Computer-aided detection and abnormality score for the outer retinal layer in optical coherence tomography
© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ..
BACKGROUND: To develop computer-aided detection (CADe) of ORL abnormalities in the retinal pigmented epithelium, interdigitation zone and ellipsoid zone via optical coherence tomography (OCT).
METHODS: In this retrospective study, healthy participants with normal ORL, and patients with abnormality of ORL including choroidal neovascularisation (CNV) or retinitis pigmentosa (RP) were included. First, an automatic segmentation deep learning (DL) algorithm, CADe, was developed for the three outer retinal layers using 120 handcraft masks of ORL. This automatic segmentation algorithm generated 4000 segmentations, which included 2000 images with normal ORL and 2000 (1000 CNV and 1000 RP) images with focal or wide defects in ORL. Second, based on the automatically generated segmentation images, a binary classifier (normal vs abnormal) was developed. Results were evaluated by area under the receiver operating characteristic curve (AUC).
RESULTS: The DL algorithm achieved an AUC of 0.984 (95% CI 0.976 to 0.993) for individual image evaluation in the internal test set of 797 images. In addition, performance analysis of a publicly available external test set (n=968) had an AUC of 0.957 (95% CI 0.944 to 0.970) and a second clinical external test set (n=1124) had an AUC of 0.978 (95% CI 0.970 to 0.986). Moreover, the CADe highlighted well normal parts of ORL and omitted highlights in abnormal ORLs of CNV and RP.
CONCLUSION: The CADe can use OCT images to segment ORL and differentiate between normal ORL and abnormal ORL. The CADe classifier also performs visualisation and may aid future physician diagnosis and clinical applications.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:106 |
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| Enthalten in: |
The British journal of ophthalmology - 106(2022), 9 vom: 19. Sept., Seite 1301-1307 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Rim, Tyler Hyungtaek [VerfasserIn] |
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| Links: |
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| Themen: |
Epidemiology |
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| Anmerkungen: |
Date Completed 23.08.2022 Date Revised 20.09.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1136/bjophthalmol-2020-317817 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM324300379 |
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| 100 | 1 | |a Rim, Tyler Hyungtaek |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Computer-aided detection and abnormality score for the outer retinal layer in optical coherence tomography |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
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| 500 | |a Date Completed 23.08.2022 | ||
| 500 | |a Date Revised 20.09.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ. | ||
| 520 | |a BACKGROUND: To develop computer-aided detection (CADe) of ORL abnormalities in the retinal pigmented epithelium, interdigitation zone and ellipsoid zone via optical coherence tomography (OCT) | ||
| 520 | |a METHODS: In this retrospective study, healthy participants with normal ORL, and patients with abnormality of ORL including choroidal neovascularisation (CNV) or retinitis pigmentosa (RP) were included. First, an automatic segmentation deep learning (DL) algorithm, CADe, was developed for the three outer retinal layers using 120 handcraft masks of ORL. This automatic segmentation algorithm generated 4000 segmentations, which included 2000 images with normal ORL and 2000 (1000 CNV and 1000 RP) images with focal or wide defects in ORL. Second, based on the automatically generated segmentation images, a binary classifier (normal vs abnormal) was developed. Results were evaluated by area under the receiver operating characteristic curve (AUC) | ||
| 520 | |a RESULTS: The DL algorithm achieved an AUC of 0.984 (95% CI 0.976 to 0.993) for individual image evaluation in the internal test set of 797 images. In addition, performance analysis of a publicly available external test set (n=968) had an AUC of 0.957 (95% CI 0.944 to 0.970) and a second clinical external test set (n=1124) had an AUC of 0.978 (95% CI 0.970 to 0.986). Moreover, the CADe highlighted well normal parts of ORL and omitted highlights in abnormal ORLs of CNV and RP | ||
| 520 | |a CONCLUSION: The CADe can use OCT images to segment ORL and differentiate between normal ORL and abnormal ORL. The CADe classifier also performs visualisation and may aid future physician diagnosis and clinical applications | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a epidemiology | |
| 650 | 4 | |a imaging | |
| 650 | 4 | |a retina | |
| 650 | 4 | |a telemedicine | |
| 700 | 1 | |a Lee, Aaron Yuntai |e verfasserin |4 aut | |
| 700 | 1 | |a Ting, Daniel S |e verfasserin |4 aut | |
| 700 | 1 | |a Teo, Kelvin Yi Chong |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Hee Seung |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Hyeonmin |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Geunyoung |e verfasserin |4 aut | |
| 700 | 1 | |a Teo, Zhen Ling |e verfasserin |4 aut | |
| 700 | 1 | |a Teo Wei Jun, Alvin |e verfasserin |4 aut | |
| 700 | 1 | |a Takahashi, Kengo |e verfasserin |4 aut | |
| 700 | 1 | |a Yoo, Tea Keun |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Sung Eun |e verfasserin |4 aut | |
| 700 | 1 | |a Yanagi, Yasuo |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Ching-Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Sung Soo |e verfasserin |4 aut | |
| 700 | 1 | |a Wong, Tien Yin |e verfasserin |4 aut | |
| 700 | 1 | |a Cheung, Chui Ming Gemmy |e verfasserin |4 aut | |
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