AI Article Synopsis
- Reticular pseudodrusen (RPD) are a key factor in vision loss related to age-related macular degeneration (AMD), making their detection essential for effective clinical management.
- Researchers created a deep learning model that segments RPD from a dataset of 9,800 optical coherence tomography scans, achieving better agreement with retinal specialists compared to their own consensus.
- The model demonstrated high performance in detecting RPD across multiple test datasets, matching the accuracy of experienced specialists, and is now publicly accessible for use.
Article Abstract
Reticular pseudodrusen (RPD) signify a critical phenotype driving vision loss in age-related macular degeneration (AMD). Their detection is paramount in the clinical management of those with AMD, yet they remain challenging to reliably identify. We thus developed a deep learning (DL) model to segment RPD from 9,800 optical coherence tomography B-scans, and this model produced RPD segmentations that had higher agreement with four retinal specialists (Dice similarity coefficient [DSC]=0·76 [95% confidence interval [CI] 0·71-0·81]) than the agreement amongst the specialists (DSC=0·68, 95% CI=0·63-0·73; <0·001). In five external test datasets consisting of 1,017 eyes from 812 individuals, the DL model detected RPD with a similar level of performance as two retinal specialists (area-under-the-curve of 0·94 [95% CI=0·92-0·97], 0·95 [95% CI=0·92-0·97] and 0·96 [95% CI=0·94-0·98] respectively; ≥0·32). This DL model enables the automatic detection and quantification of RPD with expert-level performance, which we have made publicly available.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11419239 | PMC |
| http://dx.doi.org/10.1101/2024.09.11.24312817 | DOI Listing |
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Article Synopsis
- Age-related macular degeneration (AMD) is a major cause of blindness, influenced by metabolic disorders and dietary choices.
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