An exploratory SMOTE-SVM approach for identifying preoperative biomechanical risk factors driving early toric intraocular lens micro-rotation in extremely imbalanced cohorts.
This exploratory pilot study introduces a machine learning framework designed as a hypothesis-generating tool to handle extremely imbalanced ophthalmic data and identify potential preoperative biometric features associated with toric IOL micro-rotation, effectively identifying minority risk features and laying the groundwork for future AI-driven surgical navigation systems.