Yapay Zekanın Oftalmolojide Kullanımı

Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284:574–582. doi: 10.1148/radiol.2017162326

Ting DSW, Yi H, Hui F. Clinical applicability of deep learning system in detecting tuberculosis with chest radiography. Radiology. 2018;286:729–731. doi: 10.1148/radiol.2017172407.

Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115–118. doi: 10.1038/nature21056.

Ehteshami B, Veta M, Johannes van DP, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318:2199–2210. doi: 10.1001/jama.2017.14585.

Ting DSW, Cheung CY, 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;318:2211–2223. doi: 10.1001/jama.2017.18152.

Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402–2410. doi: 10.1001/jama.2016.17216.

Lee CS, Tyring AJ, Deruyter NP, et al. Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed Opt Express. 2017;8:3440–3448. doi: 10.1364/BOE.8.003440.

Abràmoff MD, Lou Y, Erginay A, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57:5200–5206. doi: 10.1167/iovs.16-19964.

Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology. 2017;124:962–969. doi: 10.1016/j.ophtha.2017.02.008.

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;125:1199–1206. doi: 10.1016/j.ophtha.2018.01.023.

Burlina PM, Joshi N, Pekala M, et al. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 2017;135:1170–1176. doi: 10.1001/jamaophthalmol.2017.3782.

Grassmann F, Mengelkamp J, Brandl C, et al. 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. doi: 10.1016/j.ophtha.2018.02.037.

Brown JM, Campbell JP, Beers A, et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol. 2018;136:803–810. doi: 10.1001/jamaophthalmol.2018.1934.

Poplin R, Varadarajan AV, Blumer K, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018;2:158–164. doi: 10.1038/s41551-018-0195-0.

Varadarajan AV, Poplin R, Blumer K, et al. Deep learning for predicting refractive error from retinal fundus images. Invest Ophthalmol Vis Sci. 2018;59:2861–8. doi: 10.1167/iovs.18-23887.

McCarthy J, Minsky ML, Rochester N, et al. A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine. 2006;27(4):12-16. doi:10.1609/aimag.v27i4.1904.

Srivastava O, Tennant M, Grewal P, et al. Artificial intelligence and machine learning in ophthalmology: a review. Indian J Ophthalmol. 2023;71(1):11-17. doi: 10.4103/ijo.IJO_1569_22.

Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167-175. doi: 10.1136/bjophthalmol-2018-313173.

Ahadi S, Wilson KA, Babenko B, et al. Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock. Elife. 2023;12:e82364. doi: 10.7554/eLife.82364.

Benet D, Pellicer-Valero OJ. Artificial intelligence: the unstoppable revolution in ophthalmology. Surv Ophthalmol. 2022;67(1):252-270. doi: 10.1016/j.survophthal.2021.03.003.

Mihalache A, Huang RS, Popovic MM, et al. Performance of an upgraded artificial intelligence chatbot for ophthalmic knowledge assessment. JAMA Ophthalmol. 2023;141(8):798-800. doi: 10.1001/jamaophthalmol.2023.2754.

Boudry C, Al-Hajj H, Arnould L, et al. Analysis of international publication trends in artificial intelligence in ophthalmology. Graefes Arch Clin Exp Ophthalmol. 2022;260(5):1779-1788. doi: 10.1007/s00417-021-05511-7.

Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, et al. Artificial intelligence in retina. Prog Retin Eye Res. 2018;67:1-29. doi:10.1016/j.preteyeres.2018.07.004.

Zhu SJ, Zhan HD, Wu MN, et al. Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm. Int J Ophthalmol. 2023:16(7):995-1004. doi: 10.18240/ijo.2023.07.01.

Abdullah YI, Schuman JS, Shabsigh R, et al. Ethics of artificial intelligence in medicine and ophthalmology. Asia Pac J Ophthalmol (Phila). 2021;10(3):289-298. doi: 10.1097/APO.0000000000000397.

Grzybowski A, Brona P, Lim G, et al. Artificial intelligence for diabetic retinopathy screening: a review. Eye (Lond). 2020;34(3):451-460. doi: 10.1038/s41433-019-0566-0.

Moraru AD, Costin D, Moraru RL, et al. Artificial intelligence and deep learning in ophthalmology-present and future (Review). Exp Ther Med. 2020;20(4):3469-3473. doi: 10.3892/etm.2020.9118.

Hogg HDJ, Brittain K, Teare D, et al. Safety and efficacy of an artificial intelligence- enabled decision tool for treatment decisions in neovascular age- related macular degeneration and an exploration of clinical pathway integration and implementation: protocol for a multi-methods validation study. BMJ Open. 2023;13(2):e069443. doi: 10.1136/bmjopen-2022-069443.

Bogunović H, Mares V, Reiter GS, et al. Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence. Front Med (Lausanne). 2022;9:958469. doi: 10.3389/fmed.2022.958469.

Reid JE, Eaton E. Artificial intelligence for pediatric ophthalmology. Curr Opin Ophthalmol. 2019;30(5):337-346. doi: 10.1097/ICU.0000000000000593.

Bunod R, Augstburger E, Brasnu E, et al. Artificial intelligence and glaucoma: a literature review. J Fr Ophtalmol. 2022;45(2):216-232. doi: 10.1016/j.jfo.2021.11.002.

Mayro EL, Wang MY, Elze T, et al. The impact of artificial intelligence in the diagnosis and management of glaucoma. Eye (Lond). 2020,34(1):1-11. doi: 10.1038/s41433-019-0577-x.

Ittoop SM, Jaccard N, Lanouette G, et al. The role of artificial intelligence in the diagnosis and management of glaucoma. J Glaucoma. 2022;31(3):137-146. doi: 10.1097/IJG.0000000000001972.

Balyen L, Peto T. Promising artificial intelligence-machine learning- deep learning algorithms in ophthalmology. Asia Pac J Ophthalmol (Phila). 2019;8(3):264-272. doi: 10.22608/APO.2018479.

Ting DSJ, Foo VH, Yang LWY, et al. Artificial intelligence for anterior segment diseases: emerging applications in ophthalmology. Br J Ophthalmol. 2021;105(2):158-168. doi: 10.1136/bjophthalmol-2019-315651.

Li JO, Liu HR, Ting DSJ, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: a global perspective. Prog Retin Eye Res. 2021;82:100900. doi: 10.1016/j.preteyeres.2020.100900.

Keskinbora K, Güven F. Artificial intelligence and ophthalmology. Turk J Ophthalmol. 2020;50(1):37-43. doi: 10.4274/tjo.galenos.2020.78989.

Pietris J, Lam A, Bacchi S, et al. Health economic implications of artificial intelligence implementation for ophthalmology in Australia: a systematic review. Asia Pac J Ophthalmol (Phila). 2022:11(6):554-562. doi: 10.1097/APO.0000000000000565.

Riedl, M.O. Human-centered artificial intelligence and machine learning. Hum Behav & Emerg Tech. 2019;1:33-36. doi:10.48550/arXiv.1901.11184.

Wehkamp K, Krawczak M, Schreiber S. The quality and utility of artificial intelligence in patient care. Dtsch Arztebl Int (Forthcoming). 2023;120(27-28):463-469. doi: 10.3238/arztebl.m2023.0124.

Cao J, Chang-Kit B, Katsnelson G, et al. Protocol for a systematic review and meta-analysis of the diagnostic accuracy of artificial intelligence for grading of ophthalmology imaging modalities. Diagn Progn Res. 2022;6(1):15. doi: 10.1186/s41512-022-00127-9.

Moor M, Banerjee O, Abad ZSH, et al. Foundation models for generalist medical artificial intelligence. Nature. 2023;616(7956):259-265. doi: 10.1038/s41586-023-05881-4.

Grzybowski A. Artificial intelligence in ophthalmology: promises, hazards and challenges. Grzybowski A (ed) Artificial Intelligence in Ophthalmology. Switzerland: Springer; 2021. p.1-16.

Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284:574–582. doi: 10.1148/radiol.2017162326

Ting DSW, Yi H, Hui F. Clinical applicability of deep learning system in detecting tuberculosis with chest radiography. Radiology. 2018;286:729–731. doi: 10.1148/radiol.2017172407.

Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115–118. doi: 10.1038/nature21056.

Ehteshami B, Veta M, Johannes van DP, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318:2199–2210. doi: 10.1001/jama.2017.14585.

Ting DSW, Cheung CY, 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;318:2211–2223. doi: 10.1001/jama.2017.18152.

Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402–2410. doi: 10.1001/jama.2016.17216.

Lee CS, Tyring AJ, Deruyter NP, et al. Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed Opt Express. 2017;8:3440–3448. doi: 10.1364/BOE.8.003440.

Abràmoff MD, Lou Y, Erginay A, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57:5200–5206. doi: 10.1167/iovs.16-19964.

Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology. 2017;124:962–969. doi: 10.1016/j.ophtha.2017.02.008.

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;125:1199–1206. doi: 10.1016/j.ophtha.2018.01.023.

Burlina PM, Joshi N, Pekala M, et al. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 2017;135:1170–1176. doi: 10.1001/jamaophthalmol.2017.3782.

Grassmann F, Mengelkamp J, Brandl C, et al. 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. doi: 10.1016/j.ophtha.2018.02.037.

Brown JM, Campbell JP, Beers A, et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol. 2018;136:803–810. doi: 10.1001/jamaophthalmol.2018.1934.

Poplin R, Varadarajan AV, Blumer K, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018;2:158–164. doi: 10.1038/s41551-018-0195-0.

Varadarajan AV, Poplin R, Blumer K, et al. Deep learning for predicting refractive error from retinal fundus images. Invest Ophthalmol Vis Sci. 2018;59:2861–8. doi: 10.1167/iovs.18-23887.

McCarthy J, Minsky ML, Rochester N, et al. A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine. 2006;27(4):12-16. doi:10.1609/aimag.v27i4.1904.

Srivastava O, Tennant M, Grewal P, et al. Artificial intelligence and machine learning in ophthalmology: a review. Indian J Ophthalmol. 2023;71(1):11-17. doi: 10.4103/ijo.IJO_1569_22.

Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167-175. doi: 10.1136/bjophthalmol-2018-313173.

Ahadi S, Wilson KA, Babenko B, et al. Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock. Elife. 2023;12:e82364. doi: 10.7554/eLife.82364.

Benet D, Pellicer-Valero OJ. Artificial intelligence: the unstoppable revolution in ophthalmology. Surv Ophthalmol. 2022;67(1):252-270. doi: 10.1016/j.survophthal.2021.03.003.

Mihalache A, Huang RS, Popovic MM, et al. Performance of an upgraded artificial intelligence chatbot for ophthalmic knowledge assessment. JAMA Ophthalmol. 2023;141(8):798-800. doi: 10.1001/jamaophthalmol.2023.2754.

Boudry C, Al-Hajj H, Arnould L, et al. Analysis of international publication trends in artificial intelligence in ophthalmology. Graefes Arch Clin Exp Ophthalmol. 2022;260(5):1779-1788. doi: 10.1007/s00417-021-05511-7.

Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, et al. Artificial intelligence in retina. Prog Retin Eye Res. 2018;67:1-29. doi:10.1016/j.preteyeres.2018.07.004.

Zhu SJ, Zhan HD, Wu MN, et al. Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm. Int J Ophthalmol. 2023:16(7):995-1004. doi: 10.18240/ijo.2023.07.01.

Abdullah YI, Schuman JS, Shabsigh R, et al. Ethics of artificial intelligence in medicine and ophthalmology. Asia Pac J Ophthalmol (Phila). 2021;10(3):289-298. doi: 10.1097/APO.0000000000000397.

Grzybowski A, Brona P, Lim G, et al. Artificial intelligence for diabetic retinopathy screening: a review. Eye (Lond). 2020;34(3):451-460. doi: 10.1038/s41433-019-0566-0.

Moraru AD, Costin D, Moraru RL, et al. Artificial intelligence and deep learning in ophthalmology-present and future (Review). Exp Ther Med. 2020;20(4):3469-3473. doi: 10.3892/etm.2020.9118.

Hogg HDJ, Brittain K, Teare D, et al. Safety and efficacy of an artificial intelligence- enabled decision tool for treatment decisions in neovascular age- related macular degeneration and an exploration of clinical pathway integration and implementation: protocol for a multi-methods validation study. BMJ Open. 2023;13(2):e069443. doi: 10.1136/bmjopen-2022-069443.

Bogunović H, Mares V, Reiter GS, et al. Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence. Front Med (Lausanne). 2022;9:958469. doi: 10.3389/fmed.2022.958469.

Reid JE, Eaton E. Artificial intelligence for pediatric ophthalmology. Curr Opin Ophthalmol. 2019;30(5):337-346. doi: 10.1097/ICU.0000000000000593.

Bunod R, Augstburger E, Brasnu E, et al. Artificial intelligence and glaucoma: a literature review. J Fr Ophtalmol. 2022;45(2):216-232. doi: 10.1016/j.jfo.2021.11.002.

Mayro EL, Wang MY, Elze T, et al. The impact of artificial intelligence in the diagnosis and management of glaucoma. Eye (Lond). 2020,34(1):1-11. doi: 10.1038/s41433-019-0577-x.

Ittoop SM, Jaccard N, Lanouette G, et al. The role of artificial intelligence in the diagnosis and management of glaucoma. J Glaucoma. 2022;31(3):137-146. doi: 10.1097/IJG.0000000000001972.

Balyen L, Peto T. Promising artificial intelligence-machine learning- deep learning algorithms in ophthalmology. Asia Pac J Ophthalmol (Phila). 2019;8(3):264-272. doi: 10.22608/APO.2018479.

Ting DSJ, Foo VH, Yang LWY, et al. Artificial intelligence for anterior segment diseases: emerging applications in ophthalmology. Br J Ophthalmol. 2021;105(2):158-168. doi: 10.1136/bjophthalmol-2019-315651.

Li JO, Liu HR, Ting DSJ, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: a global perspective. Prog Retin Eye Res. 2021;82:100900. doi: 10.1016/j.preteyeres.2020.100900.

Keskinbora K, Güven F. Artificial intelligence and ophthalmology. Turk J Ophthalmol. 2020;50(1):37-43. doi: 10.4274/tjo.galenos.2020.78989.

Pietris J, Lam A, Bacchi S, et al. Health economic implications of artificial intelligence implementation for ophthalmology in Australia: a systematic review. Asia Pac J Ophthalmol (Phila). 2022:11(6):554-562. doi: 10.1097/APO.0000000000000565.

Riedl, M.O. Human-centered artificial intelligence and machine learning. Hum Behav & Emerg Tech. 2019;1:33-36. doi:10.48550/arXiv.1901.11184.

Wehkamp K, Krawczak M, Schreiber S. The quality and utility of artificial intelligence in patient care. Dtsch Arztebl Int (Forthcoming). 2023;120(27-28):463-469. doi: 10.3238/arztebl.m2023.0124.

Cao J, Chang-Kit B, Katsnelson G, et al. Protocol for a systematic review and meta-analysis of the diagnostic accuracy of artificial intelligence for grading of ophthalmology imaging modalities. Diagn Progn Res. 2022;6(1):15. doi: 10.1186/s41512-022-00127-9.

Moor M, Banerjee O, Abad ZSH, et al. Foundation models for generalist medical artificial intelligence. Nature. 2023;616(7956):259-265. doi: 10.1038/s41586-023-05881-4.

Grzybowski A. Artificial intelligence in ophthalmology: promises, hazards and challenges. Grzybowski A (ed) Artificial Intelligence in Ophthalmology. Switzerland: Springer; 2021. p.1-16.