PURPOSE To validate the efficacy of a fully automatic, deep learning-based segmentation algorithm beyond conventional performance metrics by measuring the primary outcome of a clinical trial for macular telangiectasia type 2 (MacTel2). DESIGN Evaluation of diagnostic test or technology. PARTICIPANTS A total of 92 eyes from 62 participants with MacTel2 from a phase 2 clinical trial (NCT01949324) randomized to 1 of 2 treatment groups METHODS: The ellipsoid zone (EZ) defect areas were measured on spectral domain OCT images of each eye at 2 time points (baseline and month 24) by a fully automatic, deep learning-based segmentation algorithm. The change in EZ defect area from baseline to month 24 was calculated and analyzed according to the clinical trial protocol. MAIN OUTCOME MEASURE Difference in the change in EZ defect area from baseline to month 24 between the 2 treatment groups. RESULTS The difference in the change in EZ defect area from baseline to month 24 between the 2 treatment groups measured by the fully automatic segmentation algorithm was 0.072±0.035 mm2 (P = 0.021). This was comparable to the outcome of the clinical trial using semiautomatic measurements by expert readers, 0.065±0.033 mm2 (P = 0.025). CONCLUSIONS The fully automatic segmentation algorithm was as accurate as semiautomatic expert segmentation to assess EZ defect areas and was able to reliably reproduce the statistically significant primary outcome measure of the clinical trial. This approach, to validate the performance of an automatic segmentation algorithm on the primary clinical trial end point, provides a robust gauge of its clinical applicability.

中文翻译：

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超越性能指标：自动深度学习视网膜OCT分析可再现临床试验结果。

目的通过测量2型黄斑毛细血管扩张症（MacTel2）的临床试验的主要结果，验证超越常规性能指标的全自动，基于深度学习的分割算法的有效性。设计评估诊断测试或技术。参与者来自来自2期临床试验（NCT01949324）的62名MacTel2参与者的92只眼睛，随机分配到2个治疗组中的1个方法。全自动，基于深度学习的细分算法（基准点和第24个月）积分。根据临床试验方案，计算并分析了从基线到第24个月EZ缺损区域的变化。主要观察指标2个治疗组之间从基线到第24个月EZ缺损区域变化的差异。结果全自动分割算法测得的两个治疗组之间，从基线到第24个月的EZ缺损面积变化的差异为0.072±0.035 mm2（P = 0.021）。这与专家阅读器使用半自动测量的临床试验结果相当，为0.065±0.033 mm2（P = 0.025）。结论全自动分割算法与评估EZ缺损区域的半自动专家分割一样准确，并且能够可靠地重现临床试验中具有统计学意义的主要结局指标。这种方法可以验证自动分割算法在主要临床试验终点上的性能，