Subscribe to RSS
Please copy the URL and add it into your RSS Feed Reader.
https://www.thieme-connect.de/rss/thieme/en/10.1055-s-00000045.xml
Der Nuklearmediziner 2021; 44(03): 284-288
DOI: 10.1055/a-1232-3629
DOI: 10.1055/a-1232-3629
Die Niederlassung in der Zukunft
Anwendungsmöglichkeiten von „Künstlicher Intelligenz“ und „Big Data“ in der ophthalmologischen Diagnostik
Applications of „Artificial Intelligence“ and „Big Data“ in Ophthalmological DiagnosticsZusammenfassung
„Künstliche Intelligenz“ und „Big Data“ haben in den letzten Jahren immer mehr Einzug in die Medizin erhalten. Auch die Augenheilkunde ist hiervon betroffen. Dieser Artikel soll den Lesern dieser Zeitschrift einen Überblick über interessante ophthalmologische Anwendungsmöglichkeiten aufzeigen.
Abstract
„Artificial Intelligence“ and „Big Data“ has increasingly found their way into medicine in recent years. Ophthalmology is also affected by this. This article is intended to give the readers of this journal an overview of interesting ophthalmological applications.
Schlüsselwörter
Künstliche Intelligenz - Big Data - Augenheilkunde - Kornea - Retina - Glaukom - Multiple SkleroseKeywords
Artificial Intelligence - Big Data - Ophthalmology - Cornea - Retina - Glaucoma - Multiple SclerosisPublication History
Article published online:
31 August 2021
© 2021. Thieme. All rights reserved.
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
-
Literatur
- 1 Helmholtz H. Beschreibung eines Augen-Spiegels zur Untersuchung der Netzhaut im lebenden Auge. Berlin: Förstner; 1851
- 2 Gullstrand A. Demonstration der Nernst-Spaltlampe. Heidelberg: Ber dt Ges; 1911: 374-376
- 3 Novotny H, Alvis D. A method of photographing fluorescence in circulating blood in the human retina. Circulation 1961; 24: 82-86
- 4 Huang D, Swanson E, Lin C. et al. Optical coherence tomography. Science 1991; 254: 1178-1181
- 5 Schmidt-Erfurth U, Sadeghipour A, Gerendas B. et al. Artificial intelligence in retina. Prog Retin Eye Res 2018; 67: 1-29 DOI: 10.1016/j.preteyeres.2018.07.004.
- 6 Treder M, Eter N. „Deep Learning“ und neuronale Netzwerke in der Augenheilkunde. Ophthalmologe 2018; 115: 714-721 DOI: 10.1007/s00347-018-0706-0.
- 7 Treder M, Diener R, Eter N. Künstliche Intelligenz zum Management von Makulaödemen. Ophthalmologe 2020; 117: 989-992 DOI: 10.1007/s00347-020-01110-9.
- 8 Ting D, Foo V, Yang L. et al. Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology. Br J Ophthalmol 2020; DOI: 10.1136/bjophthalmol-2019-315651.
- 9 Kamiya K, Ayatsuka Y, Kato Y. 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.
- 10 Yousefi S, Yousefi E, Takahashi H. et al. Keratoconus severity identification using unsupervised machine learning. PLoS One 2018; 13: e0205998 DOI: 10.1371/journal.pone.0205998.
- 11 Dos Santos V, Schmetterer L, Stegmann H. et al. CorneaNet: fast segmentation of cornea OCT scans of healthy and keratoconic eyes using deep learning. Biomed Opt Express 2019; 10: 622-641 DOI: 10.1364/BOE.10.000622.
- 12 Lv J, Zhang K, Chen Q. et al. Deep learning-based automated diagnosis of fungal keratitis with in vivo confocal microscopy images. Ann Transl Med 2020; 8: 706 DOI: 10.21037/atm.2020.03.134.
- 13 Wu X, Qui Q, Liu Z. et al. Hyphae Detection in Fungal Keratitis Images With Adaptive Robust Binary Pattern. IEEE Access 2018; 13449-13460 DOI: 10.1109/ACCESS.2018.2808941.
- 14 Liu Z, Cao Y, Li Y. et al. Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network. Comput Methods Programs Biomed 2020; 187: 105019 DOI: 10.1016/j.cmpb.2019.105019.
- 15 Sahay P, Stevenson L, Agarwal T. et al. Shaped corneal transplantation surgery. Br J Ophthalmol 2020; DOI: 10.1136/bjophthalmol-2019-315754.
- 16 Ang M, Wilkins M, Mehta J. et al. Descemet membrane endothelial keratoplasty. Br J Ophthalmol 2015; 100: 15-21 DOI: 10.1136/bjophthalmol-2015-306837.
- 17 Treder M, Lauermann J, Alnawaiseh M. et al. Using Deep Learning in Automated Detection of Graft Detachment in Descemet Membrane Endothelial Keratoplasty: A Pilot Study. Cornea 2019; 38: 157-161 DOI: 10.1097/ICO.0000000000001776.
- 18 Hayashi T, Tabuchi H, Masumoto H. et al. A Deep Learning Approach in Rebubbling After Descemet's Membrane Endothelial Keratoplasty. Eye Contact Lens 2020; 46: 121-126 DOI: 10.1097/ICL.0000000000000634.
- 19 Burlina P, Pacheco KD, Joshi N. et al. Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis. Comput Biol Med 2017; 82: 80-86 DOI: 10.1016/j.compbiomed.2017.01.018.
- 20 Treder M, Lauermann JL, Eter N. Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning. Graefes Arch Clin Exp Ophthalmol 2018; 256: 259-265 DOI: 10.1007/s00417-017-3850-3.
- 21 Treder M, Lauermann JL, Eter N. Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier. Graefes Arch Clin Exp Ophthalmol 2018; 256: 2053-2060 DOI: 10.1007/s00417-018-4098-2.
- 22 Yoo TK, Choi JY, Seo JG. et al. 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. Med Biol Eng Comput 2019; 57: 677-687 DOI: 10.1007/s11517-018-1915-z.
- 23 Abramoff 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.
- 24 van der Heijden AA, Abramoff MD, Verbraak F. et al. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System. Acta Ophthalmol 2018; 96: 63-68 DOI: 10.1111/aos.13613.
- 25 Schlegl T, Waldstein SM, Bogunovic H. et al. Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning. Ophthalmology 2017; 125: 549-558 DOI: 10.1016/j.ophtha.2017.10.031.
- 26 U.S. Food & Drug Administration. Im Internet (Stand 04.10.2018): www.fda.gov/newsevents/newsroom/pressannouncements/ucm604357.html
- 27 Prahs P, Radeck V, Mayer C. et al. OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications. Graefes Arch Clin Exp Ophthalmol 2018; 256: 91-98 DOI: 10.1007/s00417-017-3839-y.
- 28 Fang L, Cunefare D, Wang C. et al. Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed Opt Express 2017; 8: 2732-2744 DOI: 10.1364/BOE.8.002732.
- 29 Liefers B, Venhuizen F, Schreur V. et al. Automatic detection of the foveal center in optical coherence tomography. Biomed Opt Express 2017; 8: 5160-5178 DOI: 10.1364/BOE.8.005160.
- 30 Venhuizen FG, van Ginneken B, Liefers B. et al. Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks. Biomed Opt Express 2017; 8: 3292-3316 DOI: 10.1364/BOE.8.003292.
- 31 Bogunovic H, Waldstein SM, Schlegl T. et al. Prediction of Anti-VEGF Treatment Requirements in Neovascular AMD Using a Machine Learning Approach. Invest Ophthalmol Vis Sci 2017; 58: 3240-3248 DOI: 10.1167/iovs.16-21053.
- 32 Burlina P, Joshi N, Pacheco K. et al. Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular Degeneration. JAMA ophthalmology 2018; 136: 1359-1366 DOI: 10.1001/jamaophthalmol.2018.4118.
- 33 Schmidt-Erfurth U, Waldstein S, Klimscha S. et al. Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence. Invest Ophthalmol Vis Sci 2018; 59: 3199-3208 DOI: 10.1167/iovs.18-24106.
- 34 Bogunovic H, Montuoro A, Baratsits M. et al. Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging. Invest Ophthalmol Vis Sci 2017; 58 BIO141-BIO150 DOI: 10.1167/iovs.17-21789.
- 35 Russakoff D, Lamin A, Oakley J. et al. Deep Learning for Prediction of AMD Progression: A Pilot Study. Invest Ophthalmol Vis Sci 2019; 20: 712-722 DOI: 10.1167/iovs.18-25325.
- 36 Garcia-Martin E, Herrero R, Bambo M. et al. Artificial neural network techniques to improve the ability of optical coherence tomography to detect optic neuritis. Semin Ophthalmol 2015; 30: 11-19 DOI: 10.3109/08820538.2013.810277.
- 37 Garcia-Martin E, Ortiz M, Boquete L. et al. Early diagnosis of multiple sclerosis by OCT analysis using Cohen's d method and a neural network as classifier. Comput Biol Med 2020; DOI: 10.1016/j.compbiomed.2020.104165.
- 38 Clark A, Ng J, Morlet N. et al. Big data and ophthalmic research. Surv Ophthalmol 2016; 61: 443-465 DOI: 10.1016/j.survophthal.2016.01.003.
- 39 Rim T, Cheng C, Kim D. et al. A nationwide cohort study of cigarette smoking and risk of neovascular age-related macular degeneration in East Asian men. Br J Ophthalmol 2017; 101: 1367-1373 DOI: 10.1136/bjophthalmol-2016-309952.
- 40 Rim T, Kim H, Kim J. et al. A Nationwide Cohort Study on the Association Between Past Physical Activity and Neovascular Age-Related Macular Degeneration in an East Asian Population. JAMA Ophthalmol 2018; 136: 132-139 DOI: 10.1001/jamaophthalmol.2017.5682.
- 41 Rim T, Lee C, Lee S. et al. Association between Previous Cataract Surgery and Age-Related Macular Degeneration. Semin Ophthalmol 2017; 32: 466-473 DOI: 10.3109/08820538.2015.1119861.
- 42 Rim T, Lee C, Lee S. et al. INTRAVITREAL RANIBIZUMAB THERAPY FOR NEOVASCULAR AGE-RELATED MACULAR DEGENERATION AND THE RISK OF STROKE: A National Sample Cohort Study. Retina 2016; 36: 2166-2174 DOI: 10.1097/IAE.0000000000001084.
- 43 Lee S, Kim S, Rim T. et al. Incidence, Comorbidity, and Mortality of Primary Congenital Glaucoma in Korea from 2001 to 2015: A Nationwide Population-based Study. Korean J Ophthalmol 2020; 34: 316-321 DOI: 10.3341/kjo.2020.0015.
- 44 Rough K, Thompson J. When Does Size Matter? Promises, Pitfalls, and Appropriate Interpretation of "Big" Medical Records Data. Ophthalmology 2018; 125: 1136-1138 DOI: 10.1016/j.ophtha.2018.04.034.
- 45 Gillies M, Walton R, Liong J. et al. Efficient capture of high-quality data on outcomes of treatment for macular diseases: the fight retinal blindness!. Project Retina 2014; 34: 188-195 DOI: 10.1097/IAE.0b013e318296b271.
- 46 Parke ID, Lum F, Rich W. The IRIS® Registry : Purpose and perspectives. Ophthalmologe 2017; 114: 1-6 DOI: 10.1007/s00347-016-0300-2.
- 47 Oregis. Im Internet (Stand 15.12.2020): www.oregis.de
- 48 Alnawaiseh M, Alten F, Huelsken G. et al. Implementierung einer elektronischen Patientenakte an einer deutschen Augenklinik der Maximalversorgung. Ophthalmologe 2015; 112: 337-345 DOI: 10.1007/s00347-014-3124-y.
- 49 Kortüm K, Müller M, Babenko A. et al. Entwicklung eines augenärztlichen klinischen Informationssystems für bettenführende Augenkliniken. Ophthalmologe 2015; 112: 995-1001 DOI: 10.1007/s00347-015-0072-0.
- 50 Kuchenbecker J, Behrens-Baumann W. Einsatz einer elektronischen Patientenakte (EPA) an der Universitätsaugenklinik Magdeburg. Ophthalmologe 2004; 101: 1214-1219 DOI: 10.1007/s00347-004-1048-7.
- 51 Spira-Eppig C, Eppig T, Bischof M. et al. Per aspera ad astra: Einführung einer elektronischen Patientenakte an einer Universitätsaugenklinik: Erfahrungen mit „FIDUS“ in der Klinik für Augenheilkunde am Universitätsklinikum des Saarlandes UKS. Ophthalmologe 2018; 115: 868-877 DOI: 10.1007/s00347-017-0588-6.
- 52 Kortüm K, Kern C, Meyer G. et al. Rahmenbedingungen zur Sammlung von „Real-Life“-Daten am Beispiel der Augenklinik der Universität München. Klin Monbl Augenheilkd 2017; 234: 1477-1482 DOI: 10.1055/s-0043-115900.
- 53 Johnston RLLA, Buckle M, Antcliff R. et al. UK Age-Related Macular Degeneration Electronic Medical Record System (AMD EMR) Users Group Report IV: Incidence of Blindness and Sight Impairment in Ranibizumab-Treated Patients. Ophthalmology 2016; 123: 2386-2392 DOI: 10.1016/j.ophtha.2016.07.037.
- 54 Lee A, Lee C, Butt T. et al. UK AMD EMR USERS GROUP REPORT V: benefits of initiating ranibizumab therapy for neovascular AMD in eyes with vision better than 6/12. Br J Ophthalmol 2015; 99: 1045-1050 DOI: 10.1136/bjophthalmol-2014-306229.
- 55 Denniston A, Chakravarthy U, Zhu H. et al. The UK Diabetic Retinopathy Electronic Medical Record (UK DR EMR) Users Group, Report 2: real-world data for the impact of cataract surgery on diabetic macular oedema. Br J Ophthalmol 2017; 101: 1673-1678 DOI: 10.1136/bjophthalmol-2016-309838.
- 56 Writing Committee for the UK Age-Related Macular Degeneration EMR Users Group. The neovascular age-related macular degeneration database: multicenter study of 92 976 ranibizumab injections: report 1: visual acuity. Ophthalmology 2014; 121: 1092-1101 DOI: 10.1016/j.ophtha.2013.11.031.
- 57 Gale R, Gill C, Pikoula M. et al. Multicentre study of 4626 patients assesses the effectiveness, safety and burden of two categories of treatments for central retinal vein occlusion: intravitreal anti-vascular endothelial growth factor injections and intravitreal Ozurdex injections. Br J Ophthalmol 2020; DOI: 10.1136/bjophthalmol-2020-317306.
- 58 Gräßel E, Donath C, Hollederer A. et al. Versorgungsforschung – evidenzbasiert: Ein Kurzüberblick und Implikationen. Gesundheitswesen 2015; 77: 193-199 DOI: 10.1055/s-0034-1382042.
- 59 Ziemssen F, Eter N, Fauser S. et al. Retrospektive Untersuchung der Anti-VEGF-Behandlungsrealität und Wirksamkeit bei Patienten mit neovaskulärer altersabhängiger Makuladegeneration (nAMD) in Deutschland. Ophthalmologe 2015; 112: 246-254 DOI: 10.1007/s00347-014-3217-7.
- 60 Treder M, Gaber A, Rudloff B. et al. Real-Life-Daten-Analyse der Therapiequalität bei Patienten mit exsudativer altersabhängiger Makuladegeneration (AMD) und venösen Gefäßverschlüssen an einer deutschen Universitätsaugenklinik. Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft 2018; DOI: 10.1007/s00347-018-0746-5.
- 61 Wecker T, Ehlken C, Bühler A. et al. Five-year visual acuity outcomes and injection patterns in patients with pro-re-nata treatments for AMD, DME, RVO and myopic CNV. Br J Ophthalmol 2017; 101: 353-359