Kalra Gagan, Kar Sudeshna Sil, Sevgi Duriye Damla, Madabhushi Anant, Srivastava Sunil K, Ehlers Justis P
Cole Eye Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
Tony and Leona Campane Center for Excellence in Image-Guided Surgery & Advanced, Cleveland Clinic, Cleveland, OH 44195, USA.
J Pers Med. 2021 Nov 8;11(11):1161. doi: 10.3390/jpm11111161.
The management of retinal diseases relies heavily on digital imaging data, including optical coherence tomography (OCT) and fluorescein angiography (FA). Targeted feature extraction and the objective quantification of features provide important opportunities in biomarker discovery, disease burden assessment, and predicting treatment response. Additional important advantages include increased objectivity in interpretation, longitudinal tracking, and ability to incorporate computational models to create automated diagnostic and clinical decision support systems. Advances in computational technology, including deep learning and radiomics, open new doors for developing an imaging phenotype that may provide in-depth personalized disease characterization and enhance opportunities in precision medicine. In this review, we summarize current quantitative and radiomic imaging biomarkers described in the literature for age-related macular degeneration and diabetic eye disease using imaging modalities such as OCT, FA, and OCT angiography (OCTA). Various approaches used to identify and extract these biomarkers that utilize artificial intelligence and deep learning are also summarized in this review. These quantifiable biomarkers and automated approaches have unleashed new frontiers of personalized medicine where treatments are tailored, based on patient-specific longitudinally trackable biomarkers, and response monitoring can be achieved with a high degree of accuracy.
视网膜疾病的管理在很大程度上依赖于数字成像数据，包括光学相干断层扫描（OCT）和荧光素血管造影（FA）。靶向特征提取和特征的客观量化在生物标志物发现、疾病负担评估以及预测治疗反应方面提供了重要机遇。其他重要优势包括解读的客观性增强、纵向跟踪以及能够纳入计算模型以创建自动化诊断和临床决策支持系统。包括深度学习和放射组学在内的计算技术进步，为开发成像表型打开了新的大门，这种成像表型可能提供深入的个性化疾病特征描述，并增加精准医学的机遇。在本综述中，我们总结了文献中使用OCT、FA和OCT血管造影（OCTA）等成像方式描述的与年龄相关性黄斑变性和糖尿病眼病相关的当前定量和放射组学成像生物标志物。本综述还总结了用于识别和提取这些生物标志物的各种利用人工智能和深度学习的方法。这些可量化的生物标志物和自动化方法开启了个性化医学的新领域，在这个领域中，治疗是根据患者特异性的可纵向跟踪的生物标志物量身定制的，并且可以高度准确地实现反应监测。
