Artificial intelligence for morphology-based function prediction in neovascular age-related macular degeneration
Spatially-resolved mapping of rod- and cone-function may facilitate monitoring of macular diseases and serve as a functional outcome parameter. However, mesopic and dark-adapted two-color fundus-controlled perimetry (FCP, also called "microperimetry") constitute laborious examinations. We...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
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Springer Nature
2019
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| _version_ | 1826263588710383616 |
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| author | von der Emde, L Pfau, M Dysli, C Thiele, S Möller, PT Lindner, M Schmid, M Fleckenstein, M Holz, FG Schmitz-Valckenberg, S |
| author_facet | von der Emde, L Pfau, M Dysli, C Thiele, S Möller, PT Lindner, M Schmid, M Fleckenstein, M Holz, FG Schmitz-Valckenberg, S |
| author_sort | von der Emde, L |
| collection | OXFORD |
| description | Spatially-resolved mapping of rod- and cone-function may facilitate monitoring of macular diseases and serve as a functional outcome parameter. However, mesopic and dark-adapted two-color fundus-controlled perimetry (FCP, also called "microperimetry") constitute laborious examinations. We have devised a machine-learning-based approach to predict mesopic and dark-adapted (DA) retinal sensitivity in eyes with neovascular age-related macular degeneration (nAMD). Extensive psychophysical testing and volumetric multimodal retinal imaging data were acquired including mesopic, DA red and DA cyan FCP, spectral-domain optical coherence tomography and confocal scanning laser ophthalmoscopy infrared reflectance and fundus autofluorescence imaging. With patient-wise leave-one-out cross-validation, we have been able to achieve prediction accuracies of (mean absolute error, MAE [95% CI]) 3.94 dB [3.38, 4.5] for mesopic, 4.93 dB [4.59, 5.27] for DA cyan and 4.02 dB [3.63, 4.42] for DA red testing. Partial addition of patient-specific sensitivity data decreased the cross-validated MAE to 2.8 dB [2.51, 3.09], 3.71 dB [3.46, 3.96], and 2.85 dB [2.62, 3.08]. The most important predictive feature was outer nuclear layer thickness. This artificial intelligence-based analysis strategy, termed "inferred sensitivity", herein, enables to estimate differential effects of retinal structural abnormalities on cone- and rod-function in nAMD, and may be used as quasi-functional surrogate endpoint in future clinical trials. |
| first_indexed | 2024-03-06T19:54:11Z |
| format | Journal article |
| id | oxford-uuid:24fbf87f-107b-4804-b10c-fa6926c43464 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:54:11Z |
| publishDate | 2019 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:24fbf87f-107b-4804-b10c-fa6926c434642022-03-26T11:53:17ZArtificial intelligence for morphology-based function prediction in neovascular age-related macular degenerationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:24fbf87f-107b-4804-b10c-fa6926c43464EnglishSymplectic ElementsSpringer Nature2019von der Emde, LPfau, MDysli, CThiele, SMöller, PTLindner, MSchmid, MFleckenstein, MHolz, FGSchmitz-Valckenberg, SSpatially-resolved mapping of rod- and cone-function may facilitate monitoring of macular diseases and serve as a functional outcome parameter. However, mesopic and dark-adapted two-color fundus-controlled perimetry (FCP, also called "microperimetry") constitute laborious examinations. We have devised a machine-learning-based approach to predict mesopic and dark-adapted (DA) retinal sensitivity in eyes with neovascular age-related macular degeneration (nAMD). Extensive psychophysical testing and volumetric multimodal retinal imaging data were acquired including mesopic, DA red and DA cyan FCP, spectral-domain optical coherence tomography and confocal scanning laser ophthalmoscopy infrared reflectance and fundus autofluorescence imaging. With patient-wise leave-one-out cross-validation, we have been able to achieve prediction accuracies of (mean absolute error, MAE [95% CI]) 3.94 dB [3.38, 4.5] for mesopic, 4.93 dB [4.59, 5.27] for DA cyan and 4.02 dB [3.63, 4.42] for DA red testing. Partial addition of patient-specific sensitivity data decreased the cross-validated MAE to 2.8 dB [2.51, 3.09], 3.71 dB [3.46, 3.96], and 2.85 dB [2.62, 3.08]. The most important predictive feature was outer nuclear layer thickness. This artificial intelligence-based analysis strategy, termed "inferred sensitivity", herein, enables to estimate differential effects of retinal structural abnormalities on cone- and rod-function in nAMD, and may be used as quasi-functional surrogate endpoint in future clinical trials. |
| spellingShingle | von der Emde, L Pfau, M Dysli, C Thiele, S Möller, PT Lindner, M Schmid, M Fleckenstein, M Holz, FG Schmitz-Valckenberg, S Artificial intelligence for morphology-based function prediction in neovascular age-related macular degeneration |
| title | Artificial intelligence for morphology-based function prediction in neovascular age-related macular degeneration |
| title_full | Artificial intelligence for morphology-based function prediction in neovascular age-related macular degeneration |
| title_fullStr | Artificial intelligence for morphology-based function prediction in neovascular age-related macular degeneration |
| title_full_unstemmed | Artificial intelligence for morphology-based function prediction in neovascular age-related macular degeneration |
| title_short | Artificial intelligence for morphology-based function prediction in neovascular age-related macular degeneration |
| title_sort | artificial intelligence for morphology based function prediction in neovascular age related macular degeneration |
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