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.
Abstract
The predictive modeling of postoperative mechanical complications, such as the early micro-rotation of premium toric intraocular lenses (IOLs), is severely hindered by the extreme imbalance of clinical datasets. Traditional statistical methods often fail to capture complex biomechanical interactions in rare-event scenarios. 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. A prospective cohort of 35 eyes implanted with the Clareon PanOptix® Toric IOL was analyzed, quantifying true rotational stability via high-resolution photographic registration. Given the exceedingly low incidence of > 1-degree micro-rotation, a strict, leak-proof fivefold cross-validation pipeline was established. The Synthetic Minority Over-sampling Technique (SMOTE) was applied exclusively within the training folds, and an interpretable Linear Support Vector Machine (Linear SVM) was deployed to extract robust feature weights for biomechanical interpretation. Our findings highlight the "accuracy paradox" in small clinical datasets: complex ensemble models exhibited severe majority-class bias, failing to detect rare micro-rotations. Conversely, the SMOTE-enhanced Linear SVM achieved a Precision-Recall Area Under the Curve (PR-AUC) of 0.463, outperforming a random baseline by nearly a factor of three. The algorithmic feature weights successfully isolated Anterior Chamber Depth (ACD) and steep keratometry (Steep K2) as the primary geometric drivers of rotational instability, demonstrating a profound alignment with clinical ocular biomechanics. While strictly constrained by the small sample size (N = 35) and limited event rate, this preliminary pilot framework successfully bridges high-dimensional data augmentation with physical ocular biomechanics, effectively identifying minority risk features and laying the groundwork for future AI-driven surgical navigation systems.
This study successfully developed and validated an explainable RF-based machine learning model for the prediction of postoperative ACD in highly myopic cataract patients, which supports more reliable IOL power calculation and offers a practical tool for optimizing surgical planning in highly myopic eyes.
Yuyang Yang, Hao Cui, Jiajia Gao et al.· Frontiers in Cell and Develo...· 0 citations
Structured preoperative variables can partially reproduce single-center clinician-selected refractive procedure patterns but do not establish optimal surgical recommendation or external generalizability.
Yinhao Li, Gang Li, Chuanyun Xu et al.· BMC Ophthalmology· 0 citations
ICLGuru demonstrated useful postoperative vault predictions with performance comparable to existing machine learning and UBM-based nomograms, though it appeared to overestimate longer term vault in the authors' cohort, and may be valuable in identifying eyes at risk for excessive vault.
Sanjana Molleti, Ethan J. Lindberg, Hanna Pawlowski et al.· Clinical Ophthalmology· 0 citations
Background The early detection of idiopathic corneal endothelial compromise, a common yet underdiagnosed pre-cataract finding, remains a challenge. This study aimed to investigate the association between corneal biomechanical parameters and endothelial status, identify potential biomechanical markers, and explore the biomechanical manifestations of idiopathic endothelial impairment. Methods In this cross-sectional study, cataract patients with normal endothelium or idiopathic endothelial abnormality underwent Corneal Visualization Scheimpflug Technology (Corvis ST) tonometry and specular microscopy. Beyond conventional statistics, we employed stability selection—a machine learning method with explicit error control—to identify the most reproducible predictors from a multitude of biomechanical parameters, while rigorously accounting for central corneal thickness (CCT) via stratified analysis and propensity score matching (PSM). Results Among 241 patients (35 with endothelial abnormality), five biomechanical parameters (SSI, HCR, A2L, A1T, cTBI) significantly differed between groups, even after PSM for CCT. Stratified analysis revealed the strongest biomechanical-endothelial correlations specifically within the mid-range CCT (530–570 μm). A2L was identified as an independent protective factor (OR: 0.202, 95%CI: 0.051–0.731, p = 0.018). Stability selection confirmed A2L and cTBI as the most robust markers. A novel DASC model (incorporating A2L, DA, SSI, and CCT) achieved an AUC of 0.711 for detecting endothelial abnormalities, comparable to the cTBI score (AUC: 0.683, 95%CI: 0.565–0.800). Conclusion Corneal biomechanics reflect early endothelial alterations. A2L, cTBI, and the DASC model serve as promising, noninvasive tools for risk stratification. Our findings suggest that subclinical endothelial impairment manifests as reduced corneal stiffness-related response and impaired deformation behavior.
Chengjie Feng, Miao-miao Chi, Shaofeng Gu et al.· Frontiers in Medicine· 0 citations
OBJECTIVE
Normal-tension glaucoma (NTG) is characterized by progressive optic nerve damage despite intraocular pressure remaining consistently within the normal range. Predicting disease progression in patients with confirmed NTG remains challenging. This study aimed to develop and validate an interpretable machine learning model integrating genetic risk scores and corneal biomechanical parameters to predict progression risk in patients with NTG, identify independent predictors, and quantify the contribution of individual features to support precision clinical management.
METHODS
A total of 342 patients with NTG were consecutively enrolled at a tertiary hospital and randomly allocated to training set (n = 238) and validation set (n = 104) at a ratio of 7:3. Baseline characteristics and six core indicators were collected. Candidate predictors were selected through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation and the λ-1se criterion. Independent predictors were subsequently identified using multivariable logistic regression. Three machine learning models-random forest (RF), support vector machine (SVM), and logistic regression (LR)-were developed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) analysis was performed to interpret feature contributions.
RESULTS
Univariate analysis revealed significant differences in all six indicators between the progression and non-progression groups (p < .05). Multivariable logistic regression further confirmed that all six indicators were independently associated with NTG progression (p < .05). The RF model demonstrated the best predictive performance, with an AUC of 0.760 (95% confidence interval (CI) : 0.678-0.842) in the training set and 0.747 (95% CI: 0.623-0.871) in the validation set. It outperformed both the SVM model (training AUC = 0.704; validation AUC = 0.694) and the LR model (training AUC = 0.742; validation AUC = 0.729). SHAP analysis ranked the features, in descending order of contribution, as mean retinal nerve fibre layer (RNFL) thickness, first applanation velocity, visual field mean deviation, relative tear GNAI1 level, polygenic risk score for NTG, and relative tear PRDX4 level. The calibration curves showed good agreement between predicted and observed probabilities, while DCA demonstrated a high clinical net benefit across a broad range of threshold probabilities.
CONCLUSION
A model integrating corneal biomechanical parameters, genetic risk scores, and tear molecular biomarkers was developed to predict the risk of NTG progression and demonstrated potential clinical utility. This model may provide a quantitative reference for risk stratification and personalised management in patients with NTG.
OBJECTIVE
To identify risk factors for persistent dry eye after refractive surgery in patients with ultra-high myopia and their prognostic value.
METHODS
Data from 260 patients were analyzed. Least absolute shrinkage and selection operator regression screened preoperative variables. Random forest, Extreme Gradient Boosting, multivariate logistic regression, mediation, and interaction analyses were used to identify predictors and mechanisms of persistent dry eye (≥ 3 months).
RESULTS
Persistent dry eye occurred in 33.1% of patients (86/260). Eight preoperative predictors were identified. Elevated Ocular Surface Disease Index (OSDI) (odds ratio [OR] = 1.483) and corneal fluorescein staining (CFS) (OR = 7.154) were independent risk factors; prolonged tear film breakup time (TBUT) (OR = 0.292), no systemic history (OR = 0.042), and normal meibomian gland dysfunction (MGD) (OR = 0.042) were protective. Age and Schirmer test were marginally significant. Medication adherence fully mediated the effect of age on dry eye duration, while ocular inflammation partly mediated the effect of photorefractive keratectomy surgery. A significant synergistic interaction was found between postoperative inflammation and medication adherence (P < 0.05).
CONCLUSION
Persistent dry eye after refractive surgery for ultra-high myopia is influenced by multiple factors. Preoperative OSDI, CFS, TBUT, systemic history, MGD, daily screen time, medication adherence, and inflammation play important roles. Individualized perioperative management targeting these factors may improve prognosis.
Guike Li, Xinyu Shen, Juan Wu et al.· American journal of translat...· 0 citations