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The eye in AI: artificial intelligence in ophthalmology
摘要： The convergence of major developments in artificial intelligence (AI) for image analysis with advances in clinical imaging technologies has major implications for the practice of medicine. Gains in AI system performance have been the product of improvements in computing hardware and progress in algorithm design, such that large volumes of data can now be processed with great accuracy at extraordinary speeds. As Hogarty et al. illustrate in this edition of the Journal, the discipline of ophthalmology is at the forefront of the AI revolution, with a growing body of research indicating that AI systems can be applied to a wide range of ophthalmic imaging methods across a broad range of disease categories with remarkable performance.
关键词： image analysis,clinical imaging,artificial intelligence,deep learning,ophthalmology
更新于2025-09-11 14:15:04
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Pattern Recognition Analysis of Age-Related Retinal Ganglion Cell Signatures in the Human Eye
摘要： PURPOSE. To characterize macular ganglion cell layer (GCL) changes with age and provide a framework to assess changes in ocular disease. This study used data clustering to analyze macular GCL patterns from optical coherence tomography (OCT) in a large cohort of subjects without ocular disease. METHODS. Single eyes of 201 patients evaluated at the Centre for Eye Health (Sydney, Australia) were retrospectively enrolled (age range, 20–85); 8 3 8 grid locations obtained from Spectralis OCT macular scans were analyzed with unsupervised classi?cation into statistically separable classes sharing common GCL thickness and change with age. The resulting classes and gridwise data were ?tted with linear and segmented linear regression curves. Additionally, normalized data were analyzed to determine regression as a percentage. Accuracy of each model was examined through comparison of predicted 50-year-old equivalent macular GCL thickness for the entire cohort to a true 50-year-old reference cohort. RESULTS. Pattern recognition clustered GCL thickness across the macula into ?ve to eight spatially concentric classes. F-test demonstrated segmented linear regression to be the most appropriate model for macular GCL change. The pattern recognition–derived and normalized model revealed less difference between the predicted macular GCL thickness and the reference cohort (average 6 SD 0.19 6 0.92 and (cid:2)0.30 6 0.61 lm) than a gridwise model (average 6 SD 0.62 6 1.43 lm). CONCLUSIONS. Pattern recognition successfully identi?ed statistically separable macular areas that undergo a segmented linear reduction with age. This regression model better predicted macular GCL thickness. The various unique spatial patterns revealed by pattern recognition combined with core GCL thickness data provide a framework to analyze GCL loss in ocular disease.
关键词： pattern recognition,aging,optical coherence tomography,ganglion cells,image analysis (clinical)
更新于2025-09-09 09:28:46