AI-driven Eyeball Exposure Rate (EER) analysis: A useful tool for assessing ptosis surgery effectiveness

Abstract

Introduction Ptosis surgery outcomes are measured by one-dimensional metrics like Marginal Reflex Distance (MRD) and Palpebral Fissure Height (PFH) using ImageJ. However, these methods are insufficient to capture the full range of changes post-surgery. Eyeball Exposure Rate (EER) offers a more comprehensive two-dimensional perspective as metric. This study compares AI-based EER measurements with conventional ImageJ methods for assessing outcome of ptosis surgery. Methods: Images from 50 patients (total 100 eyes) taken before and after surgery were analyzed using manual ImageJ and the AI-tool “Anigma-View”. Statistical tests assessed the accuracy and consistency of both methods, using intraclass correlation coefficients (ICCs) and Bland-Altman plots for comparison. Results EER measured by the AI-tool at pre- and post-operation were 58.85% and 75.36%, respectively. Similarly, manual measurements using ImageJ showed an increase from 58.22% to 75.27%. The Intraclass Correlation Coefficients (ICCs) between the AI-tool and manual measurements ranged from 0.984 to 0.994, indicating excellent agreement, with the repeated AI-tool demonstrating high reproducibility (ICC =  1). Bland-Altman plots showed excellent agreement between the two methods and reproducibility of AI-based measurements. Additionally, EER improvement was more prominent in the moderate to severe ptosis group with a 45.94% increase, compared to the mild group with 14.39% increase. Discussion The findings revealed no significant differences between AI-tool and manual methods, suggesting that AI-tool is just as reliable. AI-tool to automate measurements offers efficiency and objectivity, making it a valuable method in clinical fields. Conclusion AI-based EER analysis is accurate and efficient, providing comparable results to manual methods. Its ability to simplify surgical outcome assessments makes it a promising addition to clinical practice. Further exploration of AI in evaluating three-dimensional changes in ptosis surgery could enhance future surgical assessments and outcomes.