Офтальмохирургия. 2022; : 77-96
Применение искусственного интеллекта в диагностике и хирургии кератоконуса: систематический обзор
https://doi.org/10.25276/0235-4160-2022-1-77-96Аннотация
Актуальность. Искусственный интеллект – это новые теоретические подходы, методы, технологии и прикладные системы для моделирования и расширения человеческого интеллекта. В офтальмологии искусственный интеллект является одним из инструментов, способствующих повышению эффективности процесса лечения за счет более точной диагностики, поиска новых биомаркеров заболеваний, автоматизации процессов принятия решений и помощи в других аспектах повседневной деятельности врача.
Цель. Описание имеющихся на сегодняшний день разработок в области искусственного интеллекта применительно к процессу диагностики и хирургии кератоконуса.
Материал и методы. Базы данных, которые использовали для поиска литературы включали: Google и Google Scholar, PubMed, Embase, MEDLINE и Web of Science.
Результаты. В результате поиска по всем выбранным базам данных, а также отбора релевантных исследований было проанализировано 75 статей. Среди исследований, которые были выбраны для полнотекстового анализа, большая часть представляла собой разработку алгоритмов диагностики. Наиболее часто встречающимися методами машинного обучения являлись метод опорных векторов, метод случайного леса и конволюционная нейронная сеть. В 4 исследованиях из 75 сообщалось о создании графического интерфейса для применения разработанного алгоритма в клинической среде.
Заключение. Точность алгоритмов, которые были получены в анализируемых работах, составила в основном более 90%, что говорит о возможности моделей машинного обучения решать сложные клинические задачи.
Список литературы
1. Abdelmotaal H, Mostafa MM, Mostafa ANR, Mohamed AA, Abdelazeem K. Classification of color-coded scheimpflug camera corneal tomography images using deep learning. Transl Vis Sci Technol. 2020;9: 30. doi: 10.1167/tvst.9.13.30
2. Gatinel D. Screening for subclinical keratoconus and prevention of corneal ectasia with SCORE analyzer software. In: Febbraro J-L, Khan HN, Koch DD, editors. Surg. Correct. Astigmatism, Cham: Springer International Publishing; 2018. doi: 10.1007/978-3-319-56565-1_9
3. Ruiz Hidalgo I, Rozema JJ, Saad A, Gatinel D, Rodriguez P, Zakaria N, et al. Validation of an objective keratoconus detection system implemented in a scheimpflug tomographer and comparison with other methods. Cornea. 2017;36: 689–695. doi: 10.1097/ICO.0000000000001194
4. Takahashi H, Tampo H, Arai Y, Inoue Y, Kawashima H. Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy. PLOS ONE. 2017;12: e0179790. doi: 10.1371/journal.pone.0179790
5. Schmidt-Erfurth U, Bogunovic H, Sadeghipour A, Schlegl T, Langs G, Gerendas BS, et al. Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration. Ophthalmol Retina. 2018;2: 24–30. doi: 10.1016/j.oret.2017.03.015
6. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Int J Surg. 2021;88: 105906. doi: 10.1016/j.ijsu.2021.105906.
7. Cao K, Verspoor K, Sahebjada S, Baird PN. Evaluating the performance of various machine learning algorithms to detect subclinical keratoconus. Transl Vis Sci Technol. 2020;9: 24. doi: 10.1167/tvst.9.2.24
8. Cao K, Verspoor K, Chan E, Daniell M, Sahebjada S, Baird PN. Machine learning with a reduced dimensionality representation of comprehensive Pentacam tomography parameters to identify subclinical keratoconus. Comput Biol Med 2021;138: 104884. doi: 10.1016/j.compbiomed.2021.104884
9. de Almeida Jr GC, Guido RC, Neto JS, Rosa JM, Castiglioni L, de Mattos LC, et al. Corneal tomography multivariate index (CTMVI) effectively distinguishes healthy corneas from those susceptible to ectasia. Biomed Signal Process Control. 2021;70: 102995. doi: 10.1016/j.bspc.2021.102995
10. Ghaderi M, Sharifi A, Jafarzadeh Pour E. Proposing an ensemble learning model based on neural network and fuzzy system for keratoconus diagnosis based on Pentacam measurements. Int Ophthalmol. 2021;41: 3935–3948. doi: 10.1007/s10792-021-01963-2.
11. Hallett N, Yi K, Dick J, Hodge C, Sutton G, Wang YG, et al. Deep learning based unsupervised and semi-supervised classification for keratoconus. ArXiv200111653 Cs Eess Stat. 2020.
12. Ruiz Hidalgo I, Rodriguez P, Rozema JJ, Ní Dhubhghaill S, Zakaria N, Tassignon M-J, et al. Evaluation of a machine-learning classifier for keratoconus detection based on scheimpflug tomography. Cornea. 2016;35: 827–832. doi: 10.1097/ICO.0000000000000834
13. Issarti I, Consejo A, Jiménez-García M, Hershko S, Koppen C, Rozema JJ. Computer aided diagnosis for suspect keratoconus detection. Comput Biol Med. 2019;109: 33–42. doi: 10.1016/j.compbiomed.2019.04.024
14. Jiménez-García M, Issarti I, Kreps E, Ní Dhubhghaill S, Koppen C, Varssano D, et al. Forecasting progressive trends in keratoconus by means of a time delay neural network. J Clin Med. 2021;10: 3238. doi: 10.3390/jcm10153238
15. Kovács I, Miháltz K, Kránitz K, Juhász É, Takács Á, Dienes L, et al. Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus. J Cataract Refract Surg. 2016;42: 275–283. doi: 10.1016/j.jcrs.2015.09.020
16. Lavric A, Anchidin L, Popa V, Al-Timemy AH, Alyasseri Z, Takahashi H, et al. Keratoconus severity detection from elevation, topography and pachymetry raw data using a machine learning approach. IEEE Access. 2021;9: 84344–84355. doi: 10.1109/ACCESS.2021.3086021
17. Lopes BT, Ramos I de C, Salomão MQ, Canedo ALC, Ambrósio Jr. R. Horizontal pachymetric profile for the detection of keratoconus. Rev Bras Oftalmol. 2015;74: 382–385. doi: 10.5935/0034-7280.20150080
18. Lopes BT, Ramos IC, Salomão MQ, Guerra FP, Schallhorn SC, Schallhorn JM, et al. Enhanced tomographic assessment to detect corneal ectasia based on artificial intelligence. Am J Ophthalmol. 2018;195: 223–232. doi: 10.1016/j.ajo.2018.08.005.
19. Malyugin B, Sakhnov S, Izmailova S, Boiko E, Pozdeyeva N, Axenova L, et al. Keratoconus diagnostic and treatment algorithms based on machine-learning methods. diagnostics. 2021;11: 1933. doi: 10.3390/diagnostics11101933
20. Shetty R, Kundu G, Narasimhan R, Khamar P, Gupta K, Singh N, et al. Artificial intelligence efficiently identifies regional differences in the progression of tomographic parameters of keratoconic corneas. J Refract Surg. 2021;37: 240–248. doi: 10.3928/1081597X-20210120-01
21. Shi C, Wang M, Zhu T, Zhang Y, Ye Y, Jiang J, et al. Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities. Eye Vis. 2020;7: 48. doi: 10.1186/s40662-020-00213-3
22. Abdelmotaal H, Abdou AA, Omar AF, El-Sebaity DM, Abdelazeem K. Pix2pix conditional generative adversarial networks for scheimpflug camera color-coded corneal tomography image generation. Transl Vis Sci Technol. 2021;10: 21. doi: org/10.1167/tvst.10.7.21
23. Chen X, Zhao J, Iselin KC, Borroni D, Romano D, Gokul A, et al. Keratoconus detection of changes using deep learning of colour-coded maps. BMJ Open Ophthalmol. 2021;6: e000824. doi: 10.1136/bmjophth-2021-000824
24. Feng R, Xu Z, Zheng X, Hu H, Jin X, Chen DZ, et al. KerNet: A novel deep learning approach for keratoconus and sub-clinical keratoconus detection based on raw data of the pentacam HR system. IEEE J Biomed Health Inform. 2021;25: 3898–3910. doi: 10.1109/JBHI.2021.3079430
25. Issarti I, Consejo A, Jiménez-García M, Kreps EO, Koppen C, Rozema JJ. Logistic index for keratoconus detection and severity scoring (Logik). Comput Biol Med. 2020;122: 103809. doi: 10.1016/j.compbiomed.2020.103809
26. Gao Y, Wu Q, Li J, Sun J, Wan W. SVM-based automatic diagnosis method for keratoconus. In: Jiang X, Arai M, Chen G (eds.). Singapore, Singapore; 2017. doi: 10.1117/12.2280344
27. Feizi S, Yaseri M, Kheiri B. Predictive ability of galilei to distinguish subclinical keratoconus and keratoconus from normal corneas. J Ophthalmic Vis Res. 2016;11: 8. doi: 10.4103/2008-322X.180707
28. Smadja D, Touboul D, Cohen A, Doveh E, Santhiago MR, Mello GR, et al. Detection of subclinical keratoconus using an automated decision tree classification. Am J Ophthalmol. 2013;156: 237–246.e1. https://doi.org/10.1016/j.ajo.2013.03.034
29. Bevilacqua V, Simeone S, Brunetti A, Loconsole C, Trotta GF, Tramacere S, et al. A computer aided ophthalmic diagnosis system based on tomographic features. In: Huang D-S, Hussain A, Han K, Gromiha MM (eds.). Intelligent Computing Methodologies. Vol. 10363. Cham: Springer International Publishing; 2017. P. 598–609. doi: 10.1007/978-3-319-63315-2_52
30. Altinkurt E, Avci O, Muftuoglu O, Ugurlu A, Cebeci Z, Ozbilen KT. Logistic regression model using scheimpflug-placido cornea topographer parameters to diagnose keratoconus. J Ophthalmol. 2021;2021: 1–7. doi: 10.1155/2021/5528927
31. Arbelaez MC, Versaci F, Vestri G, Barboni P, Savini G. Use of a support vector machine for keratoconus and subclinical keratoconus detection by topographic and tomographic data. Ophthalmology. 2012;119: 2231–2238. doi: 10.1016/j.ophtha.2012.06.005
32. Bolarín JM, Cavas F, Velázquez JS, Alió JL. A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI development. Appl Sci. 2020;10: 1874. doi: 10.3390/app10051874
33. Castro-Luna GM, Martínez-Finkelshtein A, Ramos-López D. Robust keratoconus detection with Bayesian network classifier for Placido-based corneal indices. Contact Lens Anterior Eye. 2020;43: 366–372. doi: 10.1016/j.clae.2019.12.006
34. Velázquez-Blázquez JS, Bolarín JM, Cavas-Martínez F, Alió JL. EMKLAS: a new automatic scoring system for early and mild keratoconus detection. Transl Vis Sci Technol. 2020;9: 30. doi: 10.1167/tvst.9.2.30
35. Souza MB, Medeiros FW de, Souza DB, Alves MR. Diagnóstico do ceratocone baseado no Orbscan com o auxílio de uma rede neural. Arq Bras Oftalmol. 2008;71: 65–68. doi: 10.1590/S0004-27492008000700013
36. Souza MB, Medeiros FW, Souza DB, Garcia R, Alves MR. Evaluation of machine learning classifiers in keratoconus detection from orbscan II examinations. Clinics. 2010;65: 1223–1228. doi: 10.1590/S1807-59322010001200002
37. Zéboulon P, Debellemanière G, Bouvet M, Gatinel D. Corneal topography raw data classification using a convolutional neural network. Am J Ophthalmol. 2020;219: 33–39. doi: 10.1016/j.ajo.2020.06.005
38. Zéboulon P, Debellemanière G, Gatinel D. Unsupervised learning for largescale corneal topography clustering. Sci Rep 2020;10: 16973. doi: 10.1038/s41598-020-73902-7.
39. Kamiya K, Ayatsuka Y, Kato Y, Shoji N, Mori Y, Miyata K. Diagnosability of keratoconus using deep learning with Placido disk-based corneal topography. Front Med. 2021;8: 724902. doi:10.3389/fmed.2021.724902
40. Kuo B-I, Chang W-Y, Liao T-S, Liu F-Y, Liu H-Y, Chu H-S, et al. Keratoconus screening based on deep learning approach of corneal topography. Transl Vis Sci Technol. 2020;9: 53. doi: 10.1167/tvst.9.2.53
41. Maeda N, Klyce SD, Smolek MK, Thompson HW. Automated keratoconus screening with corneal topography analysis. Invest Ophthalmol Vis Sci. 1994;35: 2749-2757.
42. Maeda N, Klyce SD, Smolek MK. Neural network classification of corneal topography. Preliminary demonstration. Invest Ophthalmol Vis Sci. 1995;36: 1327–1335.
43. Smolek MK, Klyce SD. Current keratoconus detection methods compared with a neural network approach. Invest Ophthalmol Vis Sci. 1997;38: 2290–2299.
44. Accardo PA, Pensiero S. Neural network-based system for early keratoconus detection from corneal topography. J Biomed Inform. 2002;35: 151–159. doi: 10.1016/S1532-0464(02)00513-0
45. Elsawy A, Abdel-Mottaleb M. A novel network with parallel resolution encoders for the diagnosis of corneal diseases. IEEE Trans Biomed Eng. 2021;68: 3671–3680. doi: 10.1109/TBME.2021.3082152
46. Elsawy A, Eleiwa T, Chase C, Ozcan E, Tolba M, Feuer W, et al. Multidisease deep learning neural network for the diagnosis of corneal diseases. Am J Ophthalmol. 2021;226: 252–261. doi: 10.1016/j.ajo.2021.01.018.
47. Santos VA dos, Schmetterer L, Stegmann H, Pfister M, Messner A, Schmidinger G, et al. CorneaNet: fast segmentation of cornea OCT scans of healthy and keratoconic eyes using deep learning. Biomed Opt Express. 2019;10:622. doi: 10.1364/BOE.10.000622
48. Elsawy A, Abdel-Mottaleb M, Abou Shousha M. Segmentation of corneal optical coherence tomography images using randomized Hough transform. In: Angelini ED, Landman BA (eds.). Med. Imaging 2019, Image Process., San Diego. United States: SPIE; 2019. P. 29. doi: 10.1117/12.2512865
49. Mahmoud HAH, Mengash HA. Automated keratoconus detection by 3D corneal images reconstruction sensors. 2021;21: 2326. doi: 10.3390/s21072326
50. Hayashi T, Masumoto H, Tabuchi H, Ishitobi N, Tanabe M, Grün M, et al. A deep learning approach for successful big-bubble formation prediction in deep anterior lamellar keratoplasty. Sci Rep. 2021;11: 18559. doi: 10.1038/s41598-021-98157-8
51. Kamiya K, Ayatsuka Y, Kato Y, Fujimura F, Takahashi M, Shoji N, et al. Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study. BMJ Open. 2019;9: e031313. doi: 10.1136/bmjopen-2019-031313
52. Kamiya K, Ayatsuka Y, Kato Y, Shoji N, Miyai T, Ishii H, et al. Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps. Ann Transl Med. 2021;9: 1287–1287. doi: 10.21037/atm-21-1772
53. Kosaki R, Maeda N, Bessho K, Hori Y, Nishida K, Suzaki A, et al. Magnitude and orientation of Zernike terms in patients with keratoconus. Investig Opthalmology Vis Sci. 2007;48: 3062. doi: 10.1167/iovs.06-1285
54. Saika M, Maeda N, Hirohara Y, Mihashi T, Fujikado T, Nishida K. Four discriminant models for detecting keratoconus pattern using Zernike coefficients of corneal aberrations. Jpn J Ophthalmol. 2013;57: 503–509. doi: 10.1007/s10384-013-0269-1
55. Lavric A, Popa V, Takahashi H, Yousefi S. Detecting keratoconus from corneal imaging data using machine learning. IEEE Access. 2020;8: 149113–149121. doi: 10.1109/ACCESS.2020.3016060
56. Castro-Luna G, Jiménez-Rodríguez D, Castaño-Fernández AB, Pérez-Rueda A. Diagnosis of subclinical keratoconus based on machine learning techniques. J Clin Med. 2021;10: 4281. doi: 10.3390/jcm10184281
57. Twa MD, Parthasarathy S, Roberts C, Mahmoud AM, Raasch TW, Bullimore MA. Automated decision tree classification of corneal shape. Optom Vis Sci Off Publ Am Acad Optom. 2005;82: 1038–1046. doi: 10.1097/01.opx.0000192350.01045.6f
58. Saad A, Gatinel D. Evaluation of total and corneal wavefront high order aberrations for the detection of Forme Fruste keratoconus. Invest Opthalmol Vis Sci. 2012;53: 2978. doi: 10.1167/iovs.11-8803
59. Carvalho LA. Preliminary results of neural networks and Zernike polynomials for classification of videokeratography maps. Optom Vis Sci. 2005;82: 151–158. doi: 10.1097/01.OPX.0000153193.41554.A1
60. Carvalho LAV de, Barbosa MS. Neural networks and statistical analysis for classification of corneal videokeratography maps based on Zernike coefficients: a quantitative comparison. Arq Bras Oftalmol. 2008;71: 337–341. doi: 10.1590/S0004-27492008000300006
61. Leão E, Ing Ren T, Lyra JM, Machado A, Koprowski R, Lopes B, et al. Corneal deformation amplitude analysis for keratoconus detection through compensation for intraocular pressure and integration with horizontal thickness profile. Comput Biol Med. 2019;109: 263–271. doi: 10.1016/j.compbiomed.2019.04.019
62. Yoo TK, Ryu IH, Lee G, Kim Y, Kim JK, Lee IS, et al. Adopting machine learning to automatically identify candidate patients for corneal refractive surgery. Npj Digit Med. 2019;2: 59. doi: 10.1038/s41746-019-0135-8
63. Yoo TK, Ryu IH, Choi H, Kim JK, Lee IS, Kim JS, et al. Explainable machine learning approach as a tool to understand factors used to select the refractive surgery technique on the expert level. Transl Vis Sci Technol. 2020;9: 8. doi: 10.1167/tvst.9.2.8
64. Chandapura R, Salomão MQ, Ambrósio R, Swarup R, Shetty R, Sinha Roy A. Bowman’s topography for improved detection of early ectasia. J Biophotonics. 2019;12. doi: 10.1002/jbio.201900126
65. Valdés-Mas MA, Martín-Guerrero JD, Rupérez MJ, Pastor F, Dualde C, Monserrat C, et al. A new approach based on Machine Learning for predicting corneal curvature (K1) and astigmatism in patients with keratoconus after intracorneal ring implantation. Comput Methods Programs Biomed. 2014;116: 39–47. doi: 10.1016/j.cmpb.2014.04.003
66. Consejo A, Solarski J, Karnowski K, Rozema JJ, Wojtkowski M, Iskander DR. Keratoconus detection based on a single scheimpflug image. Transl Vis Sci Technol. 2020;9: 36. doi: 10.1167/tvst.9.7.36
67. Herber R, Pillunat LE, Raiskup F. Development of a classification system based on corneal biomechanical properties using artificial intelligence predicting keratoconus severity. Eye Vis. 2021;8: 21. doi: 10.1186/s40662-021-00244-4
68. Zaki WMDW, Daud MM, Saad AH, Hussain A, Mutalib HA. Towards automated keratoconus screening approach using lateral segment photographed images. 2020 IEEE-EMBS Conf. Biomed. Eng. Sci. IECBES, Langkawi Island, Malaysia: IEEE; 2021: 466–471. doi: 10.1109/IECBES48179.2021.9398781.
69. Al-Timemy AH, Ghaeb NH, Mosa ZM, Escudero J. Deep transfer learning for improved detection of keratoconus using corneal topographic maps. Cogn Comput. 2021. doi:10.1007/s12559-021-09880-3
70. Lavric A, Valentin P. KeratoDetect: keratoconus detection algorithm using convolutional neural networks. Comput Intell Neurosci. 2019;2019: 1–9. doi: 10.1155/2019/8162567
71. Salem BR, Solodovnikov VI. Decision support system for an early-stage keratoconus diagnosis. J Phys Conf Ser. 2019;1419: 012023. doi: 10.1088/1742-6596/1419/1/012023
72. Yousefi S, Yousefi E, Takahashi H, Hayashi T, Tampo H, Inoda S, et al. Keratoconus severity identification using unsupervised machine learning. PLOS ONE. 2018;13: e0205998. doi: 10.1371/journal.pone.0205998
73. Yousefi S, Takahashi H, Hayashi T, Tampo H, Inoda S, Arai Y, et al. Predicting the likelihood of need for future keratoplasty intervention using artificial intelligence. Ocul Surf. 2020;18: 320–325. doi: 10.1016/j.jtos.2020.02.008
74. Obaid HS, Dheyab SA, Sabry SS. The impact of data pre-processing techniques and dimensionality reduction on the accuracy of machine learning. 2019 9th Annu. Inf. Technol. Electromechanical Eng. Microelectron. Conf. IEMECON, Jaipur, India: IEEE; 2019: 279–283. doi: 10.1109/IEMECONX.2019.8877011
75. Aatila M, Lachgar M, Hamid H, Kartit A. Keratoconus severity classification using features selection and machine learning algorithms. Comput Math Methods Med. 2021;2021: 1–26. doi: 10.1155/2021/9979560
76. Shanthi S, Nirmaladevi K, Pyingkodi M, Dharanesh K, Gowthaman T, Harsavardan B. Machine learning approach for detection of keratoconus. IOP Conf Ser Mater Sci Eng. 2021;1055: 012112. doi: 10.1088/1757-899X/1055/1/012112
77. Kanimozhi R, Gayathri R. Detection and evaluation of keratoconus (corneal topography) by using the image classifier techniques. Soft Comput. 2021;25: 2531–2543. doi: 10.1007/s00500-020-05255-2
78. Kato N, Masumoto H, Tanabe M, Sakai C, Negishi K, Torii H, et al. Predicting keratoconus progression and need for corneal crosslinking using deep learning. J Clin Med. 2021;10: 844. doi: 10.3390/jcm10040844
79. Ucar M, Sen B, Cakmak HB. A novel classification and estimation approach for detecting keratoconus disease with intelligent systems. 2013 8th Int. Conf. Electr. Electron. Eng. ELECO, Bursa, Turkey: IEEE; 2013: 521–525. doi: 10.1109/ELECO.2013.6713897
80. Shashank V, Priya D. Con-Ker: a convolutional neural network based approach for keratoconus detection and classification. A Journal of University of Shanghai for Science and Technology. 2021;23: 71–81. doi: 10.51201/JUSST/21/06472
81. Xie Y, Zhao L, Yang X, Wu X, Yang Y, Huang X, et al. Screening candidates for refractive surgery with corneal tomographic-based deep learning. JAMA Ophthalmol. 2020;138: 519. doi: 10.1001/jamaophthalmol.2020.0507
Fyodorov Journal of Ophthalmic Surgery. 2022; : 77-96
Application of artificial intelligence in diagnostics and surgery of keratoconus: a systematic overview
https://doi.org/10.25276/0235-4160-2022-1-77-96Abstract
Introduction. Artificial Intelligence is new theoretical approaches, methods, technologies and applied systems for modeling and extending human intelligence. In ophthalmology, artificial intelligence is one of the tools that help improve the efficiency of the treatment process through more accurate diagnostics, search for new biomarkers of diseases, automation of decision-making processes and assistance in other aspects of the physician’s daily activities. The purpose of this review is to describe the currently available developments for the diagnosis and surgery of keratoconus in the field of artificial intelligence.
Material and methods. Databases that were used for literature search included: Google and Google Scholar, PubMed, Embase, MEDLINE and Web of Science.
Results. As a result of a search across all selected databases, as well as a selection of relevant studies, 75 articles were analyzed. Most of the studies that were selected for full-text analysis were the development of diagnostic algorithms. The most common classical machine learning methods were support vector machines method and random forest method. The most commonly used type of neural network is the convolutional neural network. 4 studies out of 75 reported the creation of a graphical interface for using the developed algorithm in a clinical environment.
Conclusion. The accuracy of the algorithms that were obtained in the analyzed researches was basically more than 90%. It indicates the ability of machine learning models to solve complex clinical problems
References
1. Abdelmotaal H, Mostafa MM, Mostafa ANR, Mohamed AA, Abdelazeem K. Classification of color-coded scheimpflug camera corneal tomography images using deep learning. Transl Vis Sci Technol. 2020;9: 30. doi: 10.1167/tvst.9.13.30
2. Gatinel D. Screening for subclinical keratoconus and prevention of corneal ectasia with SCORE analyzer software. In: Febbraro J-L, Khan HN, Koch DD, editors. Surg. Correct. Astigmatism, Cham: Springer International Publishing; 2018. doi: 10.1007/978-3-319-56565-1_9
3. Ruiz Hidalgo I, Rozema JJ, Saad A, Gatinel D, Rodriguez P, Zakaria N, et al. Validation of an objective keratoconus detection system implemented in a scheimpflug tomographer and comparison with other methods. Cornea. 2017;36: 689–695. doi: 10.1097/ICO.0000000000001194
4. Takahashi H, Tampo H, Arai Y, Inoue Y, Kawashima H. Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy. PLOS ONE. 2017;12: e0179790. doi: 10.1371/journal.pone.0179790
5. Schmidt-Erfurth U, Bogunovic H, Sadeghipour A, Schlegl T, Langs G, Gerendas BS, et al. Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration. Ophthalmol Retina. 2018;2: 24–30. doi: 10.1016/j.oret.2017.03.015
6. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Int J Surg. 2021;88: 105906. doi: 10.1016/j.ijsu.2021.105906.
7. Cao K, Verspoor K, Sahebjada S, Baird PN. Evaluating the performance of various machine learning algorithms to detect subclinical keratoconus. Transl Vis Sci Technol. 2020;9: 24. doi: 10.1167/tvst.9.2.24
8. Cao K, Verspoor K, Chan E, Daniell M, Sahebjada S, Baird PN. Machine learning with a reduced dimensionality representation of comprehensive Pentacam tomography parameters to identify subclinical keratoconus. Comput Biol Med 2021;138: 104884. doi: 10.1016/j.compbiomed.2021.104884
9. de Almeida Jr GC, Guido RC, Neto JS, Rosa JM, Castiglioni L, de Mattos LC, et al. Corneal tomography multivariate index (CTMVI) effectively distinguishes healthy corneas from those susceptible to ectasia. Biomed Signal Process Control. 2021;70: 102995. doi: 10.1016/j.bspc.2021.102995
10. Ghaderi M, Sharifi A, Jafarzadeh Pour E. Proposing an ensemble learning model based on neural network and fuzzy system for keratoconus diagnosis based on Pentacam measurements. Int Ophthalmol. 2021;41: 3935–3948. doi: 10.1007/s10792-021-01963-2.
11. Hallett N, Yi K, Dick J, Hodge C, Sutton G, Wang YG, et al. Deep learning based unsupervised and semi-supervised classification for keratoconus. ArXiv200111653 Cs Eess Stat. 2020.
12. Ruiz Hidalgo I, Rodriguez P, Rozema JJ, Ní Dhubhghaill S, Zakaria N, Tassignon M-J, et al. Evaluation of a machine-learning classifier for keratoconus detection based on scheimpflug tomography. Cornea. 2016;35: 827–832. doi: 10.1097/ICO.0000000000000834
13. Issarti I, Consejo A, Jiménez-García M, Hershko S, Koppen C, Rozema JJ. Computer aided diagnosis for suspect keratoconus detection. Comput Biol Med. 2019;109: 33–42. doi: 10.1016/j.compbiomed.2019.04.024
14. Jiménez-García M, Issarti I, Kreps E, Ní Dhubhghaill S, Koppen C, Varssano D, et al. Forecasting progressive trends in keratoconus by means of a time delay neural network. J Clin Med. 2021;10: 3238. doi: 10.3390/jcm10153238
15. Kovács I, Miháltz K, Kránitz K, Juhász É, Takács Á, Dienes L, et al. Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus. J Cataract Refract Surg. 2016;42: 275–283. doi: 10.1016/j.jcrs.2015.09.020
16. Lavric A, Anchidin L, Popa V, Al-Timemy AH, Alyasseri Z, Takahashi H, et al. Keratoconus severity detection from elevation, topography and pachymetry raw data using a machine learning approach. IEEE Access. 2021;9: 84344–84355. doi: 10.1109/ACCESS.2021.3086021
17. Lopes BT, Ramos I de C, Salomão MQ, Canedo ALC, Ambrósio Jr. R. Horizontal pachymetric profile for the detection of keratoconus. Rev Bras Oftalmol. 2015;74: 382–385. doi: 10.5935/0034-7280.20150080
18. Lopes BT, Ramos IC, Salomão MQ, Guerra FP, Schallhorn SC, Schallhorn JM, et al. Enhanced tomographic assessment to detect corneal ectasia based on artificial intelligence. Am J Ophthalmol. 2018;195: 223–232. doi: 10.1016/j.ajo.2018.08.005.
19. Malyugin B, Sakhnov S, Izmailova S, Boiko E, Pozdeyeva N, Axenova L, et al. Keratoconus diagnostic and treatment algorithms based on machine-learning methods. diagnostics. 2021;11: 1933. doi: 10.3390/diagnostics11101933
20. Shetty R, Kundu G, Narasimhan R, Khamar P, Gupta K, Singh N, et al. Artificial intelligence efficiently identifies regional differences in the progression of tomographic parameters of keratoconic corneas. J Refract Surg. 2021;37: 240–248. doi: 10.3928/1081597X-20210120-01
21. Shi C, Wang M, Zhu T, Zhang Y, Ye Y, Jiang J, et al. Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities. Eye Vis. 2020;7: 48. doi: 10.1186/s40662-020-00213-3
22. Abdelmotaal H, Abdou AA, Omar AF, El-Sebaity DM, Abdelazeem K. Pix2pix conditional generative adversarial networks for scheimpflug camera color-coded corneal tomography image generation. Transl Vis Sci Technol. 2021;10: 21. doi: org/10.1167/tvst.10.7.21
23. Chen X, Zhao J, Iselin KC, Borroni D, Romano D, Gokul A, et al. Keratoconus detection of changes using deep learning of colour-coded maps. BMJ Open Ophthalmol. 2021;6: e000824. doi: 10.1136/bmjophth-2021-000824
24. Feng R, Xu Z, Zheng X, Hu H, Jin X, Chen DZ, et al. KerNet: A novel deep learning approach for keratoconus and sub-clinical keratoconus detection based on raw data of the pentacam HR system. IEEE J Biomed Health Inform. 2021;25: 3898–3910. doi: 10.1109/JBHI.2021.3079430
25. Issarti I, Consejo A, Jiménez-García M, Kreps EO, Koppen C, Rozema JJ. Logistic index for keratoconus detection and severity scoring (Logik). Comput Biol Med. 2020;122: 103809. doi: 10.1016/j.compbiomed.2020.103809
26. Gao Y, Wu Q, Li J, Sun J, Wan W. SVM-based automatic diagnosis method for keratoconus. In: Jiang X, Arai M, Chen G (eds.). Singapore, Singapore; 2017. doi: 10.1117/12.2280344
27. Feizi S, Yaseri M, Kheiri B. Predictive ability of galilei to distinguish subclinical keratoconus and keratoconus from normal corneas. J Ophthalmic Vis Res. 2016;11: 8. doi: 10.4103/2008-322X.180707
28. Smadja D, Touboul D, Cohen A, Doveh E, Santhiago MR, Mello GR, et al. Detection of subclinical keratoconus using an automated decision tree classification. Am J Ophthalmol. 2013;156: 237–246.e1. https://doi.org/10.1016/j.ajo.2013.03.034
29. Bevilacqua V, Simeone S, Brunetti A, Loconsole C, Trotta GF, Tramacere S, et al. A computer aided ophthalmic diagnosis system based on tomographic features. In: Huang D-S, Hussain A, Han K, Gromiha MM (eds.). Intelligent Computing Methodologies. Vol. 10363. Cham: Springer International Publishing; 2017. P. 598–609. doi: 10.1007/978-3-319-63315-2_52
30. Altinkurt E, Avci O, Muftuoglu O, Ugurlu A, Cebeci Z, Ozbilen KT. Logistic regression model using scheimpflug-placido cornea topographer parameters to diagnose keratoconus. J Ophthalmol. 2021;2021: 1–7. doi: 10.1155/2021/5528927
31. Arbelaez MC, Versaci F, Vestri G, Barboni P, Savini G. Use of a support vector machine for keratoconus and subclinical keratoconus detection by topographic and tomographic data. Ophthalmology. 2012;119: 2231–2238. doi: 10.1016/j.ophtha.2012.06.005
32. Bolarín JM, Cavas F, Velázquez JS, Alió JL. A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI development. Appl Sci. 2020;10: 1874. doi: 10.3390/app10051874
33. Castro-Luna GM, Martínez-Finkelshtein A, Ramos-López D. Robust keratoconus detection with Bayesian network classifier for Placido-based corneal indices. Contact Lens Anterior Eye. 2020;43: 366–372. doi: 10.1016/j.clae.2019.12.006
34. Velázquez-Blázquez JS, Bolarín JM, Cavas-Martínez F, Alió JL. EMKLAS: a new automatic scoring system for early and mild keratoconus detection. Transl Vis Sci Technol. 2020;9: 30. doi: 10.1167/tvst.9.2.30
35. Souza MB, Medeiros FW de, Souza DB, Alves MR. Diagnóstico do ceratocone baseado no Orbscan com o auxílio de uma rede neural. Arq Bras Oftalmol. 2008;71: 65–68. doi: 10.1590/S0004-27492008000700013
36. Souza MB, Medeiros FW, Souza DB, Garcia R, Alves MR. Evaluation of machine learning classifiers in keratoconus detection from orbscan II examinations. Clinics. 2010;65: 1223–1228. doi: 10.1590/S1807-59322010001200002
37. Zéboulon P, Debellemanière G, Bouvet M, Gatinel D. Corneal topography raw data classification using a convolutional neural network. Am J Ophthalmol. 2020;219: 33–39. doi: 10.1016/j.ajo.2020.06.005
38. Zéboulon P, Debellemanière G, Gatinel D. Unsupervised learning for largescale corneal topography clustering. Sci Rep 2020;10: 16973. doi: 10.1038/s41598-020-73902-7.
39. Kamiya K, Ayatsuka Y, Kato Y, Shoji N, Mori Y, Miyata K. Diagnosability of keratoconus using deep learning with Placido disk-based corneal topography. Front Med. 2021;8: 724902. doi:10.3389/fmed.2021.724902
40. Kuo B-I, Chang W-Y, Liao T-S, Liu F-Y, Liu H-Y, Chu H-S, et al. Keratoconus screening based on deep learning approach of corneal topography. Transl Vis Sci Technol. 2020;9: 53. doi: 10.1167/tvst.9.2.53
41. Maeda N, Klyce SD, Smolek MK, Thompson HW. Automated keratoconus screening with corneal topography analysis. Invest Ophthalmol Vis Sci. 1994;35: 2749-2757.
42. Maeda N, Klyce SD, Smolek MK. Neural network classification of corneal topography. Preliminary demonstration. Invest Ophthalmol Vis Sci. 1995;36: 1327–1335.
43. Smolek MK, Klyce SD. Current keratoconus detection methods compared with a neural network approach. Invest Ophthalmol Vis Sci. 1997;38: 2290–2299.
44. Accardo PA, Pensiero S. Neural network-based system for early keratoconus detection from corneal topography. J Biomed Inform. 2002;35: 151–159. doi: 10.1016/S1532-0464(02)00513-0
45. Elsawy A, Abdel-Mottaleb M. A novel network with parallel resolution encoders for the diagnosis of corneal diseases. IEEE Trans Biomed Eng. 2021;68: 3671–3680. doi: 10.1109/TBME.2021.3082152
46. Elsawy A, Eleiwa T, Chase C, Ozcan E, Tolba M, Feuer W, et al. Multidisease deep learning neural network for the diagnosis of corneal diseases. Am J Ophthalmol. 2021;226: 252–261. doi: 10.1016/j.ajo.2021.01.018.
47. Santos VA dos, Schmetterer L, Stegmann H, Pfister M, Messner A, Schmidinger G, et al. CorneaNet: fast segmentation of cornea OCT scans of healthy and keratoconic eyes using deep learning. Biomed Opt Express. 2019;10:622. doi: 10.1364/BOE.10.000622
48. Elsawy A, Abdel-Mottaleb M, Abou Shousha M. Segmentation of corneal optical coherence tomography images using randomized Hough transform. In: Angelini ED, Landman BA (eds.). Med. Imaging 2019, Image Process., San Diego. United States: SPIE; 2019. P. 29. doi: 10.1117/12.2512865
49. Mahmoud HAH, Mengash HA. Automated keratoconus detection by 3D corneal images reconstruction sensors. 2021;21: 2326. doi: 10.3390/s21072326
50. Hayashi T, Masumoto H, Tabuchi H, Ishitobi N, Tanabe M, Grün M, et al. A deep learning approach for successful big-bubble formation prediction in deep anterior lamellar keratoplasty. Sci Rep. 2021;11: 18559. doi: 10.1038/s41598-021-98157-8
51. Kamiya K, Ayatsuka Y, Kato Y, Fujimura F, Takahashi M, Shoji N, et al. Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study. BMJ Open. 2019;9: e031313. doi: 10.1136/bmjopen-2019-031313
52. Kamiya K, Ayatsuka Y, Kato Y, Shoji N, Miyai T, Ishii H, et al. Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps. Ann Transl Med. 2021;9: 1287–1287. doi: 10.21037/atm-21-1772
53. Kosaki R, Maeda N, Bessho K, Hori Y, Nishida K, Suzaki A, et al. Magnitude and orientation of Zernike terms in patients with keratoconus. Investig Opthalmology Vis Sci. 2007;48: 3062. doi: 10.1167/iovs.06-1285
54. Saika M, Maeda N, Hirohara Y, Mihashi T, Fujikado T, Nishida K. Four discriminant models for detecting keratoconus pattern using Zernike coefficients of corneal aberrations. Jpn J Ophthalmol. 2013;57: 503–509. doi: 10.1007/s10384-013-0269-1
55. Lavric A, Popa V, Takahashi H, Yousefi S. Detecting keratoconus from corneal imaging data using machine learning. IEEE Access. 2020;8: 149113–149121. doi: 10.1109/ACCESS.2020.3016060
56. Castro-Luna G, Jiménez-Rodríguez D, Castaño-Fernández AB, Pérez-Rueda A. Diagnosis of subclinical keratoconus based on machine learning techniques. J Clin Med. 2021;10: 4281. doi: 10.3390/jcm10184281
57. Twa MD, Parthasarathy S, Roberts C, Mahmoud AM, Raasch TW, Bullimore MA. Automated decision tree classification of corneal shape. Optom Vis Sci Off Publ Am Acad Optom. 2005;82: 1038–1046. doi: 10.1097/01.opx.0000192350.01045.6f
58. Saad A, Gatinel D. Evaluation of total and corneal wavefront high order aberrations for the detection of Forme Fruste keratoconus. Invest Opthalmol Vis Sci. 2012;53: 2978. doi: 10.1167/iovs.11-8803
59. Carvalho LA. Preliminary results of neural networks and Zernike polynomials for classification of videokeratography maps. Optom Vis Sci. 2005;82: 151–158. doi: 10.1097/01.OPX.0000153193.41554.A1
60. Carvalho LAV de, Barbosa MS. Neural networks and statistical analysis for classification of corneal videokeratography maps based on Zernike coefficients: a quantitative comparison. Arq Bras Oftalmol. 2008;71: 337–341. doi: 10.1590/S0004-27492008000300006
61. Leão E, Ing Ren T, Lyra JM, Machado A, Koprowski R, Lopes B, et al. Corneal deformation amplitude analysis for keratoconus detection through compensation for intraocular pressure and integration with horizontal thickness profile. Comput Biol Med. 2019;109: 263–271. doi: 10.1016/j.compbiomed.2019.04.019
62. Yoo TK, Ryu IH, Lee G, Kim Y, Kim JK, Lee IS, et al. Adopting machine learning to automatically identify candidate patients for corneal refractive surgery. Npj Digit Med. 2019;2: 59. doi: 10.1038/s41746-019-0135-8
63. Yoo TK, Ryu IH, Choi H, Kim JK, Lee IS, Kim JS, et al. Explainable machine learning approach as a tool to understand factors used to select the refractive surgery technique on the expert level. Transl Vis Sci Technol. 2020;9: 8. doi: 10.1167/tvst.9.2.8
64. Chandapura R, Salomão MQ, Ambrósio R, Swarup R, Shetty R, Sinha Roy A. Bowman’s topography for improved detection of early ectasia. J Biophotonics. 2019;12. doi: 10.1002/jbio.201900126
65. Valdés-Mas MA, Martín-Guerrero JD, Rupérez MJ, Pastor F, Dualde C, Monserrat C, et al. A new approach based on Machine Learning for predicting corneal curvature (K1) and astigmatism in patients with keratoconus after intracorneal ring implantation. Comput Methods Programs Biomed. 2014;116: 39–47. doi: 10.1016/j.cmpb.2014.04.003
66. Consejo A, Solarski J, Karnowski K, Rozema JJ, Wojtkowski M, Iskander DR. Keratoconus detection based on a single scheimpflug image. Transl Vis Sci Technol. 2020;9: 36. doi: 10.1167/tvst.9.7.36
67. Herber R, Pillunat LE, Raiskup F. Development of a classification system based on corneal biomechanical properties using artificial intelligence predicting keratoconus severity. Eye Vis. 2021;8: 21. doi: 10.1186/s40662-021-00244-4
68. Zaki WMDW, Daud MM, Saad AH, Hussain A, Mutalib HA. Towards automated keratoconus screening approach using lateral segment photographed images. 2020 IEEE-EMBS Conf. Biomed. Eng. Sci. IECBES, Langkawi Island, Malaysia: IEEE; 2021: 466–471. doi: 10.1109/IECBES48179.2021.9398781.
69. Al-Timemy AH, Ghaeb NH, Mosa ZM, Escudero J. Deep transfer learning for improved detection of keratoconus using corneal topographic maps. Cogn Comput. 2021. doi:10.1007/s12559-021-09880-3
70. Lavric A, Valentin P. KeratoDetect: keratoconus detection algorithm using convolutional neural networks. Comput Intell Neurosci. 2019;2019: 1–9. doi: 10.1155/2019/8162567
71. Salem BR, Solodovnikov VI. Decision support system for an early-stage keratoconus diagnosis. J Phys Conf Ser. 2019;1419: 012023. doi: 10.1088/1742-6596/1419/1/012023
72. Yousefi S, Yousefi E, Takahashi H, Hayashi T, Tampo H, Inoda S, et al. Keratoconus severity identification using unsupervised machine learning. PLOS ONE. 2018;13: e0205998. doi: 10.1371/journal.pone.0205998
73. Yousefi S, Takahashi H, Hayashi T, Tampo H, Inoda S, Arai Y, et al. Predicting the likelihood of need for future keratoplasty intervention using artificial intelligence. Ocul Surf. 2020;18: 320–325. doi: 10.1016/j.jtos.2020.02.008
74. Obaid HS, Dheyab SA, Sabry SS. The impact of data pre-processing techniques and dimensionality reduction on the accuracy of machine learning. 2019 9th Annu. Inf. Technol. Electromechanical Eng. Microelectron. Conf. IEMECON, Jaipur, India: IEEE; 2019: 279–283. doi: 10.1109/IEMECONX.2019.8877011
75. Aatila M, Lachgar M, Hamid H, Kartit A. Keratoconus severity classification using features selection and machine learning algorithms. Comput Math Methods Med. 2021;2021: 1–26. doi: 10.1155/2021/9979560
76. Shanthi S, Nirmaladevi K, Pyingkodi M, Dharanesh K, Gowthaman T, Harsavardan B. Machine learning approach for detection of keratoconus. IOP Conf Ser Mater Sci Eng. 2021;1055: 012112. doi: 10.1088/1757-899X/1055/1/012112
77. Kanimozhi R, Gayathri R. Detection and evaluation of keratoconus (corneal topography) by using the image classifier techniques. Soft Comput. 2021;25: 2531–2543. doi: 10.1007/s00500-020-05255-2
78. Kato N, Masumoto H, Tanabe M, Sakai C, Negishi K, Torii H, et al. Predicting keratoconus progression and need for corneal crosslinking using deep learning. J Clin Med. 2021;10: 844. doi: 10.3390/jcm10040844
79. Ucar M, Sen B, Cakmak HB. A novel classification and estimation approach for detecting keratoconus disease with intelligent systems. 2013 8th Int. Conf. Electr. Electron. Eng. ELECO, Bursa, Turkey: IEEE; 2013: 521–525. doi: 10.1109/ELECO.2013.6713897
80. Shashank V, Priya D. Con-Ker: a convolutional neural network based approach for keratoconus detection and classification. A Journal of University of Shanghai for Science and Technology. 2021;23: 71–81. doi: 10.51201/JUSST/21/06472
81. Xie Y, Zhao L, Yang X, Wu X, Yang Y, Huang X, et al. Screening candidates for refractive surgery with corneal tomographic-based deep learning. JAMA Ophthalmol. 2020;138: 519. doi: 10.1001/jamaophthalmol.2020.0507
