Updates in deep learning research in ophthalmology

【Author】 Ng, Wei Yan; Zhang, Shihao; Wang, Zhaoran; Ong, Charles Jit Teng; Gunasekeran, Dinesh, V; San Lim, Gilbert Yong; Zheng, Feihui; Tan, Shaun Chern Yuan; Tan, Gavin Siew Wei; Rim, Tyler Hyungtaek; Schmetterer, Leopold; Ting, Daniel Shu Wei

【Source】CLINICAL SCIENCE

【Abstract】Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.

【Keywords】

【标题】眼科深度学习研究进展

【摘要】眼科一直是医学领域人工智能 (AI) 的早期采用者之一。尤其是深度学习 (DL)，由于大量数据和数字化的视觉图像的可用性而引起了极大的关注。目前，眼科中的人工智能主要专注于在治疗糖尿病视网膜病变、年龄相关性黄斑变性（AMD）、青光眼和早产儿视网膜病变（ROP）等眼科疾病时改善疾病分类和支持决策。然而，迄今为止开发的大多数深度学习系统（DLS）仍处于研究阶段，只有少数能够实现临床转化。这种现象是由于对安全和隐私的担忧、普遍性差、信任和可解释性问题、不利的最终用户感知和不确定的经济价值等多种因素造成的。克服这一挑战需要一种综合方法。首先，联邦学习 (FL)、生成对抗网络 (GAN)、自主人工智能和区块链等新兴技术将在增强隐私、协作和 DLS 性能方面发挥越来越重要的作用。接下来，将需要遵守报告和监管指南，例如 CONSORT-AI 和 STARD-AI，以提高透明度、最大限度地减少滥用并确保可重复性。第三，将需要框架来获得患者同意、进行伦理评估和评估最终用户的感知。最后，必须进行适当的健康经济评估 (HEA)，以在 DLS 开发的早期阶段提供财务可见性。这对于审慎管理资源和指导 DLS 的发展是必要的。

【关键词】无

【发表时间】2021

【收录时间】2022-07-06

【文献类型】Review

【论文大主题】区块链联邦学习

【论文小主题】两者结合

【影响因子】6.876

【翻译者】石东瑛