Preoperative predictive factors for opaque bubble layer formation and area during small-incision lenticule...

We aimed to develop exploratory machine learning (ML)-based predictive models for the occurrence and area of an opaque bubble layer (OBL) during small-incision lenticule extraction (SMILE) and identify associated preoperative and surgical-planning factors.

This retrospective study included 216 eyes (72 with an OBL, 144 controls) that underwent SMILE at the Zhongshan Ophthalmic Center between August 2024 and August 2025, which were matched 1:2 by age and sex. Comprehensive preoperative ocular examinations were performed using a Pentacam HR camera system and standard equipment. The OBL area was quantified from intraoperative videos using ImageJ software. Fifty-four preoperative and surgical features were used to develop the ML models: 15 classification algorithms for OBL occurrence, and 19 regression algorithms for the relative OBL area. Shapley additive explanations analysis was applied for model interpretability.

The Extra Trees model achieved optimal performance for predicting OBL occurrence (area under the receiver operating characteristic curve = 0.885, accuracy = 0.820). The top five key factors included femtosecond laser energy, intraocular pressure, residual stromal thickness, 10-mm corneal volume, and total astigmatism, all of which showed positive correlations. For OBL area prediction, the random forest regression model performed best in the test sets (mean absolute error = 2.89%, root mean square error = 3.37%). Corneal optical density (within the central 2-mm zone of the anterior 120-μm corneal layer) and age were negatively associated with OBL area, whereas keratoconus index showed the strongest positive association.

The ML models showed exploratory predictive ability for OBL occurrence and area during SMILE using preoperative and surgical-planning parameters. External validation and recalibration in consecutive cohorts with a natural OBL prevalence are required before clinical use.