AI-based structure-function correlation in age-related macular degeneration

Sensitive and robust outcome measures of retinal function are pivotal for clinical trials in age-related macular degeneration (AMD). A recent development is the implementation of artificial intelligence (AI) to infer results of psychophysical examinations based on findings derived from multimodal imaging. We conducted a review of the current literature referenced in PubMed and Web of Science among others with the keywords 'artificial intelligence' and 'machine learning' in combination with 'perimetry', 'best-corrected visual acuity (BCVA)', 'retinal function' and 'age-related macular degeneration'. So far AI-based structure-function correlations have been applied to infer conventional visual field, fundus-controlled perimetry, and electroretinography data, as well as BCVA, and patient-reported outcome measures (PROM). In neovascular AMD, inference of BCVA (hereafter termed inferred BCVA) can estimate BCVA results with a root mean squared error of ~7-11 letters, which is comparable to the accuracy of actual visual acuity assessment. Further, AI-based structure-function correlation can successfully infer fundus-controlled perimetry (FCP) results both for mesopic as well as dark-adapted (DA) cyan and red testing (hereafter termed inferred sensitivity). Accuracy of inferred sensitivity can be augmented by adding short FCP examinations and reach mean absolute errors (MAE) of ~3-5 dB for mesopic, DA cyan and DA red testing. Inferred BCVA, and inferred retinal sensitivity, based on multimodal imaging, may be considered as a quasi-functional surrogate endpoint for future interventional clinical trials in the future.摘要: 对于年龄相关性黄斑变性(AMD)的临床试验来说, 敏感且强大的视网膜功能测量手段是至关重要的。近期的新进展是利用人工智能(AI)来解释多模式影像成像的心理物理检查结果。我们以“人工智能”和“机器学习”为关键词, 结合“视野测量”、“最佳矫正视力(BCVA)”、“视网膜功能”和“年龄相关性黄斑变性”, 对PubMed和Web of Science中的文献进行了综述。到目前为止, 基于人工智能的结构功能相关性的研究已被应用于传统的视野检查、FCP视野检查、视网膜电生理检查以及BCVA的检查和患者报告结果(PROM)的测量。对于新生血管性AMD, BCVA推理法(以下称为BCVA推测法)可以评估BCVA结果, 其均方根误差大约为7-11个字母, 这与实际视力评估的准确性相当。此外, 基于人工智能的结构功能相关性的研究可以成功推测FCP视野检查的结果, 包括中间视觉、暗适应(DA)青色和红色试验(以下称为灵敏度推测法)。通过增加短时间的FCP检查, 可以提高灵敏度推测法的准确性, 对于中间视觉法、暗适应青色法和暗适应红色法, 其平均绝对误差(MAE)约为3-5 dB。基于多模态成像的BCVA推测法和视网膜灵敏度推测法, 可被认为是未来干预性临床试验的功能评估手段的替代终点。.