دورية أكاديمية
SynthEye: Investigating the Impact of Synthetic Data on Artificial Intelligence-assisted Gene Diagnosis of Inherited Retinal Disease
| العنوان: | SynthEye: Investigating the Impact of Synthetic Data on Artificial Intelligence-assisted Gene Diagnosis of Inherited Retinal Disease |
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| المؤلفون: | Veturi, Yoga Advaith, Woof, William, Lazebnik, Teddy, Moghul, Ismail, Woodward-Court, Peter, Wagner, Siegfried K, Cabral de Guimarães, Thales Antonio, Daich Varela, Malena, Liefers, Bart, Patel, Praveen J, Beck, Stephan, Webster, Andrew R, Mahroo, Omar, Keane, Pearse A, Michaelides, Michel, Balaskas, Konstantinos, Pontikos, Nikolas |
| المصدر: | Ophthalmology Science , 3 (2) , Article 100258. (2023) |
| بيانات النشر: | Elsevier BV |
| سنة النشر: | 2023 |
| المجموعة: | University College London: UCL Discovery |
| مصطلحات موضوعية: | Synthetic data, Deep Learning, Generative Adversarial Networks, Inherited Retinal Diseases, Class imbalance, Clinical Decision-Support Model |
| الوصف: | PURPOSE: Rare disease diagnosis is challenging in medical image-based artificial intelligence due to a natural class imbalance in datasets, leading to biased prediction models. Inherited retinal diseases (IRDs) are a research domain that particularly faces this issue. This study investigates the applicability of synthetic data in improving artificial intelligence-enabled diagnosis of IRDs using generative adversarial networks (GANs). DESIGN: Diagnostic study of gene-labeled fundus autofluorescence (FAF) IRD images using deep learning. PARTICIPANTS: Moorfields Eye Hospital (MEH) dataset of 15 692 FAF images obtained from 1800 patients with confirmed genetic diagnosis of 1 of 36 IRD genes. METHODS: A StyleGAN2 model is trained on the IRD dataset to generate 512 × 512 resolution images. Convolutional neural networks are trained for classification using different synthetically augmented datasets, including real IRD images plus 1800 and 3600 synthetic images, and a fully rebalanced dataset. We also perform an experiment with only synthetic data. All models are compared against a baseline convolutional neural network trained only on real data. MAIN OUTCOME MEASURES: We evaluated synthetic data quality using a Visual Turing Test conducted with 4 ophthalmologists from MEH. Synthetic and real images were compared using feature space visualization, similarity analysis to detect memorized images, and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score for no-reference-based quality evaluation. Convolutional neural network diagnostic performance was determined on a held-out test set using the area under the receiver operating characteristic curve (AUROC) and Cohen's Kappa (κ). RESULTS: An average true recognition rate of 63% and fake recognition rate of 47% was obtained from the Visual Turing Test. Thus, a considerable proportion of the synthetic images were classified as real by clinical experts. Similarity analysis showed that the synthetic images were not copies of the real images, indicating that copied ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | https://discovery.ucl.ac.uk/id/eprint/10163770/1/1-s2.0-S2666914522001476-main.pdfTest; https://discovery.ucl.ac.uk/id/eprint/10163770Test/ |
| الإتاحة: | https://discovery.ucl.ac.uk/id/eprint/10163770/1/1-s2.0-S2666914522001476-main.pdfTest https://discovery.ucl.ac.uk/id/eprint/10163770Test/ |
| حقوق: | open |
| رقم الانضمام: | edsbas.427ABE02 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |