Deep learning has recently gained high interest in ophthalmology, due to its ability to detect clinically significant features for diagnosis and prognosis. Despite these significant advances, little is known about the ability of various deep learning systems to be embedded within ophthalmic imaging devices, allowing automated image acquisition. In this work, we will review the existing and future directions for "active acquisition" embedded deep learning, leading to as high quality images with little intervention by the human operator. In clinical practice, the improved image quality should translate into more robust deep learning-based clinical diagnostics. Embedded deep learning will be enabled by the constantly improving hardware performance with low cost. We will briefly review possible computation methods in larger clinical systems. Briefly, they can be included in a three-layer framework composed of edge, fog and cloud layers, the former being performed at a device-level. Improved edge layer performance via "active acquisition" serves as an automatic data curation operator translating to better quality data in electronic health records (EHRs), as well as on the cloud layer, for improved deep learning-based clinical data mining.
翻译:深层学习最近由于能够发现临床上重要的诊断和预测特征而引起对眼科学的高度兴趣。尽管取得了这些重大进步,但对于各种深层学习系统是否有能力嵌入眼科成像装置,并允许自动获取图像,却知之甚少。在这项工作中,我们将审查“积极获取”嵌入的深层学习的现有和未来方向,从而产生高质量的图像,而人类操作者很少介入。在临床实践中,改进后的图像质量应转化为更健全的深层次的基于学习的临床诊断。嵌入式深层学习将通过不断以低成本改进硬件性能而得以进行。我们将简要审查较大临床系统中可能的计算方法。简而言之,这些方法可以纳入由边缘、雾和云层组成的三层框架,前者是在设备一级进行。通过“积极获取”改进边缘层性能,作为自动数据整理操作者,将电子健康记录(EHRs)和云层中更好的高质量数据转化成,以改进基于深层学习的临床数据挖掘。