Modeling pegcetacoplan treatment effect for atrophic age-related macular degeneration with AI-based progression prediction
Abstract Background To illustrate the treatment effect of Pegcetacoplan for atrophy secondary to age-related macular degeneration (AMD), on an individualized topographic progression prediction basis, using a deep learning model. Methods Patients (N = 99) with atrophy secondary to AMD with longitudin...
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BMC
2025-02-01
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| Series: | International Journal of Retina and Vitreous |
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| Online Access: | https://doi.org/10.1186/s40942-025-00634-z |
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| author | Irmela Mantel Romina M. Lasagni Vitar Sandro De Zanet |
| author_facet | Irmela Mantel Romina M. Lasagni Vitar Sandro De Zanet |
| author_sort | Irmela Mantel |
| collection | DOAJ |
| description | Abstract Background To illustrate the treatment effect of Pegcetacoplan for atrophy secondary to age-related macular degeneration (AMD), on an individualized topographic progression prediction basis, using a deep learning model. Methods Patients (N = 99) with atrophy secondary to AMD with longitudinal optical coherence tomography (OCT) data were retrospectively analyzed. We used a previously published deep-learning-based atrophy progression prediction algorithm to predict the 2-year atrophy progression, including the topographic likelihood of future retinal pigment epithelial and outer retinal atrophy (RORA), according to the baseline OCT input. The algorithm output was a step-less individualized topographic modeling of the RORA growth, allowing for illustrating the progression line corresponding to an 80% growth compared to the natural course of 100% growth. Results The treatment effect of Pegcetacoplan was illustrated as the line when 80% of the growth is reached in this continuous model. Besides the well-known variability of atrophy growth rate, our results showed unequal growth according to the fundus location. It became evident that this difference is of potential functional interest for patient outcomes. Conclusions This model based on an 80% growth of RORA after two years illustrates the variable effect of treatment with Pegcetacoplan according to the individual situation, supporting personalized medical care. |
| format | Article |
| id | doaj-art-64d2b4f7957c4809b6e589c328ca4b9d |
| institution | Kabale University |
| issn | 2056-9920 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | International Journal of Retina and Vitreous |
| spelling | doaj-art-64d2b4f7957c4809b6e589c328ca4b9d2025-02-09T12:49:10ZengBMCInternational Journal of Retina and Vitreous2056-99202025-02-011111610.1186/s40942-025-00634-zModeling pegcetacoplan treatment effect for atrophic age-related macular degeneration with AI-based progression predictionIrmela Mantel0Romina M. Lasagni Vitar1Sandro De Zanet2Department of Ophthalmology, University of Lausanne, Jules-Gonin Eye HospitalIkerian AGIkerian AGAbstract Background To illustrate the treatment effect of Pegcetacoplan for atrophy secondary to age-related macular degeneration (AMD), on an individualized topographic progression prediction basis, using a deep learning model. Methods Patients (N = 99) with atrophy secondary to AMD with longitudinal optical coherence tomography (OCT) data were retrospectively analyzed. We used a previously published deep-learning-based atrophy progression prediction algorithm to predict the 2-year atrophy progression, including the topographic likelihood of future retinal pigment epithelial and outer retinal atrophy (RORA), according to the baseline OCT input. The algorithm output was a step-less individualized topographic modeling of the RORA growth, allowing for illustrating the progression line corresponding to an 80% growth compared to the natural course of 100% growth. Results The treatment effect of Pegcetacoplan was illustrated as the line when 80% of the growth is reached in this continuous model. Besides the well-known variability of atrophy growth rate, our results showed unequal growth according to the fundus location. It became evident that this difference is of potential functional interest for patient outcomes. Conclusions This model based on an 80% growth of RORA after two years illustrates the variable effect of treatment with Pegcetacoplan according to the individual situation, supporting personalized medical care.https://doi.org/10.1186/s40942-025-00634-zAge-related macular degenerationAtrophyComplement inhibitionPegcetacoplanDeep learning-based RORA progression prediction |
| spellingShingle | Irmela Mantel Romina M. Lasagni Vitar Sandro De Zanet Modeling pegcetacoplan treatment effect for atrophic age-related macular degeneration with AI-based progression prediction International Journal of Retina and Vitreous Age-related macular degeneration Atrophy Complement inhibition Pegcetacoplan Deep learning-based RORA progression prediction |
| title | Modeling pegcetacoplan treatment effect for atrophic age-related macular degeneration with AI-based progression prediction |
| title_full | Modeling pegcetacoplan treatment effect for atrophic age-related macular degeneration with AI-based progression prediction |
| title_fullStr | Modeling pegcetacoplan treatment effect for atrophic age-related macular degeneration with AI-based progression prediction |
| title_full_unstemmed | Modeling pegcetacoplan treatment effect for atrophic age-related macular degeneration with AI-based progression prediction |
| title_short | Modeling pegcetacoplan treatment effect for atrophic age-related macular degeneration with AI-based progression prediction |
| title_sort | modeling pegcetacoplan treatment effect for atrophic age related macular degeneration with ai based progression prediction |
| topic | Age-related macular degeneration Atrophy Complement inhibition Pegcetacoplan Deep learning-based RORA progression prediction |
| url | https://doi.org/10.1186/s40942-025-00634-z |
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