE Trans Med Imaging. 2017;36(9):1930-1938.
22. Niwas SI, Lin W, Bai X, et al. Automated anterior segment OCT image for angle closure glaucoma mechanisms classification. Computer Methods and Programs in Biomedicine. 2016;130: 65-75.
23. Kamthan G, Meenink T, Morgan IC, et al. Microinterventional system for robot-assisted gonioscopic surgery-technical feasibility and preclinical evaluation in synthetic eye modes. BMC Ophthalmol. 2024;24:324.
24. Dolinina O, Kamenskikh T, Veselova E, et al. Using of Artificial Intelligence in Detection of the Compliance of the Ophthalmic Patients. In: Dolinina, O., et al. Artificial Intelligence in Models, Methods and Applications. 2023 AIES 2022. Studies in Systems, Decision and Control, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-22938-1_40
25. Mas TV, MacGregor C, Jayaswal R, et al. A review of keratoconus: diagnosis, pathophysiology, and genetics. Surv Ophthalmol. 2017;62(6):770–783.
26. Al-Timemy AH, Ghaeb NH, and Escudero J. Deep transfer learning for improved detection of ketatoconus using corneal topographic maps. Cognitive Computation. 2021. https://doi.org/10.1007/s12559-021-09880-3
27. Yousefi S, Al-Timemy A, Mosa ZM, Abdelmotaal H, Lavric A. A deep feature fusion of improved suspected keratoconus detection with deep learning. Diagnostics. 2023;13(10):1689.
28. Moshirfar M, Tukan AN, Bundogji N, et al. Ectasis after corneal refractive surgery: A systematic review. 2021;10(4):753-776.
29. Haque AEU, Rabbany G, Siam M. Detection of keratoconus diseases using deep learning. arXiv. 2023;2311.01996v1.
30. Vandevenne MM, Favuzza E, Veta M, et al. Artificial intelligence for detecting keratoconus. Cochrane Database Syst Rev. 2023;11(11):1-4.
31. Bodmer NS, Christensen DG, Bachmann LM, et al. Deep learning models used in the diagnostic workup of keratoconus: a systematic review and exploratory meta-analysis. Cornea. 2024;43(7):916-931.
32. Al-Timemy AH, Alzubaidi L, Mosa ZM, et al. A deep feature fusion of improved suspected keratoconus detection with deep learning. Diagnostics. 2023;13:1689-702.
33. Goodman D and Zhu AY. Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review. Frontiers in Ophthalm. 2024;4:1380701.
34. Afifah A, Syarfira F, Afladhanti PM and Dharmawidiarini D. Artificial intelligence as diagnostic modality for keratoconus: A systematic review and meta-analysis. J Taibah Univ Med Sci. 2024;19(2);296-303.
35. Al-Timemy AH, Mosa ZM, Alyasseri Z, et al. A hybrid deep learning construct for detecting keratoconus from corneal maps. Transl Vis Sci Technol. 2021;10(14):16.
36. Kindu G, Shetty N, Shetty R, et al. Artificial intelligence-based stratification of demographic, ocular surface high-risk factors in progression of keratoconus. Indian J Ophthalmol. 2023;71:1882-88.
37. Jimenez-Garcia M, Issarti I, Kreps EO, et al. Forecasting progressive trends in keratoconus by means of a time delay network. 2021;10(15):3238.
38. Gu H, Guo Y, Gu L, et al. Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs. Scientific Reports. 2020;10: 17851.
39. Hu S, Sun Y, Li J, et al. Automatic diagnosis of infectious kertatitis based on slit lamp images analysis. J Pers Med. 2023;13(3):519.
40. Lv J, Zhang K, Chen Q, et al. Deep learning-based automated diagnosis of fungal keratitis with in vivo confocal microscopy images. Annals of Translational Med. 2020;8(11):706.
41. Jiang J, Liu W, Pei M, et al. Automatic diagnosis of keratitis using object localization combined with cost-sensitive deep attention convolutional neural network. Journal of Big Data. 2023;10:121.
42. Sarayar R, Lestari YD, and Setio AAA. Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysis. Front Public Health. 2023;11:1239231.
43. Kuo MT, Wie-Yun B, Yu-Kai Y, et al. A deep learning approach in diagnosing fungal keratitis based on corneal photographs. Scientific Reports. 2020;10:14424.
44. Hung N, Shih AKY, Lin C, et al. Using slit-lamp images for deep learning-based identification of bacterial and fungal keratitis: model development and validation with different convolutional neural networks. Diagnostics. 2021;11(7):1246.
45. Ghosh AK, Thammasudjarit R, and Jongkhajornpong P. A deep learning for discrimination between fungal keratitis and bacterial keratitis: deepkeratitis. Cornea. 2022;41(5):616.
46. Redd TK, Prajna NV, Srinivasan M, et al. Image-based differentiation of bacterial and fungal keratitis using deep convolutional neural networks. Ophthalmol Sci. 2022;2(2):100119.
47. Lincke A, Roth J, Macedo AF, et al. AI-Based Decision-Support System for Diagnosing Acanthamoeba Keratitis Using In Vivo Confocal Microscopy Images. Transl Vis Sci Technol. 2023; 12(11):29.
48. Cicinelli MV, Buchan JC, Nicholson M, et al. Cataracts. The Lancet. 2023;401(10374:377-89.
49. Li H, Lim JH, Liu J, et al. An automatic diagnosis system of nuclear cataract using slit-lamp images. Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3693–3696.
50. Xu Y., Gao X., Lin S., et al. Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression. Med Image Comput Assist Interv. 2013;16:468–475.
51. Ladas JG, Siddiqui AA, Devgan U, and Jun AS. A 3-D “super surface” combining modern intraocular lens formulas to generate a “super formula” and maximize accuracy. JAMA Ophthalmol. 2015; 133(12):1431–1436.
52. Melles R.B., Kane J.X., Olsen T., Chang W.J. Update on intraocular lens calculation formulas. Ophthalmology. 2019;126(9):1334–1335.
53. Carmona González D., Palomino Bautista C. Accuracy of a new intraocular lens power calculation method based on artificial intelligence. Eye (Lond) 2021;35(2):517–522.
54. Grammatikopoulou M, Flouty E, Kadkhodamohammadi A, et al. CaDIS: cataract dataset for surgical RGB-image segmentation. Med Image Anal. 2021;71:102053.
55. Smith P, Tang L, Balntas V, et al. “PhacoTracking”: an evolving paradigm in ophthalmic surgical training. JAMA Ophthalmol. 2013;131(5):659–661.
56. Mamtora S., Jones R., Rabiolo A., et al. Remote supervision for simulated cataract surgery. Eye (Lond). 2021:1–2.
57. Lindegger DJ, Wawrzynski J, Saleh GM. Evolution and Applications of Artificial Intelligence to Cataract Surgery. Ophthalmol Sci. 2022;2(3):10 0164.
58. Meinert E, Milne-Ives M, Lim E, et al. Accuracy and safety of an autonomous artificial intelligence clinical assistant conducting telemedicine follow-up assessment for cataract surgery. eClinicalMedicine. 2024;73:102692.
59. Davenport TH, Glaser JP. Factors governing the adoption of artificial intelligence in healthcare providers. Discov Health Syst. 2022;1(1):4.