Oftalmoloji Klinik Uygulamalarında Yapay Zeka

Year 2023, Volume: 6 Issue: 3, 445 - 459, 27.12.2023

Ekrem Çelik , Ezgi İnan

https://doi.org/10.52538/iduhes.1339320

Abstract

Oftalmolojinin klinik uygulamalarında yüksek kaliteli ve tekrarlanan çok sayıda dijital görüntüler oftalmolojide yapay zekâ çalışmalarının küresel düzeyde gelişmesine olanak sağlamıştır. Direkt fotoğraf, fundus fotoğrafı ve optik koherens tomografinin başını çektiği dijital verileri kullanarak hastalıkları teşhis etmek, verileri izlemek, görüntüleri analiz etmek ve tedavi etkinliğini değerlendirmek amacıyla yapay zekâ algoritmaları kullanılabilmektedir. Başta diyabetik retinopati, glokom ve yaşa bağlı makula dejenerasyonu olmak üzere oftalmolojinin tüm alanlarında klinik uygulamalarda hızlı ve doğru karar vermek için bu programlar geniş kullanım alanı bulmuştur. Bu derleme ile yapay zekanın oftalmolojinin klinik uygulamalarındaki güncel durumu, klinik uygulamadaki yaygınlığı ve potansiyel zorluklarını ortaya koymak amaçlanmıştır.

Keywords

oftalmoloji, yapay zekâ, dijital görüntüleme

Artificial Intelligence in Ophthalmology Clinical Practices

Abstract

A large number of high-quality and repeated digital images in clinical applications of ophthalmology have allowed the development of artificial intelligence studies in ophthalmology at a global level. Artificial intelligence algorithms can be used to diagnose diseases, monitor progression, analyze images, and evaluate treatment effectiveness by using digital data led by direct photography, fundus photography and optical coherence tomography. These programs can be used to make quick and accurate decisions in clinical applications in all areas of ophthalmology, especially diabetic retinopathy, glaucoma and age-related macular degeneration. This review, it is aimed to reveal the current status of artificial intelligence in clinical applications of ophthalmology, its prevalence and potential difficulties in clinical practice.

Keywords

ophthalmology, artificial intelligence, digital imaging

References

• Abdullah, Y. I., Schuman, J. S., Shabsigh, R., Caplan, A., Al-Aswad, L. A. (2021). Ethics of artificial intelligence in medicine and ophthalmology. Asia-Pacific journal of ophthalmology (Philadelphia, Pa.), 10(3), 289.
• Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices, NPJ digital medicine, Vol. 1, 39.
• Aiken, H., Oettinger, A.G., Bartee, T.C., (1964). Proposed automatic calculating machine. IEEE spectrum, 1(8), pp.62-69.
• Arbelaez, M. C., Versaci, F., Vestri, G., Barboni, P., Savini, G. (2012). Use of a support vector machine for keratoconus and subclinical keratoconus detection by topographic and tomographic data. Ophthalmology, 119(11), 2231-2238.
• Balthazar, P., Harri, P., Prater, A., Safdar, N. M. (2018). Protecting your patients’ interests in the era of big data, artificial intelligence, and predictive analytics. Journal of the American College of Radiology, 15(3), 580-586.
• Bussel, I. I., Wollstein, G., Schuman, J. S. (2014). OCT for glaucoma diagnosis, screening and detection of glaucoma progression. British Journal of Ophthalmology, 98(Suppl 2), ii15-ii19.
• Caicho, J., Chuya-Sumba, C., Jara, N., Salum, G. M., Tirado-Espín, A., Villalba-Meneses, G., Alvarado-Cando, O., Cadena-Morejón, C., Almeida-Galárraga, D. A. (2022). Diabetic retinopathy: detection and classification using AlexNet, GoogleNet and ResNet50 convolutional neural networks. Paper presented at the Smart Technologies, Systems and Applications: Second International Conference, SmartTech-IC 2021, Quito, Ecuador, December 1–3, 2021, Revised Selected Papers.
• Choi, R. Y., Kushner, B. J. (1998). The accuracy of experienced strabismologists using the Hirschberg and Krimsky tests. Ophthalmology, 105(7), 1301-1306.
• Corrêa, Z. M., Augsburger, J. J. (2019). Indications for Fine Needle Aspiration Biopsy of Posterior Segment Intraocular Tumors. American journal of ophthalmology, 207, 45–61.
• de Almeida, J. D. S., Silva, A. C., de Paiva, A. C., Teixeira, J. A. M. (2012). Computational methodology for automatic detection of strabismus in digital images through Hirschberg test. Computers in biology and medicine, 42(1), 135-146.
• de Figueiredo, L. A., Dias, J. V. P., Polati, M., Carricondo, P. C., Debert, I. (2021). Strabismus and artificial intelligence app: optimizing diagnostic and accuracy. Translational Vision Science & Technology, 10(7), 22-22.
• Dong, L., Yang, Q., Zhang, R. H., Wei, W. B. (2021). Artificial intelligence for the detection of age-related macular degeneration in color fundus photographs: A systematic review and meta-analysis. EclinicalMedicine, 35, 100875.
• Fan, Z., Rong, Y., Cai, X., Lu, J., Li, W., Lin, H., Chen, X. (2017). Optic disk detection in fundus image based on structured learning. IEEE journal of biomedical and health informatics, 22(1), 224-234.
• Gao, X., Lin, S., Wong, T. Y. (2015). Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Transactions on Biomedical Engineering, 62(11), 2693-2701.
• Gargeya, R., Leng, T. (2017). Automated identification of diabetic retinopathy using deep learning. Ophthalmology, 124(7), 962-969.
• Graham, P. (1974). Epidemiology of strabismus. The British journal of ophthalmology, 58(3), 224. Greene, J. A., Lea, A. S. (2019). Digital futures past the long arc of big data in medicine. The New England journal of medicine, 381(5), 480.
• Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22), 2402-2410.
• Hamet, P., Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36-S40. Harrad, R., Sengpiel, F., Blakemore, C. (1996). Physiology of suppression in strabismic amblyopia. The British journal of ophthalmology, 80(4), 373.
• Hashemi, H., Heydarian, S., Hooshmand, E., Saatchi, M., Yekta, A., Aghamirsalim, M., Valadkhan, M., Mortazavi, M., Hashemi, A., Khabazkhoob, M. (2020). The prevalence and risk factors for keratoconus: a systematic review and meta-analysis. Cornea, 39(2), 263-270.
• Howard, J. (2019). Artificial intelligence: Implications for the future of work. American Journal of Industrial Medicine, 62(11), 917-926.
• Jabs, D. A., Dick, A., Doucette, J. T., Gupta, A., Lightman, S., McCluskey, P., Okada, A. A., Palestine, A. G., Rosenbaum, J. T., Saleem, S. M., Thorne, J., Trusko, B. (2018). Standardization of Uveitis Nomenclature Working Group Interobserver Agreement Among Uveitis Experts on Uveitic Diagnoses: The Standardization of Uveitis Nomenclature Experience. American journal of ophthalmology, 186, 19–24.
• Jacquot, R., Sève, P., Jackson, T. L., Wang, T., Duclos, A., Stanescu-Segall, D. (2023). Diagnosis, Classification, and Assessment of the Underlying Etiology of Uveitis by Artificial Intelligence: A Systematic Review. Journal of clinical medicine, 12(11), 3746.
• Jiang, J., Liu, X., Liu, L., Wang, S., Long, E., Yang, H., Yuan, F., Yu, D., Zhang, K., Wang, L. (2018). Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network. PLoS One, 13(7), e0201142.
• Kapoor, R., Walters, S. P., Al-Aswad, L. A. (2019). The current state of artificial intelligence in ophthalmology. Survey of ophthalmology, 64(2), 233-240.
• Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. cell, 172(5), 1122-1131. e1129.
• Koseoglu, N. D., Corrêa, Z. M., Liu, T. Y. A. (2023). Artificial intelligence for ocular oncology. Current opinion in ophthalmology, 34(5), 437–440.
• Kuo, B.-I., Chang, W.-Y., Liao, T.-S., Liu, F.-Y., Liu, H.-Y., Chu, H.-S., Chen, W.-L., Hu, F.-R., Yen, J.-Y., Wang, I.-J. (2020). Keratoconus screening based on deep learning approach of corneal topography. Translational Vision Science & Technology, 9(2), 53-53.
• Leng, T., Gargeya, R. (2017). A deep learning approach for automatic identification of referral-warranted diabetic retinopathy. Investigative Ophthalmology & Visual Science, 58(8), 825-825.
• Li, B., Powell, A.-M., Hooper, P. L., Sheidow, T. G. (2015). Prospective evaluation of teleophthalmology in screening and recurrence monitoring of neovascular age-related macular degeneration: a randomized clinical trial. JAMA ophthalmology, 133(3), 276-282.
• Li, L., Xu, M., Liu, H., Li, Y., Wang, X., Jiang, L., Wang, Z., Fan, X., Wang, N., (2019). A large-scale database and a CNN model for attention-based glaucoma detection. IEEE transactions on medical imaging, 39(2), pp.413-424.
• Li, Z., He, Y., Keel, S., Meng, W., Chang, R. T., He, M. (2018). Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology, 125(8), 1199–1206
• Lim, L. S., Mitchell, P., Seddon, J. M., Holz, F. G., Wong, T. Y. (2012). Age-related macular degeneration. The Lancet, 379(9827), 1728-1738.
• Liu, X., Jiang, J., Zhang, K., Long, E., Cui, J., Zhu, M., An, Y., Zhang, J., Liu, Z., Lin, Z. (2017). Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network. PloS one, 12(3), e0168606.
• Mao, K., Yang, Y., Guo, C., Zhu, Y., Chen, C., Chen, J., Liu, L., Chen, L., Mo, Z., Lin, B. (2021). An artificial intelligence platform for the diagnosis and surgical planning of strabismus using corneal light-reflection photos. Annals of Translational Medicine, 9(5).
• McNeil, R. (2016). Grading of ocular inflammation in uveitis: an overview. Eye news, 22(5), 1-4.
• Mohammadi, S.-F., Sabbaghi, M., Hadi, Z., Hashemi, H., Alizadeh, S., Majdi, M., Taee, F. (2012). Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification. Journal of Cataract & Refractive Surgery, 38(3), 403-408.
• Morris, F.L., Jones, C.B., (1984). An early program proof by Alan Turing. IEEE Annals of the History of Computing, 6(02), pp.139-143.
• Muramatsu, C., Hayashi, Y., Sawada, A., Hatanaka, Y., Hara, T., Yamamoto, T., Fujita, H. (2010). Detection of retinal nerve fiber layer defects on retinal fundus images for early diagnosis of glaucoma. Journal of biomedical optics, 15(1), 016021-016021-016027.
• Mursch-Edlmayr, A. S., Ng, W. S., Diniz-Filho, A., Sousa, D. C., Arnould, L., Schlenker, M. B., Duenas-Angeles, K., Keane, P. A., Crowston, J. G., Jayaram, H. (2020). Artificial intelligence algorithms to diagnose glaucoma and detect glaucoma progression: translation to clinical practice. Translational vision science & technology, 9(2), 55-55.
• Paul, S., Tayar, A., Morawiec-Kisiel, E., Bohl, B., Großjohann, R., Hunfeld, E., Busch, M., Pfeil, J. M., Dähmcke, M., Brauckmann, T. (2022). Use of artificial intelligence in screening for diabetic retinopathy at a tertiary diabetes center. Der Ophthalmologe: Zeitschrift der Deutschen Ophthalmologischen Gesellschaft.
• Raghavendra, U., Fujita, H., Bhandary, S. V., Gudigar, A., Tan, J. H., Acharya, U. R. (2018). Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Information Sciences, 441, 41-49.
• Rampat, R., Deshmukh, R., Chen, X., Ting, D. S., Said, D. G., Dua, H. S., Ting, D. S. (2021). Artificial intelligence in cornea, refractive surgery, and cataract: basic principles, clinical applications, and future directions. Asia-Pacific journal of ophthalmology (Philadelphia, Pa.), 10(3), 268.
• Rathi, S., Tsui, E., Mehta, N., Zahid, S., Schuman, J. S. (2017). The current state of teleophthalmology in the United States. Ophthalmology, 124(12), 1729-1734.
• Read, J. C. (2015). Stereo vision and strabismus. Eye, 29(2), 214-224.
• Reiner, B. I., McKinley, M. (2012). Application of innovation economics to medical imaging and information systems technologies. Journal of digital imaging, 25, 325-329.
• Salma, A., Bustamam, A., Sarwinda, D. (2021). Diabetic Retinopathy Detection Using GoogleNet Architecture of Convolutional Neural Network Through Fundus Images. Nusantara Science and Technology Proceedings, 1-6. Savoy, M. (2020). IDx-DR for diabetic retinopathy screening. American family physician, 101(5), 307-308.
• Sharma, S., Lowder, C. Y., Vasanji, A., Baynes, K., Kaiser, P. K., Srivastava, S. K. (2015). Automated Analysis of Anterior Chamber Inflammation by Spectral-Domain Optical Coherence Tomography. Ophthalmology, 122(7), 1464–1470.
• Sim, D. A., Mitry, D., Alexander, P., Mapani, A., Goverdhan, S., Aslam, T., Tufail, A., Egan, C. A., Keane, P. A. (2016). The evolution of teleophthalmology programs in the United Kingdom: beyond diabetic retinopathy screening. Journal of diabetes science and technology, 10(2), 308-317.
• Smadja, D., Touboul, D., Cohen, A., Doveh, E., Santhiago, M. R., Mello, G. R., Krueger, R. R., Colin, J. (2013). Detection of subclinical keratoconus using an automated decision tree classification. American journal of ophthalmology, 156(2), 237-246. e231.
• Sorkhabi, M. A., Potapenko, I. O., Ilginis, T., Alberti, M., Cabrerizo, J. (2022). Assessment of anterior uveitis through anterior-segment optical coherence tomography and artificial intelligence-based image analyses. Translational Vision Science & Technology, 11(4), 7-7.
• Sudhir, R. R., Dey, A., Bhattacharrya, S., Bahulayan, A. (2019). AcrySof IQ PanOptix intraocular lens versus extended depth of focus intraocular lens and trifocal intraocular lens: a clinical overview. Asia-Pacific Journal of Ophthalmology (Philadelphia, Pa.), 8(4), 335.
• Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., Kawashima, H. (2017). Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy. PloS one, 12(6), e0179790.
• Tham, Y.-C., Li, X., Wong, T. Y., Quigley, H. A., Aung, T., Cheng, C.-Y. (2014). Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology, 121(11), 2081-2090.
• Ting, D. S. J., Ang, M., Mehta, J. S., Ting, D. S. W. (2019). Artificial intelligence-assisted telemedicine platform for cataract screening and management: a potential model of care for global eye health, Vol. 103: 1537-1538, BMJ Publishing Group Ltd.
• Ting, D. S. J., Foo, V. H., Yang, L. W. Y., Sia, J. T., Ang, M., Lin, H., Chodosh, J., Mehta, J. S., Ting, D. S. W. (2021). Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology. British Journal of Ophthalmology, 105(2), 158-168.
• Treder, M., Lauermann, J. L., Eter, N. (2018). Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier. Graefe's Archive for Clinical and Experimental Ophthalmology, 256, 2053-2060.
• Trusko, B., Thorne, J., Jabs, D., Belfort, R., Dick, A., Gangaputra, S., Nussenblatt, R., Okada, A., Rosenbaum, J. (2013) Standardization of Uveitis Nomenclature (SUN) Project. The Standardization of Uveitis Nomenclature (SUN) Project. Development of a clinical evidence base utilizing informatics tools and techniques. Methods of information in medicine, 52(3), 259–S6..
• Tugal-Tutkun, I., Onal, S., Stanford, M., Akman, M., Twisk, J. W. R., Boers, M., Oray, M., Özdal, P., Kadayifcilar, S., Amer, R., Rathinam, S. R., Vedhanayaki, R., Khairallah, M., Akova, Y., Yalcindag, F., Kardes, E., Basarir, B., Altan, Ç., Özyazgan, Y., Gül, A. (2021). An Algorithm for the Diagnosis of Behçet Disease Uveitis in Adults. Ocular immunology and inflammation, 29(6), 1154–1163.
• Turing, A. M. (2009). Computing machinery and intelligence, Parsing the turing test: 23-65, Springer. Ung, L., Bispo, P. J., Shanbhag, S. S., Gilmore, M. S., Chodosh, J. (2019). The persistent dilemma of microbial keratitis: Global burden, diagnosis, and antimicrobial resistance. Survey of ophthalmology, 64(3), 255-271.
• Vaghefi, E., Hill, S., Kersten, H.M., Squirrell, D., (2020). Multimodal retinal image analysis via deep learning for the diagnosis of intermediate dry age-related macular degeneration: a feasibility study. Journal of ophthalmology, 2020.
• Valente, T. L. A., de Almeida, J. D. S., Silva, A. C., Teixeira, J. A. M., Gattass, M. (2017). Automatic diagnosis of strabismus in digital videos through cover test. Computer methods and programs in biomedicine, 140, 295-305.
• Wang, W., Yan, W., Fotis, K., Prasad, N. M., Lansingh, V. C., Taylor, H. R., Finger, R. P., Facciolo, D., He, M. (2016). Cataract surgical rate and socioeconomics: a global study. Investigative ophthalmology & visual science, 57(14), 5872-5881.
• Wong, I. G., Nugent, A. K., Vargas-Martín, F. (2009). The effect of biomicroscope illumination system on grading anterior chamber inflammation. American journal of ophthalmology, 148(4), 516–520.
• Wright, K. W., Spiegel, P. H., Hengst, T. (2013). Pediatric ophthalmology and strabismus: Springer Science & Business Media.
• Wu, X., Huang, Y., Liu, Z., Lai, W., Long, E., Zhang, K., Jiang, J., Lin, D., Chen, K., Yu, T. (2019). Universal artificial intelligence platform for collaborative management of cataracts. British Journal of Ophthalmology, 103(11), 1553-1560.
• Wu, X., Liu, L., Zhao, L., Guo, C., Li, R., Wang, T., Yang, X., Xie, P., Liu, Y., Lin, H. (2020). Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization. Annals of Translational Medicine, 8(11).
• Xu, X., Zhang, L., Li, J., Guan, Y., Zhang, L. (2019). A hybrid global-local representation CNN model for automatic cataract grading. IEEE journal of biomedical and health informatics, 24(2), 556-567.
• Yoo, T. K., Choi, J. Y., Seo, J. G., Ramasubramanian, B., Selvaperumal, S., Kim, D. W. (2019). The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment. Medical & biological engineering & computing, 57, 677-687.
• Zéboulon, P., Debellemanière, G., Bouvet, M., Gatinel, D. (2020). Corneal topography raw data classification using a convolutional neural network. American Journal of Ophthalmology, 219, 33-39.
• Zheng, C., Johnson, T. V., Garg, A., Boland, M. V. (2019). Artificial intelligence in glaucoma. Current opinion in ophthalmology, 30(2), 97-103.
• Zhang, H., Liu, Y., Zhang, K., Hui, S., Feng, Y., Luo, J., Li, Y., Wei, W. (2021). Validation of the Relationship Between Iris Color and Uveal Melanoma Using Artificial Intelligence With Multiple Paths in a Large Chinese Population. Frontiers in cell and developmental biology, 9, 713209.
• Zhou, Y., Li, G., Li, H. (2019). Automatic cataract classification using deep neural network with discrete state transition. IEEE transactions on medical imaging, 39(2), 436-446.