PDF herunterladen
Klin Monbl Augenheilkd 2019; 236(12): 1418-1422
DOI: 10.1055/a-1012-2036
Übersicht
Georg Thieme Verlag KG Stuttgart · New York

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

Chances of Artificial Intelligence and Big Data for the Diagnosis and Treatment of Age-related Macular Degeneration
Maximilian Treder
Klinik für Augenheilkunde, Universitätsklinikum Münster
,
Nicole Eter
Klinik für Augenheilkunde, Universitätsklinikum Münster
› Institutsangaben
Weitere Informationen

Publikationsverlauf

eingereicht 06. August 2019

akzeptiert 06. September 2019

Publikationsdatum:
31. Oktober 2019 (online)

Auch verfügbar auf
    Lizenzen und Reprints

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
    CrossrefPubMedSuche in Google Scholar
  • 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
    Suche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    PubMedSuche in Google Scholar
  • 5 Spaide R, Fujimoto J, Waheed N. et al. Optical coherence tomography angiography. Prog Retin Eye Res 2018; 64: 1-55
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 7 Schmidt-Erfurth U, Sadeghipour A, Gerendas B. et al. Artificial intelligence in retina. Prog Retin Eye Res 2018; 67: 1-29
    CrossrefPubMedSuche in Google Scholar
  • 8 Clark A, Ng J, Morlet N. et al. Big data and ophthalmic research. Surv Ophthalmol 2016; 61: 443-465
    CrossrefPubMedSuche in Google Scholar
  • 9 Parke II DW, Lum F, Rich WL. The IRIS® Registry: Purpose and perspectives. Ophthalmologe 2017; 114 (Suppl. 01) S1-S6
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 11 Huang D, Swanson EA, Lin CP. et al. Optical coherence tomography. Science 1991; 254: 1178-1181
    CrossrefPubMedSuche in Google Scholar
  • 12 Alnawaiseh M, Alten F, Huelsken G. et al. Implementierung einer elektronischen Patientenakte an einer deutschen Augenklinik der Maximalversorgung. Ophthalmologe 2015; 112: 337-345
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 14 Kortüm K, Müller M, Babenko A. et al. Entwicklung eines augenärztlichen klinischen Informationssystems für bettenführende Augenkliniken. Ophthalmologe 2015; 112: 995-1001
    CrossrefPubMedSuche in Google Scholar
  • 15 Kuchenbecker J, Behrens-Baumann W. Einsatz einer elektronischen Patientenakte (EPA) an der Universitätsaugenklinik Magdeburg. Ophthalmologe 2004; 101: 1214-1219
    CrossrefPubMedSuche in Google Scholar
  • 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
    Thieme ConnectPubMedSuche in Google Scholar
  • 17 Rough K, Thompson JT. When does size matter? Promises, pitfalls, and appropriate interpretation of “big” medical records data. Ophthalmology 2018; 125: 1136-1138
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 24 Gräßel E, Donath C, Hollederer A. et al. Versorgungsforschung – evidenzbasiert: Ein Kurzüberblick und Implikationen. Gesundheitswesen 2015; 77: 193-199
    Thieme ConnectPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    PubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 35 Treder M, Eter N. „Deep Learning“ und neuronale Netzwerke in der Augenheilkunde: Einsatzmöglichkeiten im Bereich der optischen Kohärenztomographie. Ophthalmologe 2018; 115: 714-721
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 39 Russakovsky O, Deng J, Su H. et al. ImageNet large scale visual recognition challenge. Int J Comput Vis 2015; 15: 211-252
    PubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    PubMedSuche in Google Scholar
  • 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
    PubMedSuche in Google Scholar
  • 43 Hwang DK, Hsu CC, Chang KJ. et al. Artificial intelligence-based decision-making for age-related macular degeneration. Theranostics 2019; 9: 232-245
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 45 Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng 2000; 2: 315-337
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    PubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar