Thee Eric F, Luttikhuizen Daniël T, Lemij Hans G, Verbraak Frank D, Sánchez Clara I, Klaver Caroline C W
Erasmus MC, afd. Oogheelkunde en afd. Epidemiologie, Rotterdam.
Het Oogziekenhuis, Rotterdam.
Ned Tijdschr Geneeskd. 2020 Sep 17;164:D5200.
Technological developments in ophthalmic imaging and artificial intelligence (AI) create new possibilities for diagnostics in eye care. AI has already been applied in ophthalmic diabetes care. AI-systems currently detect diabetic retinopathy in general practice with a high sensitivity and specificity. AI-systems for the screening, monitoring and treatment of age-related macular degeneration and glaucoma are promising and are still being developed. AI-algorithms, however, only perform tasks for which they have been specifically trained and highly depend on the data and reference-standard that were used to train the system in identifying a certain abnormality or disease. How the data and the gold standard were established and determined, influences the performance of the algorithm. Furthermore, interpretability of deep learning algorithms is still an ongoing issue. By highlighting on images the areas that were critical for the decision of the algorithm, users can gain more insight into how algorithms come to a particular result.
眼科成像和人工智能（AI）的技术发展为眼保健诊断创造了新的可能性。AI已应用于眼科糖尿病护理。目前，AI系统在一般医疗实践中检测糖尿病视网膜病变具有很高的灵敏度和特异性。用于年龄相关性黄斑变性和青光眼筛查、监测和治疗的AI系统很有前景，仍在开发中。然而，AI算法仅执行它们经过专门训练的任务，并且高度依赖于用于训练系统以识别特定异常或疾病的数据和参考标准。数据和金标准的建立和确定方式会影响算法的性能。此外，深度学习算法的可解释性仍然是一个持续存在的问题。通过在图像上突出显示对算法决策至关重要的区域，用户可以更深入地了解算法如何得出特定结果。
