Automated Staging of Age-Related Macular Degeneration Using Optical Coherence Tomography
Purpose: To evaluate a machine learning algorithm that automatically grades age-related macular degeneration (AMD) severity stages from optical coherence tomography (OCT) scans.
Methods: A total of 3265 OCT scans from 1016 patients with either no signs of AMD or with signs of early, intermediate, or advanced AMD were randomly selected from a large European multicenter database. A machine learning system was developed to automatically grade unseen OCT scans into different AMD severity stages without requiring retinal layer segmentation. The ability of the system to identify high-risk AMD stages and to assign the correct severity stage was determined by using receiver operator characteristic (ROC) analysis and Cohen's κ statistics (κ), respectively. The results were compared to those of two human observers. Reproducibility was assessed in an independent, publicly available data set of 384 OCT scans.
Results: The system achieved an area under the ROC curve of 0.980 with a sensitivity of 98.2% at a specificity of 91.2%. This compares favorably with the performance of human observers who achieved sensitivities of 97.0% and 99.4% at specificities of 89.7% and 87.2%, respectively. A good level of agreement with the reference was obtained (κ = 0.713) and was in concordance with the human observers (κ = 0.775 and κ = 0.755, respectively).
Conclusions: A machine learning system capable of automatically grading OCT scans into AMD severity stages was developed and showed similar performance as human observers. The proposed automatic system allows for a quick and reliable grading of large quantities of OCT scans, which could increase the efficiency of large-scale AMD studies and pave the way for AMD screening using OCT.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2017 |
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| Erschienen: |
2017 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:58 |
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| Enthalten in: |
Investigative ophthalmology & visual science - 58(2017), 4 vom: 01. Apr., Seite 2318-2328 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Venhuizen, Freerk G [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 19.07.2017 Date Revised 18.01.2018 published: Print Citation Status MEDLINE |
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| doi: |
10.1167/iovs.16-20541 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM271239166 |
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| 500 | |a Date Revised 18.01.2018 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Purpose: To evaluate a machine learning algorithm that automatically grades age-related macular degeneration (AMD) severity stages from optical coherence tomography (OCT) scans | ||
| 520 | |a Methods: A total of 3265 OCT scans from 1016 patients with either no signs of AMD or with signs of early, intermediate, or advanced AMD were randomly selected from a large European multicenter database. A machine learning system was developed to automatically grade unseen OCT scans into different AMD severity stages without requiring retinal layer segmentation. The ability of the system to identify high-risk AMD stages and to assign the correct severity stage was determined by using receiver operator characteristic (ROC) analysis and Cohen's κ statistics (κ), respectively. The results were compared to those of two human observers. Reproducibility was assessed in an independent, publicly available data set of 384 OCT scans | ||
| 520 | |a Results: The system achieved an area under the ROC curve of 0.980 with a sensitivity of 98.2% at a specificity of 91.2%. This compares favorably with the performance of human observers who achieved sensitivities of 97.0% and 99.4% at specificities of 89.7% and 87.2%, respectively. A good level of agreement with the reference was obtained (κ = 0.713) and was in concordance with the human observers (κ = 0.775 and κ = 0.755, respectively) | ||
| 520 | |a Conclusions: A machine learning system capable of automatically grading OCT scans into AMD severity stages was developed and showed similar performance as human observers. The proposed automatic system allows for a quick and reliable grading of large quantities of OCT scans, which could increase the efficiency of large-scale AMD studies and pave the way for AMD screening using OCT | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Multicenter Study | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a van Ginneken, Bram |e verfasserin |4 aut | |
| 700 | 1 | |a van Asten, Freekje |e verfasserin |4 aut | |
| 700 | 1 | |a van Grinsven, Mark J J P |e verfasserin |4 aut | |
| 700 | 1 | |a Fauser, Sascha |e verfasserin |4 aut | |
| 700 | 1 | |a Hoyng, Carel B |e verfasserin |4 aut | |
| 700 | 1 | |a Theelen, Thomas |e verfasserin |4 aut | |
| 700 | 1 | |a Sánchez, Clara I |e verfasserin |4 aut | |
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