Oftalmolojide Yapay Zekanın Yeri
Özet
Günümüzde giderek artan ve yaşlanan nüfus sağlık hizmet sunucularının artan talebe yetişmesini zorlaştırmakta ve sağlığın sürdürülebilir olmasının önünde önemli bir engel olarak durmaktadır. Özellikle son yıllarda ortaya çıkan büyük teknolojik gelişmeler arasında yer alan yapay zeka temelli sistemler, her alanda olduğu gibi tıp alanında da ilgiyle takip edilen bir konudur. Başta tanı ve tarama süreçlerinin geliştirilmesine yönelik yapay zeka çalışmalarının sayısı her geçen gün artmaktadır. Oftalmoloji alanında günümüzde diyabetik retinopati (DR), yaşa bağlı maküla dejenerasyonu (YBMD), glokom, prematüre retinopatisi (ROP), nöro-oftalmoloji ve göz hareket çalışmaları ile ön plana çıkan yapay zekanın gelecek yıllarda klinik pratiğimizde daha çok yer alacağı düşünülmektedir. DR ve ROP başta olmak üzere oftalmoloji pratiğinde yer alan hastalıkların tanı süreçlerinde kabul edilebilir sonuçlar ile birlikte gündelik pratiğimizde yer alabilmesi için klinik ve maliyet etkinlik çalışmalarının artması gerekmektedir. Bu bölümde yapay zekanın oftalmoloji alanındaki yerinden bahsedilmiştir.
Referanslar
Ting DSW, Carin L, Dzau V, et al. Digital technology and COVID-19. Nature Medicine. 2020;26(4):459-461. doi:10.1038/s41591-020-0824-5.
McCarthy J, Minsky ML, Rochester N, et al. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. AI Magazine. 2006;27(4):12. doi:10.1609/aimag.v27i4.1904.
Samuel AL. Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development. 1959;3(3):210-229. doi:10.1147/rd.33.0210.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-444. doi:10.1038/nature14539.
Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks. 2015;61:85-117. doi:10.1016/j.neunet.2014.09.003.
Lakhani P, Sundaram B. Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology. 2017;284(2):574-582. doi:10.1148/radiol.2017162326.
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118. doi:10.1038/nature21056.
Yau JWY, Rogers SL, Kawasaki R, et al. Global Prevalence and Major Risk Factors of Diabetic Retinopathy. Diabetes Care. 2012;35(3):556-564. doi:10.2337/dc11-1909.
Arsalan M, Owais M, Mahmood T, et al. Aiding the diagnosis of diabetic and hypertensive retinopathy using artificial intelligence-based semantic segmentation. Journal of Clinical Medicine. 2019;8(9). doi:10.3390/jcm8091446.
Gunasekeran D v., Ting DSW, Tan GSW, et al. Artificial intelligence for diabetic retinopathy screening, prediction and management. Current Opinion in Ophthalmology. 2020;31(5):357-365. doi:10.1097/ICU.0000000000000693.
Abràmoff MD, Lou Y, Erginay A, et al. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. Investigative Opthalmology & Visual Science. 2016;57(13):5200. doi:10.1167/iovs.16-19964.
Ting DSW, Cheung CY-L, Lim G, et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA. 2017;318(22):2211. doi:10.1001/jama.2017.18152.
Wong WL, Su X, Li X, et al. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. The Lancet Global Health. 2014;2(2):e106-e116. doi:10.1016/S2214-109X(13)70145-1.
de Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine. 2018;24(9):1342-1350. doi:10.1038/s41591-018-0107-6.
Bhuiyan A, Wong TY, Ting DSW, et al. Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD. Translational Vision Science & Technology. 2020;9(2):25. doi:10.1167/tvst.9.2.25.
Yim J, Chopra R, Spitz T, et al. Predicting conversion to wet age-related macular degeneration using deep learning. Nature Medicine. 2020;26(6):892-899. doi:10.1038/s41591-020-0867-7.
Tham Y-C, Li X, Wong TY, et al. Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040. Ophthalmology. 2014;121(11):2081-2090. doi:10.1016/j.ophtha.2014.05.013.
Li Z, He Y, Keel S, et al. Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology. 2018;125(8):1199-1206. doi:10.1016/j.ophtha.2018.01.023.
Blencowe H, Moxon S, Gilbert C. Update on Blindness Due to Retinopathy of Prematurity Globally and in India. Indian Pediatrics. 2016;53 Suppl 2:S89-S92. http://www.ncbi.nlm.nih.gov/pubmed/27915313.
Cryotherapy for Retinopathy of Prematurity Cooperative Group. Multicenter Trial of Cryotherapy for Retinopathy of Prematurity. Archives of Ophthalmology. 2001;119(8):1110. doi:10.1001/archopht.119.8.1110.
Good W v., Hardy RJ, Dobson V, et al. Final Visual Acuity Results in the Early Treatment for Retinopathy of Prematurity Study. Archives of Ophthalmology. 2010;128(6):663. doi:10.1001/archophthalmol.2010.72.
Brown JM, Campbell JP, Beers A, et al. Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks. JAMA Ophthalmology. 2018;136(7):803. doi:10.1001/jamaophthalmol.2018.1934.
Biousse V, Newman NJ, Najjar RP, et al. Optic Disc Classification by Deep Learning versus Expert Neuro‐Ophthalmologists. Annals of Neurology. 2020;88(4):785-795. doi:10.1002/ana.25839.
Yang HK, Oh JE, Han SB, et al. Automatic computer‐aided analysis of optic disc pallor in fundus photographs. Acta Ophthalmologica. 2019;97(4):e519-e525. doi:10.1111/aos.13970.
Yang HK, Kim YJ, Sung JY, et al. Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems. American Journal of Ophthalmology. 2020;216:140-146. doi:10.1016/j.ajo.2020.03.035.
Sousa de Almeida JD, Silva AC, Teixeira JAM, et al. Computer-Aided Methodology for Syndromic Strabismus Diagnosis. Journal of Digital Imaging. 2015;28(4):462-473. doi:10.1007/s10278-014-9758-0.
Zheng C, Yao Q, Lu J, et al. Detection of Referable Horizontal Strabismus in Children’s Primary Gaze Photographs Using Deep Learning. Translational Vision Science & Technology. 2021;10(1):33. doi:10.1167/tvst.10.1.33.
