Conjunctival Bulbar Redness Extraction Pipeline for High-Resolution Ocular Surface Photography.

Purpose: To extract conjunctival bulbar redness from standardized high-resolution ocular surface photographs of a novel imaging system by implementing an image analysis pipeline.

Methods: Data from two trials (healthy; outgoing ophthalmic clinic) were collected, processed, and used to train a machine learning model for ocular surface segmentation. Various regions of interest were defined to globally and locally extract a redness biomarker based on color intensity. The image-based redness scores were correlated to clinical gradings (Efron) for validation.

Results: The model to determine the regions of interest was verified for a segmentation performance, yielding mean intersections over union of 0.9639 (iris) and 0.9731 (ocular surface). All trial data were analyzed and a digital grading scale for the novel imaging system was established. Photographs and redness scores from visits weeks apart showed good feasibility and reproducibility. For scores within the same session, a mean coefficient of variation of 4.09% was observed. A moderate positive Spearman correlation (0.599) was found with clinical grading.

Conclusions: The proposed conjunctival bulbar redness extraction pipeline demonstrates that by using standardized imaging, a segmentation model and image-based redness scores' external eye photography can be classified and evaluated. Therefore, it shows the potential to provide eye care professionals with an objective tool to grade ocular redness and facilitate clinical decision-making in a high-throughput manner.

Translational relevance: To empower clinicians and researchers with a high-throughput workflow by standardized imaging combined with an analysis tool based on artificial intelligence to objectively determine an image-based redness score.