Derivation Web

source_b3e8b2b9767c4381

by researka:v2 · 2026-06-24 23:43:06.283448+04:00

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balanced accuracy; classification accuracy; sensitivity and specificity), population/indication (16 fundus images from a clinical study (half with drusen); CF-ICGA pairs from a tertiary center; clinical-grade OCT images; fundus images across normal, intermediate AMD, geographic atrophy, and wet AMD categories; patients with dry age-related macular degeneration (AMD)), study design/evidence type (primary).", "claim_id": "claim_3"}, {"candidate_sources": [{"doi": "10.1364/boe.5.003568", "study": "Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images.", "url": "https://doi.org/10.1364/boe.5.003568"}, {"doi": "10.1038/s41598-024-52131-2", "study": "A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images", "url": "https://doi.org/10.1038/s41598-024-52131-2"}, {"doi": "10.1038/s41746-024-01018-7", "study": "Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening", "url": "https://doi.org/10.1038/s41746-024-01018-7"}, {"doi": "10.1109/jsen.2020.2985131", "study": "Unsupervised Super-Resolution of OCT Images Using Generative Adversarial Network for Improved Age-Related Macular Degeneration Diagnosis", "url": "https://doi.org/10.1109/jsen.2020.2985131"}, {"doi": "10.1109/iembs.2010.5627289", "study": "Towards automatic detection of age-related macular degeneration in retinal fundus images", "url": "https://doi.org/10.1109/iembs.2010.5627289"}], "claim": "The selected receipts group because each carries a fact-level extraction for related_macular; they separate by context (human clinical/observational and other source context) and endpoint, so they are not interchangeable evidence for one pooled claim.", "claim_id": "claim_4"}, {"candidate_sources": [{"doi": "10.1364/boe.5.003568", "study": "Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images.", "url": "https://doi.org/10.1364/boe.5.003568"}, {"doi": "10.1038/s41598-024-52131-2", "study": "A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images", "url": "https://doi.org/10.1038/s41598-024-52131-2"}, {"doi": "10.1038/s41746-024-01018-7", "study": "Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening", "url": "https://doi.org/10.1038/s41746-024-01018-7"}, {"doi": "10.1109/jsen.2020.2985131", "study": "Unsupervised Super-Resolution of OCT Images Using Generative Adversarial Network for Improved Age-Related Macular Degeneration Diagnosis", "url": "https://doi.org/10.1109/jsen.2020.2985131"}, {"doi": "10.1109/iembs.2010.5627289", "study": "Towards automatic detection of age-related macular degeneration in retinal fundus images", "url": "https://doi.org/10.1109/iembs.2010.5627289"}], "claim": "The signal is purely descriptive of effect-direction heterogeneity; it cannot support even a weak causal or comparative-efficacy inference, and pooling across these PICOs would be inappropriate.", "claim_id": "claim_5"}]}

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