Centre for Eye Research Australia, University of Melbourne, Royal Victorian Eye & Ear Hospital, East Melbourne, Victoria, Australia.
Department of Surgery, Ophthalmology, University of Melbourne, Parkville, Victoria, Australia.
Transl Vis Sci Technol. 2021 Nov 1;10(13):3. doi: 10.1167/tvst.10.13.3.
The purpose of this study was to identify a taxonomy of epistemic uncertainties that affect results for geographic atrophy (GA) assessment and progression.
An important source of variability is called "epistemic uncertainty," which is due to incomplete system knowledge (i.e. limitations in measurement devices, artifacts, and human subjective evaluation, including annotation errors). In this study, different epistemic uncertainties affecting the analysis of GA were identified and organized into a taxonomy. The uncertainties were discussed and analyzed, and an example was provided in the case of model structure uncertainty by characterizing progression of GA by mathematical modelling and machine learning. It was hypothesized that GA growth follows a logistic (sigmoidal) function. Using case studies, the GA growth data were used to test the sigmoidal hypothesis.
Epistemic uncertainties were identified, including measurement error (imperfect outcomes from measuring tools), subjective judgment (grading affected by grader's vision and experience), model input uncertainties (data corruption or entry errors), and model structure uncertainties (elucidating the right progression pattern). Using GA growth data from case studies, it was demonstrated that GA growth can be represented by a sigmoidal function, where growth eventually approaches an upper limit.
Epistemic uncertainties contribute to errors in study results and are reducible if identified and addressed. By prior identification of epistemic uncertainties, it is possible to (a) quantify uncertainty not accounted for by natural statistical variability, and (b) reduce the presence of these uncertainties in future studies.
Lowering epistemic uncertainty will reduce experimental error, improve consistency and reproducibility, and increase confidence in diagnostics.
本研究旨在确定影响地理萎缩（GA）评估和进展结果的不确定性分类法。
一种重要的变异性来源称为“认知不确定性”，这是由于系统知识不完整（即测量设备、人工制品和人类主观评估的局限性，包括注释错误）。在这项研究中，确定了影响 GA 分析的不同认知不确定性，并将其组织成分类法。对这些不确定性进行了讨论和分析，并通过数学建模和机器学习来描述 GA 的进展，为模型结构不确定性提供了一个示例。假设 GA 的增长遵循逻辑（Sigmoidal）函数。使用案例研究，使用 GA 生长数据来测试 Sigmoidal 假设。
确定了认知不确定性，包括测量误差（测量工具的不完善结果）、主观判断（分级受分级员的视力和经验影响）、模型输入不确定性（数据损坏或输入错误）和模型结构不确定性（阐明正确的进展模式）。使用案例研究中的 GA 生长数据，证明 GA 的生长可以用 Sigmoidal 函数来表示，其中生长最终接近上限。
认知不确定性会导致研究结果出现误差，如果能够识别和解决这些误差，就可以减少。通过事先确定认知不确定性，可以（a）量化自然统计变异性无法解释的不确定性，（b）减少未来研究中这些不确定性的存在。
杨玲
