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.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.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.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.Copyright: © 2025 Lee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.