Применение искусственного интеллекта в диагностике и хирургии кератоконуса: систематический обзор
Ключевые слова:
искусственный интеллект, машинное обучение, кератоконус, диагностикаАннотация
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.
Библиографические ссылки
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
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
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
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
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
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.
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
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
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
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.
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.
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
Altinkurt E, Avci O, Muftuoglu O, Ugurlu A, Cebeci Z, Ozbilen KT. Logisti regression model using scheimpflug-placido cornea topographer parameters to diagnose keratoconus. J Ophthalmol. 2021;2021: 1–7. doi: 10.1155/2021/5528927
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
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
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
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
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
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
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
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.
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
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
Maeda N, Klyce SD, Smolek MK, Thompson HW. Automated keratoconus screening with corneal topography analysis. Invest Ophthalmol Vis Sci. 1994;35: 2749–2757.
Maeda N, Klyce SD, Smolek MK. Neural network classification of corneal topography. Preliminary demonstration. Invest Ophthalmol Vis Sci. 1995;36: 1327–1335.
Smolek MK, Klyce SD. Current keratoconus detection methods compared with a neural network approach. Invest Ophthalmol Vis Sci. 1997;38: 2290–2299.
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
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
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.
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
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
Mahmoud HAH, Mengash HA. Automated keratoconus detection by 3D corneal images reconstruction sensors. 2021;21: 2326. doi: 10.3390/s21072326
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
Lavric A, Valentin P. KeratoDetect: keratoconus detection algorithm using convolutional neural networks. Comput Intell Neurosci. 2019;2019: 1–9. doi:10.1155/2019/8162567
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
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
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
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
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
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
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
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
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
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
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
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