DESIGN/METHODS: A non-randomized prospective study was conducted for various types of pterygium excision with superior bulbar conjunctival autograft (CAG) and fibrin glue. We introduced fluorescein staining to ensure thorough elimination of the Tenon tissue around the bare sclera area and the CAG. The primary outcome was the recurrence rate, and the secondary outcome was any complication associated with fluorescein staining.
RESULTS: Ninety-three participants with primary pterygium of Grades 1-3 were recruited and all completed follow-up for at least 1 year. No recurrence was identified during the follow-up period and no long-term adverse reactions were reported with the "hydro-fluorescein" method.
CONCLUSION: "Hydro-fluorescein" is effective and a safe adjunct in primary pterygium removal and is effective in various grades of pterygia to minimize recurrence with no adverse reaction within 1 year.
Aim: To produce an accurate model of BCVA changes of postpterygium surgery according to its morphological characteristics by using the machine learning technique. Methodology. A retrospective of the secondary dataset of 93 samples of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery.
Results: The performance of four machine learning techniques were evaluated, and it showed the support vector machine (SVM) model had the highest average accuracy (94.44% ± 5.86%), specificity (100%), and sensitivity (92.14% ± 8.33%).
Conclusion: Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.