Applications of interpretability in deep learning models for ophthalmology.
Applications of interpretability in deep learning models for ophthalmology.
复制标题
可解释性在眼科深度学习模型中的应用。
DOI：
10.1097/icu.0000000000000780
复制
发表时间：
2021-09-01
影响因子：
3.7
通讯作者：
Campbell JP
中科院分区：
文献类型：
--
作者：
Hanif AM;Beqiri S;Keane PA;Campbell JP
在这篇文章中，我们介绍了模型可解释性的概念，回顾了它在临床眼科深度学习模型中的应用，并讨论了它在医疗保健中集成人工智能的作用。医学中深度学习的出现引入了具有显著准确性的模型。然而，这些模型固有的复杂性削弱了用户在临床实践中理解、调试并最终信任它们的能力。人们越来越多地探索新的方法来提高模型的“可解释性”，并在输入数据集中的输出和特征之间建立更清晰的联系。在眼科领域，可解释性方法使用户能够做出明智的调整，识别临床相关的成像模式，并预测深度学习模型的结果。可解释性方法支持实现、操作和修改复杂深度学习模型所需的透明度。这些益处在临床眼科学模型中得到越来越多的证明。随着医疗保健中使用的深度学习模型的质量标准不断发展，可解释性方法可能会在其获得监管批准和临床实践接受的过程中产生影响。
In this article, we introduce the concept of model interpretability, review its applications in deep learning models for clinical ophthalmology, and discuss its role in the integration of artificial intelligence in healthcare. The advent of deep learning in medicine has introduced models with remarkable accuracy. However, the inherent complexity of these models undermines its users’ ability to understand, debug and ultimately trust them in clinical practice. Novel methods are being increasingly explored to improve models’ “interpretability” and draw clearer associations between their outputs and features in the input dataset. In the field of ophthalmology, interpretability methods have enabled users to make informed adjustments, identify clinically relevant imaging patterns, and predict outcomes in deep learning models. Interpretability methods support the transparency necessary to implement, operate and modify complex deep learning models. These benefits are becoming increasingly demonstrated in models for clinical ophthalmology. As quality standards for deep learning models used in healthcare continue to evolve, interpretability methods may prove influential in their path to regulatory approval and acceptance in clinical practice.