Chancen von künstlicher Intelligenz und Big Data für die Diagnostik und Behandlung der altersabhängigen Makuladegeneration

Zusammenfassung

Die altersabhängige Makuladegeneration (AMD) ist der führende Grund für Erblindung in der westlichen Welt. Für die neovaskuläre Verlaufsform steht mit der intravitrealen Anti-VEGF-Injektion (VEGF: vascular endothelial growth factor) eine wirksame Therapie zur Verfügung. In den letzten Jahren haben die multimodale Bildgebung und die standardisierte elektronische Patientendokumentation geholfen, die Diagnostik und das Management der AMD-Patienten zu verbessern. Mit dem Aufkommen der künstlichen Intelligenz und Big Data ergeben sich in diesem Zusammenhang viele Chancen für die Zukunft. Dieser Artikel soll einen Überblick über mögliche Anwendungen geben.

Abstract

Age-related macular degeneration (AMD) is the leading cause of blindness in the western world. Intravitreal injection of anti-vascular endothelial growth factor (anti-VEGF) is an effective therapy of the neovascular form of this condition. Multimodal imaging and standardised electronic patient documentation have helped to improve the diagnosis and management of AMD patients recent years. With the advent of artificial intelligence and big data, there are many opportunities for the future. This article is intended to give an overview of possible applications.

Schlüsselwörter

altersbedingte Makuladegeneration - künstliche Intelligenz - Big Data - Deep Learning - maschinelles Lernen

Key words

age-related macular degeneration - artificial intelligence - big data - deep learning - machine learning

• Literatur

• 1 Finger R, Fimmers R, Holz F. et al. Prevalence and causes of registered blindness in the largest federal state of Germany. Br J Ophthalmol 2011; 95: 1061-1067
• 2 Wolfram C, Pfeiffer N. Der Bedarf Ophthalmologischer Versorgung. In: Deutsche Ophthalmologische Gesellschaft. Hrsg. Weißbuch zur Situation der ophthalmologischen Versorgung in Deutschland. München: Deutsche Ophthalmologische Gesellschaft; 2012: 5-10
• 3 Kanagasingam Y, Bhuiyan A, Abràmoff M. et al. Progress on retinal image analysis for age related macular degeneration. Prog Retin Eye Res 2014; 38: 20-42
• 4 Deutsche Ophthalmologische Gesellschaft. Die Anti-VEGF-Therapie bei der neovaskulären altersabhängigen Makuladegeneration – therapeutische Strategien. Stellungnahme der Deutschen Ophthalmologischen Gesellschaft, der Retinologischen Gesellschaft und des Berufsverbandes der Augenärzte Deutschlands – November 2014. Ophthalmologe 2014; 112: 237-245
• 5 Spaide R, Fujimoto J, Waheed N. et al. Optical coherence tomography angiography. Prog Retin Eye Res 2018; 64: 1-55
• 6 Garrity S, Sarraf D, Freund K. et al. Multimodal imaging of nonneovascular age-related macular degeneration. Invest Ophthalmol Vis Sci 2018; 59: AMD48-AMD64 doi:10.1167/iovs.18-24158
• 7 Schmidt-Erfurth U, Sadeghipour A, Gerendas B. et al. Artificial intelligence in retina. Prog Retin Eye Res 2018; 67: 1-29
• 8 Clark A, Ng J, Morlet N. et al. Big data and ophthalmic research. Surv Ophthalmol 2016; 61: 443-465
• 9 Parke II DW, Lum F, Rich WL. The IRIS^® Registry: Purpose and perspectives. Ophthalmologe 2017; 114 (Suppl. 01) S1-S6
• 10 Gillies MC, 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
• 11 Huang D, Swanson EA, Lin CP. et al. Optical coherence tomography. Science 1991; 254: 1178-1181
• 12 Alnawaiseh M, Alten F, Huelsken G. et al. Implementierung einer elektronischen Patientenakte an einer deutschen Augenklinik der Maximalversorgung. Ophthalmologe 2015; 112: 337-345
• 13 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
• 14 Kortüm K, Müller M, Babenko A. et al. Entwicklung eines augenärztlichen klinischen Informationssystems für bettenführende Augenkliniken. Ophthalmologe 2015; 112: 995-1001
• 15 Kuchenbecker J, Behrens-Baumann W. Einsatz einer elektronischen Patientenakte (EPA) an der Universitätsaugenklinik Magdeburg. Ophthalmologe 2004; 101: 1214-1219
• 16 Kortüm K, Kern C, Meyer G. et al. Rahmenbedingungen zur Sammlung von „Real-Life“-Daten am Beispiel der Augenklinik der Universität München. Klin Monatsbl Augenheilkd 2017; 234: 1477-1482
• 17 Rough K, Thompson JT. When does size matter? Promises, pitfalls, and appropriate interpretation of “big” medical records data. Ophthalmology 2018; 125: 1136-1138
• 18 Rim TH, Lee CS, Lee SC. 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
• 19 Rim TH, Cheng CY, Kim DW. 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
• 20 Rim TH, Kim HK, Kim JW. 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
• 21 Rim TH, Lee CS, Lee SC. et al. Association between previous cataract surgery and age-related macular degeneration. Semin Ophthalmol 2017; 32: 466-473
• 22 Wu CM, Su FH, Wang WC. et al. Association of chronic hepatitis B virus infection with age-related macular degeneration. Acta Ophthalmol 2019; 97: e713-e718 doi:10.1111/aos.14032
• 23 Mehta H, Tufail A, Daien V. et al. Real-world outcomes in patients with neovascular age-related macular degeneration treated with intravitreal vascular endothelial growth factor inhibitors. Prog Retin Eye Res 2018; 65: 127-146
• 24 Gräßel E, Donath C, Hollederer A. et al. Versorgungsforschung – evidenzbasiert: Ein Kurzüberblick und Implikationen. Gesundheitswesen 2015; 77: 193-199
• 25 Catt Research Group. Martin DF, Maguire MG. et al. Ranibizumab and bevacizumab for neovascular age-related macular degeneration. N Engl J Med 2011; 364: 1897-1908 doi:10.1056/NEJMoa1102673
• 26 Comparison of Age-related Macular Degeneration Treatments Trials (CATT) Research Group. Martin DF, Maguire MG. et al. Ranibizumab and bevacizumab for treatment of neovascular age-related macular degeneration: two-year results. Ophthalmology 2012; 119: 1388-1398 doi:10.1016/j.ophtha.2012.03.053
• 27 Chakravarthy U, Harding S, Rogers C. et al. Alternative treatments to inhibit VEGF in age-related choroidal neovascularisation: 2-year findings of the IVAN randomised controlled trial. Lancet 2013; 382: 1258-1267
• 28 IVAN Study Investigators. Chakravarthy U, Harding SP. et al. Ranibizumab versus bevacizumab to treat neovascular age-related macular degeneration: one-year findings from the IVAN randomized trial. Ophthalmology 2012; 119: 1399-1411 doi:10.1016/j.ophtha.2012.04.015
• 29 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
• 30 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. Ophthalmologe 2019; 116: 553-562
• 31 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
• 32 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
• 33 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
• 34 Johnston RL, Lee AY, Buckle M. 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
• 35 Treder M, Eter N. „Deep Learning“ und neuronale Netzwerke in der Augenheilkunde: Einsatzmöglichkeiten im Bereich der optischen Kohärenztomographie. Ophthalmologe 2018; 115: 714-721
• 36 Age-Related Eye Disease Study Research Group. The Age-Related Eye Disease Study system for classifying age-related macular degeneration from stereoscopic color fundus photographs: the Age-Related Eye Disease Study Report Number 6. Am J Ophthalmol 2001; 132: 668-681
• 37 Burlina P, Pacheco K, 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
• 38 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
• 39 Russakovsky O, Deng J, Su H. et al. ImageNet large scale visual recognition challenge. Int J Comput Vis 2015; 15: 211-252
• 40 Lee CS, Baughman DM, Lee AY. Deep learning is effective for the classification of OCT images of normal versus age-related macular degeneration. Ophthalmol Retina 2017; 1: 322-327
• 41 Venhuizen FG, van Ginneken B, van Asten F. et al. Automated staging of age-related macular degeneration using optical coherence tomography. Invest Ophthalmol Vis Sci 2017; 58: 2318-2328
• 42 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
• 43 Hwang DK, Hsu CC, Chang KJ. et al. Artificial intelligence-based decision-making for age-related macular degeneration. Theranostics 2019; 9: 232-245
• 44 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
• 45 Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng 2000; 2: 315-337
• 46 Lauermann JL, Woetzel AK, Treder M. et al. Prevalences of segmentation errors and motion artifacts in OCT-angiography differ among retinal diseases. Graefes Arch Clin Exp Ophthalmol 2018; 256: 1807-1816
• 47 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
• 48 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
• 49 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
• 50 Lauermann J, Treder M, Alnawaiseh M. et al. Automated OCT angiography image quality assessment using a deep learning algorithm. Graefes Arch Clin Exp Ophthalmol 2019; 257: 1641-1648
• 51 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
• 52 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
• 53 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
• 54 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
• 55 Aslam TM, Zaki HR, Mahmood S. et al. Use of a neural net to model the impact of optical coherence tomography abnormalities on vision in age-related macular degeneration. Am J Ophthalmol 2018; 185: 94-100
• 56 Rohm M, Tresp V, Müller M. et al. Predicting visual acuity by using machine learning in patients treated for neovascular age-related macular degeneration. Ophthalmology 2018; 125: 1028-1036
• 57 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
• 58 Bindewald A, Schmitz-Valckenberg S, Jorzik J. et al. Classification of abnormal fundus autofluorescence patterns in the junctional zone of geographic atrophy in patients with age related macular degeneration. Br J Ophthalmol 2005; 89: 874-878
• 59 Holz FG, Bindewald-Wittich A, Fleckenstein M. et al. Progression of geographic atrophy and impact of fundus autofluorescence patterns in age-related macular degeneration. Am J Ophthalmol 2007; 143: 463-472