Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107445
DC Field | Value | Language |
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dc.contributor | School of Optometry | - |
dc.contributor | Research Centre for SHARP Vision | - |
dc.creator | Chen, R | en_US |
dc.creator | Zhang, W | en_US |
dc.creator | Song, F | en_US |
dc.creator | Yu, H | en_US |
dc.creator | Cao, D | en_US |
dc.creator | Zheng, Y | en_US |
dc.creator | He, M | en_US |
dc.creator | Shi, D | en_US |
dc.date.accessioned | 2024-06-24T07:02:46Z | - |
dc.date.available | 2024-06-24T07:02:46Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/107445 | - |
dc.language.iso | en | en_US |
dc.publisher | Nature Publishing Group | en_US |
dc.rights | © The Author(s) 2024 | en_US |
dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | en_US |
dc.rights | The following publication Chen, R., Zhang, W., Song, F. et al. Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening. npj Digit. Med. 7, 34 (2024) is available at https://doi.org/10.1038/s41746-024-01018-7. | en_US |
dc.title | Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 7 | en_US |
dc.identifier.doi | 10.1038/s41746-024-01018-7 | en_US |
dcterms.abstract | Age-related macular degeneration (AMD) is the leading cause of central vision impairment among the elderly. Effective and accurate AMD screening tools are urgently needed. Indocyanine green angiography (ICGA) is a well-established technique for detecting chorioretinal diseases, but its invasive nature and potential risks impede its routine clinical application. Here, we innovatively developed a deep-learning model capable of generating realistic ICGA images from color fundus photography (CF) using generative adversarial networks (GANs) and evaluated its performance in AMD classification. The model was developed with 99,002 CF-ICGA pairs from a tertiary center. The quality of the generated ICGA images underwent objective evaluation using mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity measures (SSIM), etc., and subjective evaluation by two experienced ophthalmologists. The model generated realistic early, mid and late-phase ICGA images, with SSIM spanned from 0.57 to 0.65. The subjective quality scores ranged from 1.46 to 2.74 on the five-point scale (1 refers to the real ICGA image quality, Kappa 0.79–0.84). Moreover, we assessed the application of translated ICGA images in AMD screening on an external dataset (n = 13887) by calculating area under the ROC curve (AUC) in classifying AMD. Combining generated ICGA with real CF images improved the accuracy of AMD classification with AUC increased from 0.93 to 0.97 (P < 0.001). These results suggested that CF-to-ICGA translation can serve as a cross-modal data augmentation method to address the data hunger often encountered in deep-learning research, and as a promising add-on for population-based AMD screening. Real-world validation is warranted before clinical usage. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | npj digital medicine, 2024, v. 7, 34 | en_US |
dcterms.isPartOf | npj digital medicine | en_US |
dcterms.issued | 2024 | - |
dc.identifier.scopus | 2-s2.0-85184994485 | - |
dc.identifier.eissn | 2398-6352 | en_US |
dc.identifier.artn | 34 | en_US |
dc.description.validate | 202406 bcch | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a2869 | - |
dc.identifier.SubFormID | 48597 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Start-up Fund for RAPs under the Strategic Hiring Scheme; Global STEM Professorship Scheme; National Natural Science Foundation of China, | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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s41746-024-01018-7.pdf | 1.74 MB | Adobe PDF | View/Open |
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