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Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome
Ophthalmology ( IF 7.732 ) Pub Date : 2019-12-23 , DOI: 10.1016/j.ophtha.2019.12.015
Jessica Loo; Traci E. Clemons; Emily Y. Chew; Martin Friedlander; Glenn J. Jaffe; Sina Farsiu

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 92 eyes from 62 participants with MacTel2 from a phase 2 clinical trial (NCT01949324) randomized to one of two treatment groups Methods The ellipsoid zone (EZ) defect areas were measured on spectral domain optical coherence tomography images of each eye at two 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. Primary Outcome Measure Difference in the change in EZ defect area from Baseline to Month 24 between the two treatment groups. Results The difference in the change in EZ defect area from Baseline to Month 24 between the two 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 semi-automatic measurements by expert Readers, 0.065 ± 0.033 mm2 (p = 0.025). Conclusions The fully-automatic segmentation algorithm was as accurate as semi-automatic 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 endpoint, provides a robust gauge of its clinical applicability.
更新日期:2019-12-23

 

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