Wintergerst Maximilian W M, Schultz Thomas, Birtel Johannes, Schuster Alexander K, Pfeiffer Norbert, Schmitz-Valckenberg Steffen, Holz Frank G, Finger Robert P
Department of Ophthalmology, University of Bonn, Bonn, Germany.
Department of Computer Science, University of Bonn, Bonn, Germany.
Transl Vis Sci Technol. 2017 Jul 18;6(4):10. doi: 10.1167/tvst.6.4.10. eCollection 2017 Jul.
To assess the quality of optical coherence tomography (OCT) grading algorithms for retinal biomarkers of age-related macular degeneration (AMD).
Following a systematic review of the literature data on detection and quantification of AMD retinal biomarkers by available algorithms were extracted and descriptively synthesized. Algorithm quality was assessed using a modified version of the Quality Assessment of Diagnostic Accuracy Studies 2 checklist with a focus on accuracy against established reference standards and risk of bias.
Thirty five studies reporting computer-aided diagnosis (CAD) tools for qualitative analysis or algorithms for quantitative analysis were identified. Compared with manual assessment in reference standards correlation coefficients ranged from 0.54 to 0.97 for drusen, 0.80 to 0.98 for geographic atrophy (GA), and 0.30 to 0.98 for intra- or subretinal fluid and pigment epithelial detachment (PED) detection by automated algorithms. CAD tools achieved area under the curve (AUC) values of 0.94 to 0.99, sensitivity of 0.90 to 1.00, and specificity of 0.89 to 0.92.
Automated analysis of AMD biomarkers on OCT is promising. However, most of the algorithm validation was performed in preselected patients, exhibiting the targeted biomarker only. In addition, type and quality of reported algorithm validation varied substantially.
The development of algorithms for combined, simultaneous analysis of multiple AMD biomarkers including AMD staging and the agreement on standardized validation procedures would be of considerable translational value for the clinician and the clinical researcher.
评估用于年龄相关性黄斑变性(AMD)视网膜生物标志物的光学相干断层扫描(OCT)分级算法的质量。
在对关于通过现有算法检测和量化AMD视网膜生物标志物的文献数据进行系统回顾之后,提取并进行描述性综合分析。使用诊断准确性研究质量评估2清单的修改版本评估算法质量,重点关注与既定参考标准相比的准确性和偏倚风险。
确定了35项报告用于定性分析的计算机辅助诊断(CAD)工具或用于定量分析的算法的研究。与参考标准中的手动评估相比,自动算法检测玻璃膜疣的相关系数范围为0.54至0.97,检测地图样萎缩(GA)的相关系数范围为0.80至0.98,检测视网膜内或视网膜下液以及色素上皮脱离(PED)的相关系数范围为0.30至0.98。CAD工具的曲线下面积(AUC)值为0.94至0.99,灵敏度为0.90至1.00,特异性为0.89至0.92。
OCT上对AMD生物标志物进行自动分析很有前景。然而,大多数算法验证是在预先选择的患者中进行的,仅显示出目标生物标志物。此外,报告的算法验证的类型和质量差异很大。
开发用于联合、同时分析包括AMD分期在内的多种AMD生物标志物的算法以及就标准化验证程序达成一致,对临床医生和临床研究人员具有相当大的转化价值。