Yıl: 2020 Cilt: 50 Sayı: 1 Sayfa Aralığı: 37 - 43 Metin Dili: Türkçe DOI: 10.4274/tjo.galen020.789os.289 İndeks Tarihi: 07-10-2020

Yapay Zeka ve Oftalmoloji

Öz:
Son yıllarda, hızla gelişmekte ve hemen bütün yaşam alanlarında kendine yer edinmekte olan yapay zekanın; oftalmoloji alanında kullanımıyla ilgili gelişmeleri ve olası uygulamaları, gerek oftalmoloji gerek tıbbi etik çerçevelerinde değerlendirerek ele almak. Yapay zeka uygulamalarının göz hastalıklarının tanısıyla ilgili çeşitli uygulamaları, kitap, dergi, arama motorları, basılı ve sosyal medya aracılığıyla tarandı. Kaynaklar çapraz kontrole tabi tutularak, bilgilerin sağlıklı olup olmadığı kontrol edildi. Kimisi ABD İlaç ve Gıda Kurumu tarafından onay da almış bulunan yapay zeka algoritmalarının, göz hastalıkları alanında özellikle tanıya yönelik araştırmalarda kendine yer bulduğu saptandı. Oftalmoloji alanında özellikle diyabetik retinopati, yaşa bağlı maküla dejenerasyonu, prematür retinopatisi konularında yapay zeka algoritmalarından yararlanılabileceğini kanıtlayan araştırmalar geliştirilmektedir. Bu algoritmalardan bazılarının onay aşamasına geldiği anlaşılmaktadır. Yapay zeka çalışmalarında gelinen nokta, bu teknolojinin halen önemli bir mesafe kat ettiğini göstermekte ve gelecekçalışmalar için umut vadetmektedir. Yöntemlerin, özellikle yetişmiş insan nüfusunun az ve hekime ulaşımın zor olduğu gelişmekte olan ülkelerde önlenebilir görme kayıplarının belirlenmesinde ve hekime yönlendirilmesinde etkili olacağı düşünülmektedir. Gelecekteki bazı yapay zeka sistemlerinin ahlaki/etik statüye sahip adaylar olabileceğini düşündüğümüzde, farklı etik konular ortaya çıkmaktadır. Ahlaki/etik durum ile ilgili sorular, uygulamalı etiğin bazı alanlarında önemlidir. Günümüz yapay zeka sistemlerinin bir ahlaki/etik statüye sahip olmadığı üzerinde anlaşmaya varılmış olduğu görülmekle birlikte, ahlaki/etik durumu belirleyen özelliklerin tam olarak ne olduğu ve ne olacağı da bilinmemektedir
Anahtar Kelime:

Artificial Intelligence and Ophthalmology

Öz:
Artificial intelligence is advancing rapidly and making its way into all areas of our lives. This review discusses developments and potential practices regarding the use of artificial intelligence in the field of ophthalmology, and the related topic of medical ethics. Various artificial intelligence applications related to the diagnosis of eye diseases were researched in books, journals, search engines, print and social media. Resources were cross-checked to verify the information. Artificial intelligence algorithms, some of which were approved by the US Food and Drug Administration, have been adopted in the field of ophthalmology, especially in diagnostic studies. Studies are being conducted that prove that artificial intelligence algorithms can be used in the field of ophthalmology, especially in diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity. Some of these algorithms have come to the approval stage. The current point in artificial intelligence studies shows that this technology has advanced considerably and shows promise for future work. It is believed that artificial intelligence applications will be effective in identifying patients with preventable vision loss and directing them to physicians, especially in developing countries where there are fewer trained professionals and physicians are difficult to reach. When we consider the possibility that some future artificial intelligence systems may be candidates for moral/ethical status, certain ethical issues arise. Questions about moral/ethical status are important in some areas of applied ethics. Although it is accepted that current intelligence systems do not have moral/ethical status, it has yet to be determined what the exact the characteristics that confer moral/ethical status are or will be.
Anahtar Kelime:

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  • 1. Copeland BJ. Artificial intelligence | Definition, Examples, and Applications| Britannica.com. Encyclopedia Britannica, https://www.britannica.com/technology/artificial-intelligence (2019, accessed 25 February 2019).
  • 2. Adams S, Arel I, Bach J, Coop R, Furlan R, Goertzel B, Hall JS, Samsonovich A, Scheutz M, Schlesinger M, Shapiro SC, Sowa J. Mapping the Landscape of Human-Level Artificial General Intelligence. AI Magazine. 2012;33:25-42.
  • 3. Computer AI passes Turing test in ‘world first’ - BBC News, https://www.bbc.com/news/technology-27762088 (2014, accessed 25 February 2019).
  • 4. Russell SJ, Norvig P. Artificial intelligence : a modern approach. 3rd ed. New Jersey: Pearson Education. 2010.
  • 5. Samuel AL. Some Studies in Machine Learning Using the Game of Checkers. II Recent Progress. In: Computer Games I. New York, NY: Springer New York, pp. 366-400.
  • 6. PrecisionFDA Truth Challenge - Google Genomics v1 documentation, https:// googlegenomics.readthedocs.io/en/staging-2/use_cases/discover_public_data/ precision_fda.html (accessed 26 February 2019).
  • 7. Levy MC. FDA and Artificial Intelligence in Digital Health Innovation |Artificial Intelligence Law Blog, https://www.artificialintelligencelawblog.com/2018/12/fda-artificial-intelligence-digital-health-innovation/ (2018, accessed 3 March 2019).
  • 8. Lakhani P, Sundaram B. Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology. 2017;284:574-582.
  • 9. Ting DSW, Yi PH, Hui F. Clinical Applicability of Deep Learning System in Detecting Tuberculosis with Chest Radiography. Radiology. 2018;286:729-731.
  • 10. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S.Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-118.
  • 11. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B,Karssemeijer N, Litjens G, van der Laak JAWM; the CAMELYON16 Consortium, Hermsen M, Manson QF, Balkenhol M, Geessink O, Stathonikos N, van Dijk MC, Bult P, Beca F, Beck AH, Wang D, Khosla A, Gargeya R, Irshad H, Zhong A, Dou Q , Li Q, Chen H, Lin HJ, Heng PA, Haß C, Bruni E, Wong Q, Halici U, Öner MÜ, Cetin-Atalay R, Berseth M, Khvatkov V, Vylegzhanin A, Kraus O, Shaban M, Rajpoot N, Awan R, Sirinukunwattana K, Qaiser T, Tsang YW, Tellez D, Annuscheit J, Hufnagl P, Valkonen M, Kartasalo K, Latonen L, Ruusuvuori P, Liimatainen K, Albarqouni S, Mungal B, George A, Demirci S, Navab N, Watanabe S, Seno S, Takenaka Y, Matsuda H, Ahmady Phoulady H, Kovalev V, Kalinovsky A, LiauchukV, Bueno G, Fernandez-Carrobles MM, Serrano I, Deniz O, Racoceanu D, Venâncio R.Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. 2017;318:2199-2210.
  • 12. Kubota T. Algorithm better at diagnosing pneumonia than radiologists | News Center | Stanford Medicine, https://med.stanford.edu/news/all-news/2017/11/ algorithm-can-diagnose-pneumonia-better-than-radiologists.html (accessed 3 March 2019).
  • 13. Arias E, Heron M, Xu J. United States life tables, 2014. Natl Vital Stat Rep. 2017;66:1-64.
  • 14. Zheng Y, He M, Congdon N. The worldwide epidemic of diabetic retinopathy. Indian J Ophthalmol. 2012;60:428-431.
  • 15. Flaxman SR, Bourne RRA, Resnikoff S, Ackland P, Braithwaite T, Cicinelli MV, Das A, Jonas JB, Keeffe J, Kempen JH, Leasher J, Limburg H, Naidoo K, Pesudovs K, Silvester A, Stevens GA, Tahhan N, Wong TY, Taylor HR; Vision Loss Expert Group of the Global Burden of Disease Study. Global causes of blindness and distance vision impairment 1990-2020: a systematic review and meta-analysis. Lancet Glob Health. 2017;5:e1221-e1234.
  • 16. Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathyin primary care offices. npj Digital Medicine. 2018;1:39.
  • 17. Ramachandran N, Hong SC, Sime MJ, Wilson AG. Diabetic retinopathy screening using deep neural network. Clinical & Experimental Ophthalmology. 2018;46:412-416.
  • 18. Gargeya R, Leng T. Automated Identification of Diabetic Retinopathy Using Deep Learning. Ophthalmology. 2017;124:962-969.
  • 19. Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. Invest Ophthalmol Vis Sci. 2016;57:5200-5206.
  • 20. Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, Hamzah H, Garcia-Franco R, San Yeo IY, Lee SY, Wong EYM, Sabanayagam C, Baskaran M, Ibrahim F, Tan NC, Finkelstein EA, Lamoureux EL, Wong IY, Bressler NM, Sivaprasad S, Varma R, Jonas JB, He MG, Cheng CY, Cheung GCM, Aung T, Hsu W, Lee ML, Wong TY. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye DiseasesUsing Retinal Images From Multiethnic Populations With Diabetes. JAMA.2017;318:2211-2223.
  • 21. Gulshan V, Peng L, Coram M. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402-2410.
  • 22. Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J, Lee PY, Shaw J, Ting D, Wong TY, Taylor H, Chang R, He M. An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs. Diabetes Care. 2018;41:2509-2516.
  • 23. Acharya UR, Ng EYK, Tan J-H, Sree SV, Ng KH. An integrated index for the identification of diabetic retinopathy stages using texture parameters. J Med Syst. 2012;36:2011-2020.
  • 24. Ganesan K, Martis RJ, Acharya UR, Chua CK, Min LC, Ng EY, Laude A.Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images. Med Biol Eng Comput. 2014;52:663-672.
  • 25. Sandhu HS, Eltanboly A, Shalaby A, Keynton RS, Schaal S, El-Baz A. Automated Diagnosis and Grading of Diabetic Retinopathy Using Optical Coherence Tomography. Invest Ophthalmol Vis Sci. 2018;59:3155-3160.
  • 26. Adhi M, Semy SK, Stein DW, Potter DM, Kuklinski WS, Sleeper HA, Duker JS, Waheed NK. Application of Novel Software Algorithms to SpectralDomain Optical Coherence Tomography for Automated Detection of Diabetic Retinopathy. Ophthalmic Surg Lasers Imaging Retina. 2016;47:410-417.
  • 27. ElTanboly A, Ismail M, Shalaby A, Switala A, El-Baz A, Schaal S, Gimel’farb G, El-Azab M. A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images. Med Phys. 2017;44:914- 923.
  • 28. Qi SR. Machine Learning and OCT Images—the Future of Ophthalmology, https://medium.com/health-ai/machine-learning-and-oct-images-the-futureof-ophthalmology-47dc64ee9dc6.
  • 29. Qi SR. Deep Learning in Ophthalmology — How Google Did It – Health, https://medium.com/health-ai/deep-learning-in-ophthalmology-using-128-175-retinal-images-59814e8a3f68 (accessed 4 March 2019).
  • 30. Agurto C, Barriga ES, Murray V, Nemeth S, Crammer R, Bauman W, Zamora G, Pattichis MS, Soliz P. Automatic Detection of Diabetic Retinopathy and Age-Related Macular Degeneration in Digital Fundus Images. Invest Ophthalmol Vis Sci. 2011;52:5862-5871.
  • 31. Zheng Y, Hijazi MHA, Coenen F. Automated “Disease/No Disease” Grading of Age-Related Macular Degeneration by an Image Mining Approach. Invest Ophthalmol Vis Sci. 2012;53:8310-8318.
  • 32. Mookiah MR, Acharya UR, Koh JE, Chandran V, Chua CK, Tan JH, Lim CM, Ng EY, Noronha K, Tong L, Laude A. Automated diagnosis of Agerelated Macular Degeneration using greyscale features from digital fundus images. Comput Biol Med. 2014;53:55-64.
  • 33. Burlina PM, Joshi N, Pekala M, Pacheco KD, Freund DE, Bressler NM. Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks. JAMA Ophthalmol. 2017;135:1170-1176.
  • 34. Grassmann F, Mengelkamp J, Brandl C, Harsch S, Zimmermann ME, Linkohr B, Peters A, Heid IM, Palm C, Weber BHF. A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography. Ophthalmology. 2018;125:1410-1420.
  • 35. Singh A, Dutta MK, ParthaSarathi M, Uher V, Burget R. Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image. Comput Methods Programs Biomed. 2016;124:108-120.
  • 36. Salam AA, Khalil T, Akram MU, Jameel A, Basit I. Automated detection of glaucoma using structural and non structural features. Springerplus. 2016;5:1519.
  • 37. Chakrabarty L, Joshi GD, Chakravarty A, Raman GV, Krishnadas SR, Sivaswamy J. Automated Detection of Glaucoma From Topographic Features of the Optic Nerve Head in Color Fundus Photographs. J Glaucoma. 2016;25:590-597.
  • 38. Chen X, Xu Y, Kee Wong DW, Wong TY, Liu J. Glaucoma detection based on deep convolutional neural network. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Conference 2015:715-718.
  • 39. Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology. 2018;125:1199-1206.
  • 40. Muhammad H, Fuchs TJ, De Cuir N, De Moraes CG, Blumberg DM, Liebmann JM, Ritch R, Hood DC. Hybrid Deep Learning on Single Widefield Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects. J Glaucoma. 2017;26:1086-1094.
  • 41. 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;56:3957-3966.
  • 42. Bowd C, Lee I, Goldbaum MH, Balasubramanian M, Medeiros FA, Zangwill LM, Girkin CA, Liebmann JM, Weinreb RN. Predicting Glaucomatous Progression in Glaucoma Suspect Eyes Using Relevance Vector Machine Classifiers for Combined Structural and Functional Measurements. Invest Ophthalmol Vis Sci. 2012;53:2382-2389.
  • 43. Niwas SI, Lin W, Bai X, Kwoh CK, Jay Kuo CC, Sng CC, Aquino MC, Chew PT. Automated anterior segment OCT image analysis for Angle Closure Glaucoma mechanisms classification. Comput Methods Programs Biomed. 2016;130:65-75.
  • 44. Silva FR, Vidotti VG, Cremasco F, Dias M, Gomi ES, Costa VP. Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry. Arq Bras Oftalmol. 2013;76:170-174.
  • 45. Vidotti VG, Costa VP, Silva FR, Resende GM, Cremasco F, Dias M, Gomi ES. Sensitivity and Specificity of Machine Learning Classifiers and Spectral Domain OCT for the Diagnosis of Glaucoma. Eur J Ophthalmol. 2012;23:61-69.
  • 46. Fleck BW, Dangata Y. Causes of visual handicap in the Royal Blind School, Edinburgh, 1991-2. Br J Ophthalmol. 1994;78:421.
  • 47. Early Treatment for Retinopathy of Prematurity Cooperative Group, Good WV, Hardy RJ, Dobson V, Palmer EA, Phelps DL, Tung B, Redford M. Final visual acuity results in the early treatment for retinopathy of prematurity study. Arch Ophthalmol. 2010;128:663-671.
  • 48. Chiang MF, Jiang L, Gelman R, Du YE, Flynn JT. Interexpert Agreement of Plus Disease Diagnosis in Retinopathy of Prematurity. Arch Ophthalmol. 2007;125:875-880.
  • 49. Braverman RS, Enzenauer RW. Socioeconomics of Retinopathy of Prematurity In-Hospital Care. Arch Ophthalmol. 2010;128:1055-1058.
  • 50. Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Chiang MF, Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium. Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks. JAMA Ophthalmol. 2018;136:803-810.
  • 51. Redd TK, Campbell JP, Brown JM, Kim SJ, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium. Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. Br J Ophthalmol. 2018;bjophthalmol-2018-313156.
  • 52. De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, Askham H, Glorot X, O’Donoghue B, Visentin D, van den Driessche G, Lakshminarayanan B, Meyer C, Mackinder F, Bouton S, Ayoub K, Chopra R, King D, Karthikesalingam A, Hughes CO, Raine R, Hughes J, Sim DA, Egan C, Tufail A, Montgomery H, Hassabis D, Rees G, Back T, Khaw PT, Suleyman M, Cornebise J, Keane PA, Ronneberger O. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24:1342-1350.
  • 53. Kamm FM. Terrorism and several moral distinctions. Legal Theory. 2006;12:19-69.
  • 54. Frankish K, Ramsey WM. The Cambridge handbook of artificial intelligence. Choice Reviews Online. 2015;52:3019-3019.
  • 55. Keskinbora HK, Keskinbora K. Ethical considerations on novel neuronal interfaces. Neurol Sci. 2018;39:607-613.
  • 56. Bostrom N, Cirkovic MM. Global catastrophic risks. Choice Reviews Online.2013;46:6152-6152.
  • 57. Vinge V. The Coming Technological Singularity: How to Survive in the PostHuman Era. Science Fiction Criticism: An Anthology of Essential Writings.1993;352-363.
  • 58. Kurzweil R. The singularity is near : when humans transcend biology. Viking;2005. https://www.amazon.com/Singularity-Near-Humans-TranscendBiology/dp/0143037889
  • 59. Frankish K, Ramsey WM. The Cambridge Handbook of Artificial Intelligence, https://philpapers.org/rec/FRATCH-2 (2014, accessed 4 March 2019).
  • 60. Keskinbora KH, Jameel MA. Nanotechnology Applications and Approaches in Medicine: A Review. Journal of Nanotechnology; 2018:1-5.
APA Keskinbora K, Guven F (2020). Yapay Zeka ve Oftalmoloji. , 37 - 43. 10.4274/tjo.galen020.789os.289
Chicago Keskinbora Kadircan,Guven Fatih Yapay Zeka ve Oftalmoloji. (2020): 37 - 43. 10.4274/tjo.galen020.789os.289
MLA Keskinbora Kadircan,Guven Fatih Yapay Zeka ve Oftalmoloji. , 2020, ss.37 - 43. 10.4274/tjo.galen020.789os.289
AMA Keskinbora K,Guven F Yapay Zeka ve Oftalmoloji. . 2020; 37 - 43. 10.4274/tjo.galen020.789os.289
Vancouver Keskinbora K,Guven F Yapay Zeka ve Oftalmoloji. . 2020; 37 - 43. 10.4274/tjo.galen020.789os.289
IEEE Keskinbora K,Guven F "Yapay Zeka ve Oftalmoloji." , ss.37 - 43, 2020. 10.4274/tjo.galen020.789os.289
ISNAD Keskinbora, Kadircan - Guven, Fatih. "Yapay Zeka ve Oftalmoloji". (2020), 37-43. https://doi.org/10.4274/tjo.galen020.789os.289
APA Keskinbora K, Guven F (2020). Yapay Zeka ve Oftalmoloji. Türk Oftalmoloji Dergisi, 50(1), 37 - 43. 10.4274/tjo.galen020.789os.289
Chicago Keskinbora Kadircan,Guven Fatih Yapay Zeka ve Oftalmoloji. Türk Oftalmoloji Dergisi 50, no.1 (2020): 37 - 43. 10.4274/tjo.galen020.789os.289
MLA Keskinbora Kadircan,Guven Fatih Yapay Zeka ve Oftalmoloji. Türk Oftalmoloji Dergisi, vol.50, no.1, 2020, ss.37 - 43. 10.4274/tjo.galen020.789os.289
AMA Keskinbora K,Guven F Yapay Zeka ve Oftalmoloji. Türk Oftalmoloji Dergisi. 2020; 50(1): 37 - 43. 10.4274/tjo.galen020.789os.289
Vancouver Keskinbora K,Guven F Yapay Zeka ve Oftalmoloji. Türk Oftalmoloji Dergisi. 2020; 50(1): 37 - 43. 10.4274/tjo.galen020.789os.289
IEEE Keskinbora K,Guven F "Yapay Zeka ve Oftalmoloji." Türk Oftalmoloji Dergisi, 50, ss.37 - 43, 2020. 10.4274/tjo.galen020.789os.289
ISNAD Keskinbora, Kadircan - Guven, Fatih. "Yapay Zeka ve Oftalmoloji". Türk Oftalmoloji Dergisi 50/1 (2020), 37-43. https://doi.org/10.4274/tjo.galen020.789os.289