Skip to main content
Erschienen in: Die Ophthalmologie 10/2020

08.05.2020 | Makuladegeneration | Leitthema

Künstliche Intelligenz zum Management von Makulaödemen

Chancen und Herausforderungen

verfasst von: PD Dr. med. habil. M. Treder, R. Diener, N. Eter

Erschienen in: Die Ophthalmologie | Ausgabe 10/2020

Einloggen, um Zugang zu erhalten

Zusammenfassung

Makulaödeme treten bei einer Vielzahl von ophthalmologischen Krankheitsbildern auf. Ihre Diagnostik und Therapie sind wichtiger Teil der heutigen Augenheilkunde. Durch die stetige Weiterentwicklung bietet künstliche Intelligenz (KI) viele Chancen, das Management von Makulaödemen zu verbessern. Dieser Beitrag soll dem Leser einen Überblick über dieses interessante Thema geben.
Literatur
1.
Zurück zum Zitat Abramoff MD, Lou Y, Erginay A et al (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57:5200–5206PubMed Abramoff MD, Lou Y, Erginay A et al (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57:5200–5206PubMed
2.
Zurück zum Zitat Akram MU, Tariq A, Khan SA et al (2014) Automated detection of exudates and macula for grading of diabetic macular edema. Comput Methods Programs Biomed 114:141–152PubMed Akram MU, Tariq A, Khan SA et al (2014) Automated detection of exudates and macula for grading of diabetic macular edema. Comput Methods Programs Biomed 114:141–152PubMed
3.
Zurück zum Zitat Alsaih K, Lemaitre G, Rastgoo M et al (2017) Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images. BioMed Eng OnLine 16:68PubMedPubMedCentral Alsaih K, Lemaitre G, Rastgoo M et al (2017) Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images. BioMed Eng OnLine 16:68PubMedPubMedCentral
4.
Zurück zum Zitat Arcadu F, Benmansour F, Maunz A et al (2019) Deep learning predicts OCT measures of diabetic macular thickening from color fundus photographs. Invest Ophthalmol Vis Sci 60:852–857PubMed Arcadu F, Benmansour F, Maunz A et al (2019) Deep learning predicts OCT measures of diabetic macular thickening from color fundus photographs. Invest Ophthalmol Vis Sci 60:852–857PubMed
5.
Zurück zum Zitat Bogunovic H, Waldstein S, Schlegl T et al (2017) Prediction of anti-VEGF treatment requirements in neovascular AMD using a machine learning approach. Invest Ophthalmol Vis Sci 58:3240–3248PubMed Bogunovic H, Waldstein S, Schlegl T et al (2017) Prediction of anti-VEGF treatment requirements in neovascular AMD using a machine learning approach. Invest Ophthalmol Vis Sci 58:3240–3248PubMed
6.
Zurück zum Zitat Breger A, Ehler M, Bogunovic H et al (2017) Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images. Eye 31:1212–1220PubMedPubMedCentral Breger A, Ehler M, Bogunovic H et al (2017) Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images. Eye 31:1212–1220PubMedPubMedCentral
7.
Zurück zum Zitat Chan G, Kamble R, Muller H et al (2018) Fusing results of several deep learning architectures for automatic classification of normal and diabetic macular edema in optical coherence tomography. Conf Proc IEEE Eng Med Biol Soc 2018:670–673 Chan G, Kamble R, Muller H et al (2018) Fusing results of several deep learning architectures for automatic classification of normal and diabetic macular edema in optical coherence tomography. Conf Proc IEEE Eng Med Biol Soc 2018:670–673
8.
Zurück zum Zitat Cunha-Vaz J, Bernardes R, Lobo C (2011) Blood-retinal barrier. Eur J Ophthalmol 21(Suppl 6):S3–S9PubMed Cunha-Vaz J, Bernardes R, Lobo C (2011) Blood-retinal barrier. Eur J Ophthalmol 21(Suppl 6):S3–S9PubMed
9.
Zurück zum Zitat Fang L, Cunefare D, Wang C et al (2017) Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed Opt Express 8:2732–2744PubMedPubMedCentral Fang L, Cunefare D, Wang C et al (2017) Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed Opt Express 8:2732–2744PubMedPubMedCentral
10.
Zurück zum Zitat Fekrat S, Sadda S, Blodi B (2019) Exploring the role of reading centers in the era of artificial intelligence. Retin Times 36(3):52–56 Fekrat S, Sadda S, Blodi B (2019) Exploring the role of reading centers in the era of artificial intelligence. Retin Times 36(3):52–56
11.
Zurück zum Zitat Gerendas BS, Bogunovic H, Sadeghipour A et al (2017) Computational image analysis for prognosis determination in DME. Vision Res 139:204–210PubMed Gerendas BS, Bogunovic H, Sadeghipour A et al (2017) Computational image analysis for prognosis determination in DME. Vision Res 139:204–210PubMed
12.
Zurück zum Zitat Haubold J (2020) Künstliche Intelligenz in der Radiologie: Was ist in den nächsten Jahren zu erwarten? Radiologe 60:64–69PubMed Haubold J (2020) Künstliche Intelligenz in der Radiologie: Was ist in den nächsten Jahren zu erwarten? Radiologe 60:64–69PubMed
13.
Zurück zum Zitat Hecht I, Bar A, Rokach L et al (2018) Optical coherence tomography biomarkers to distinguish diabetic MacUlar edema from pseudophakic cystoid MacUlar edema using MacHine learning algorithms. Retina 39:2283–2291 Hecht I, Bar A, Rokach L et al (2018) Optical coherence tomography biomarkers to distinguish diabetic MacUlar edema from pseudophakic cystoid MacUlar edema using MacHine learning algorithms. Retina 39:2283–2291
14.
Zurück zum Zitat Hwang DK, Hsu CC, Chang KJ et al (2019) Artificial intelligence-based decision-making for age-related macular degeneration. Theranostics 9:232–245PubMedPubMedCentral Hwang DK, Hsu CC, Chang KJ et al (2019) Artificial intelligence-based decision-making for age-related macular degeneration. Theranostics 9:232–245PubMedPubMedCentral
15.
Zurück zum Zitat Katzenmeier C (2019) Big Data, E‑Health, M‑Health, KI und Robotik in der Medizin. MedR 37:259–271 Katzenmeier C (2019) Big Data, E‑Health, M‑Health, KI und Robotik in der Medizin. MedR 37:259–271
16.
Zurück zum Zitat Lauermann JL, Woetzel AK, Treder M et al (2018) Prevalences of segmentation errors and motion artifacts in OCT-angiography differ among retinal diseases. Graefes Arch Clin Exp Ophthalmol 256:1807–1816PubMed Lauermann JL, Woetzel AK, Treder M et al (2018) Prevalences of segmentation errors and motion artifacts in OCT-angiography differ among retinal diseases. Graefes Arch Clin Exp Ophthalmol 256:1807–1816PubMed
17.
Zurück zum Zitat Lee CS, Tyring AJ, Deruyter NP et al (2017) Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed Opt Express 8:3440–3448PubMedPubMedCentral Lee CS, Tyring AJ, Deruyter NP et al (2017) Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed Opt Express 8:3440–3448PubMedPubMedCentral
18.
Zurück zum Zitat Li F, Chen H, Liu Z et al (2019) Fully automated detection of retinal disorders by image-based deep learning. Graefes Arch Clin Exp Ophthalmol 257:495–505PubMed Li F, Chen H, Liu Z et al (2019) Fully automated detection of retinal disorders by image-based deep learning. Graefes Arch Clin Exp Ophthalmol 257:495–505PubMed
19.
Zurück zum Zitat Liefers B, Venhuizen F, Schreur V et al (2017) Automatic detection of the foveal center in optical coherence tomography. Biomed Opt Express 8:5160–5178PubMedPubMedCentral Liefers B, Venhuizen F, Schreur V et al (2017) Automatic detection of the foveal center in optical coherence tomography. Biomed Opt Express 8:5160–5178PubMedPubMedCentral
20.
Zurück zum Zitat Montuoro A, Waldstein SM, Gerendas BS et al (2017) Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context. Biomed Opt Express 8:1874–1888PubMedPubMedCentral Montuoro A, Waldstein SM, Gerendas BS et al (2017) Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context. Biomed Opt Express 8:1874–1888PubMedPubMedCentral
21.
Zurück zum Zitat Murugeswari S, Sukanesh R (2017) Investigations of severity level measurements for diabetic macular oedema using machine learning algorithms. Ir J Med Sci 186:929–938PubMed Murugeswari S, Sukanesh R (2017) Investigations of severity level measurements for diabetic macular oedema using machine learning algorithms. Ir J Med Sci 186:929–938PubMed
22.
Zurück zum Zitat Pham D, Xu C, Prince JL (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2:315–337PubMed Pham D, Xu C, Prince JL (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2:315–337PubMed
23.
Zurück zum Zitat Prahs P, Radeck V, Mayer C et al (2018) OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications. Graefes Arch Clin Exp Ophthalmol 256:91–98PubMed Prahs P, Radeck V, Mayer C et al (2018) OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications. Graefes Arch Clin Exp Ophthalmol 256:91–98PubMed
24.
Zurück zum Zitat Ren F, Cao P, Zhao D et al (2018) Diabetic macular edema grading in retinal images using vector quantization and semi-supervised learning. Technol Health Care 26:389–397PubMedPubMedCentral Ren F, Cao P, Zhao D et al (2018) Diabetic macular edema grading in retinal images using vector quantization and semi-supervised learning. Technol Health Care 26:389–397PubMedPubMedCentral
25.
Zurück zum Zitat Rohm M, Tresp V, Müller M et al (2018) Predicting visual acuity by using machine learning in patients treated for neovascular age-related macular degeneration. Ophthalmology 125:1028–1036PubMed Rohm M, Tresp V, Müller M et al (2018) Predicting visual acuity by using machine learning in patients treated for neovascular age-related macular degeneration. Ophthalmology 125:1028–1036PubMed
26.
Zurück zum Zitat Roy AG, Conjeti S, Karri SPK et al (2017) ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed Opt Express 8:3627–3642PubMedPubMedCentral Roy AG, Conjeti S, Karri SPK et al (2017) ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed Opt Express 8:3627–3642PubMedPubMedCentral
27.
Zurück zum Zitat Schlegl T, Waldstein SM, Bogunovic H et al (2017) Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology 125:549–558PubMed Schlegl T, Waldstein SM, Bogunovic H et al (2017) Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology 125:549–558PubMed
28.
Zurück zum Zitat Schmidt-Erfurth U, Bogunovic H, Sadeghipour A et al (2018) Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration. Ophthalmol Retin 2:24–30 Schmidt-Erfurth U, Bogunovic H, Sadeghipour A et al (2018) Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration. Ophthalmol Retin 2:24–30
29.
Zurück zum Zitat Schmidt-Erfurth U, Sadeghipour A, Gerendas B et al (2018) Artificial intelligence in retina. Prog Retin Eye Res 67:1–29PubMed Schmidt-Erfurth U, Sadeghipour A, Gerendas B et al (2018) Artificial intelligence in retina. Prog Retin Eye Res 67:1–29PubMed
30.
Zurück zum Zitat Schwartz R, Loewenstein A (2015) Early detection of age related macular degeneration: current status. Int J Retin Vitreous Dec 1:1–20 Schwartz R, Loewenstein A (2015) Early detection of age related macular degeneration: current status. Int J Retin Vitreous Dec 1:1–20
31.
Zurück zum Zitat Soubrane G (2017) Macular edema of choroidal origin. Dev Ophthalmol 58:202–219PubMed Soubrane G (2017) Macular edema of choroidal origin. Dev Ophthalmol 58:202–219PubMed
32.
Zurück zum Zitat Sun Y, Li S, Sun Z (2017) Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning. J Biomed Opt 22:16012PubMed Sun Y, Li S, Sun Z (2017) Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning. J Biomed Opt 22:16012PubMed
33.
Zurück zum Zitat Treder M, Eter N (2018) „Deep Learning“ und neuronale Netzwerke in der Augenheilkunde. Ophthalmologe 115:714–721PubMed Treder M, Eter N (2018) „Deep Learning“ und neuronale Netzwerke in der Augenheilkunde. Ophthalmologe 115:714–721PubMed
34.
Zurück zum Zitat Treder M, Eter N (2019) Chancen von künstlicher Intelligenz und Big Data für die Diagnostik und Behandlung der altersabhängigen Makuladegeneration. Klin Monbl Augenheilkd 236:1418–1422PubMed Treder M, Eter N (2019) Chancen von künstlicher Intelligenz und Big Data für die Diagnostik und Behandlung der altersabhängigen Makuladegeneration. Klin Monbl Augenheilkd 236:1418–1422PubMed
35.
Zurück zum Zitat Treder M, Lauermann JL, Eter N (2018) Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning. Graefes Arch Clin Exp Ophthalmol 256:259–265PubMed Treder M, Lauermann JL, Eter N (2018) Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning. Graefes Arch Clin Exp Ophthalmol 256:259–265PubMed
37.
Zurück zum Zitat Van Der Heijden AA, Abramoff MD, Verbraak F et al (2018) Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn diabetes care system. Acta Ophthalmol 96:63–68 Van Der Heijden AA, Abramoff MD, Verbraak F et al (2018) Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn diabetes care system. Acta Ophthalmol 96:63–68
38.
Zurück zum Zitat Venhuizen FG, Van Ginneken B, Liefers B et al (2017) Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks. Biomed Opt Express 8:3292–3316PubMedPubMedCentral Venhuizen FG, Van Ginneken B, Liefers B et al (2017) Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks. Biomed Opt Express 8:3292–3316PubMedPubMedCentral
39.
Zurück zum Zitat Vogl W, Waldstein S, Gerendas B et al (2017) Analyzing and predicting visual acuity outcomes of anti-VEGF therapy by a longitudinal mixed effects model of imaging and clinical data. Invest Ophthalmol Vis Sci 58:4173–4181PubMed Vogl W, Waldstein S, Gerendas B et al (2017) Analyzing and predicting visual acuity outcomes of anti-VEGF therapy by a longitudinal mixed effects model of imaging and clinical data. Invest Ophthalmol Vis Sci 58:4173–4181PubMed
40.
Zurück zum Zitat Vogl W, Waldstein S, Gerendas B et al (2017) Predicting macular edema recurrence from spatio-temporal signatures in optical coherence tomography images. IEEE Trans Med Imaging 36:1773–1783PubMed Vogl W, Waldstein S, Gerendas B et al (2017) Predicting macular edema recurrence from spatio-temporal signatures in optical coherence tomography images. IEEE Trans Med Imaging 36:1773–1783PubMed
41.
Zurück zum Zitat Wang Y, Zhang Y, Yao Z et al (2016) Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images. Biomed Opt Express 7:4928–4940PubMedPubMedCentral Wang Y, Zhang Y, Yao Z et al (2016) Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images. Biomed Opt Express 7:4928–4940PubMedPubMedCentral
42.
Zurück zum Zitat Zou X, Zhao X, Yang Y et al (2016) Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image. Comput Intell Neurosci 2016:7496735PubMedPubMedCentral Zou X, Zhao X, Yang Y et al (2016) Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image. Comput Intell Neurosci 2016:7496735PubMedPubMedCentral
Metadaten
Titel
Künstliche Intelligenz zum Management von Makulaödemen
Chancen und Herausforderungen
verfasst von
PD Dr. med. habil. M. Treder
R. Diener
N. Eter
Publikationsdatum
08.05.2020
Verlag
Springer Medizin
Erschienen in
Die Ophthalmologie / Ausgabe 10/2020
Print ISSN: 2731-720X
Elektronische ISSN: 2731-7218
DOI
https://doi.org/10.1007/s00347-020-01110-9

Weitere Artikel der Ausgabe 10/2020

Die Ophthalmologie 10/2020 Zur Ausgabe

Neu im Fachgebiet Augenheilkunde

Update Augenheilkunde

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.