Can Automated Imaging for Optic Disc and Retinal Nerve Fiber Layer Analysis Aid Glaucoma Detection?

Purpose: To compare the diagnostic performance of automated imaging for glaucoma.

Design: Prospective, direct comparison study.

Participants: Adults with suspected glaucoma or ocular hypertension referred to hospital eye services in the United Kingdom.

Methods: We evaluated 4 automated imaging test algorithms: the Heidelberg Retinal Tomography (HRT; Heidelberg Engineering, Heidelberg, Germany) glaucoma probability score (GPS), the HRT Moorfields regression analysis (MRA), scanning laser polarimetry (GDx enhanced corneal compensation; Glaucoma Diagnostics (GDx), Carl Zeiss Meditec, Dublin, CA) nerve fiber indicator (NFI), and Spectralis optical coherence tomography (OCT; Heidelberg Engineering) retinal nerve fiber layer (RNFL) classification. We defined abnormal tests as an automated classification of outside normal limits for HRT and OCT or NFI ≥ 56 (GDx). We conducted a sensitivity analysis, using borderline abnormal image classifications. The reference standard was clinical diagnosis by a masked glaucoma expert including standardized clinical assessment and automated perimetry. We analyzed 1 eye per patient (the one with more advanced disease). We also evaluated the performance according to severity and using a combination of 2 technologies.

Main Outcome Measures: Sensitivity and specificity, likelihood ratios, diagnostic, odds ratio, and proportion of indeterminate tests.

Results: We recruited 955 participants, and 943 were included in the analysis. The average age was 60.5 years (standard deviation, 13.8 years); 51.1% were women. Glaucoma was diagnosed in at least 1 eye in 16.8%; 32% of participants had no glaucoma-related findings. The HRT MRA had the highest sensitivity (87.0%; 95% confidence interval [CI], 80.2%-92.1%), but lowest specificity (63.9%; 95% CI, 60.2%-67.4%); GDx had the lowest sensitivity (35.1%; 95% CI, 27.0%-43.8%), but the highest specificity (97.2%; 95% CI, 95.6%-98.3%). The HRT GPS sensitivity was 81.5% (95% CI, 73.9%-87.6%), and specificity was 67.7% (95% CI, 64.2%-71.2%); OCT sensitivity was 76.9% (95% CI, 69.2%-83.4%), and specificity was 78.5% (95% CI, 75.4%-81.4%). Including only eyes with severe glaucoma, sensitivity increased: HRT MRA, HRT GPS, and OCT would miss 5% of eyes, and GDx would miss 21% of eyes. A combination of 2 different tests did not improve the accuracy substantially.

Conclusions: Automated imaging technologies can aid clinicians in diagnosing glaucoma, but may not replace current strategies because they can miss some cases of severe glaucoma.