Open-Source Automatic Biomarker Measurement on Slit-Lamp Photography to Estimate Visual Acuity in Microbial Keratitis

Abstract

Purpose: To assess clinical applicability of automatic image analysis in microbial keratitis (MK) by evaluating the relationship between biomarker measurements on slit-lamp photography (SLP) and best-corrected visual acuity (BCVA).

Methods: Seventy-six patients with MK with SLP images and same-day logarithm of the minimum angle of resolution (logMAR) BCVA were evaluated. MK biomarkers (stromal infiltrate, white blood cell infiltration, corneal edema, hypopyon, epithelial defect) were segmented manually by ophthalmologists and automatically by a novel, open-source, deep learning-based segmentation algorithm. Five measurements (presence, maximum width, total area, proportion of the corneal limbus area affected, centrality) were calculated. Correlations between the measurements and BCVA were calculated. An automatic regression model estimated BCVA from the measurements. Differences in performance between using manual and automatic measurements were evaluated using William's test (for correlation) and the paired-sample t-test (for absolute error).

Results: Measurements had high correlations of 0.86 (manual) and 0.84 (automatic) with true BCVA. Estimated BCVA had average (mean ± SD) absolute errors of 0.39 ± 0.27 logMAR (manual, median: 0.30) and 0.35 ± 0.28 logMAR (automatic, median: 0.30) and high correlations of 0.76 (manual) and 0.80 (automatic) with true BCVA. Differences between using manual and automatic measurements were not statistically significant for correlations of measurements with true BCVA (P = .66), absolute errors of estimated BCVA (P = .15), or correlations of estimated BCVA with true BCVA (P = .60).

Conclusions: The proposed algorithm measured MK biomarkers as accurately as ophthalmologists. Measurements were highly correlated with and estimative of visual acuity.

Translational relevance: This study demonstrates the potential of developing fully automatic objective and standardized strategies to aid ophthalmologists in the clinical assessment of MK.

Figure 1.

Scatterplot showing the relationship between the true BCVA and estimated BCVA of the BCVA estimation algorithm. The difference in performance between using manual and automatic segmentations was not statistically significant.

Figure 2.

Example of SLP images with manual and automatic segmentations. The estimated BCVA was 0.7 logMAR and 0.5 logMAR using the measurements obtained from manual and automatic segmentations, respectively. The true BCVA was 0.5 logMAR.

Figure 3.

Example of SLP images with manual and automatic segmentations. The estimated BCVA was 1.7 logMAR using both measurements obtained from manual and automatic segmentations. The true BCVA was 2.0 logMAR.

Figure 4.

Example of SLP images with manual and automatic segmentations. The estimated BCVA was 0.8 logMAR and 0.9 logMAR using the measurements obtained from manual and automatic segmentations, respectively. The true BCVA was 0.4 logMAR.

Figure 5.

Example of SLP images with manual and automatic segmentations. The estimated BCVA was 1.1 logMAR using both measurements obtained from manual and automatic segmentations. The true BCVA was 0.8 logMAR.