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Open access Jul 2026

An explainable machine learning framework for accurate prediction of postoperative anterior chamber depth in highly myopic cataract surgery

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. · 0 citations
Open access Jul 2026

The Quest to Predict Surgically Induced Astigmatism After Cataract Surgery: Lessons for Toric IOL Planning

Abstract Purpose This study compared traditional statistical models with machine learning algorithms for predicting surgically induced astigmatism after cataract surgery, aiming to identify the most accurate and generalizable method among linear regression, regression trees, random forests, and neural networks. Methods Retrospective analysis was performed on 321 eyes (321 patients) undergoing phacoemulsification at a tertiary center. Data were standardized and surgically induced astigmatism was modeled using three vector-based outcomes: KEQ.post (keratometric equivalent power), KAST0.post (horizontal astigmatism component), and KAST45.post (oblique component). Preoperative variables served as predictors. Data were split into training (60%) and test (40%) sets. Four predictive models were developed and evaluated on unseen data. Performance was assessed using mean squared prediction error, and variable importance analyses identified key predictors. Results Linear regression achieved the best out-of-sample performance across all outcomes (e.g. KEQ.post mean squared prediction error = 0.043). Tree-based models performed slightly worse, while neural networks showed substantial overfitting with markedly higher test errors. Preoperative astigmatism and corneal radii were the strongest predictors. Conclusion KAST0 and KAST45 were not predictably modeled, whereas changes in KEQ.post were. Multivariable linear regression provided the most accurate and reliable predictions, while more complex machine-learning models (especially neural networks) overfit the limited dataset and offered no clinical advantage. The relationship between preoperative biometrics and postoperative equivalent power appears predominantly linear, though larger datasets may enhance machine-learning performance.

Amanda Pan, K. P. Kaiser, Stefan Raidl et al. · 0 citations
Open access Aug 2026

Role of surgeon seniority in predicting surgically induced astigmatism after phacoemulsification surgery: a machine learning study

Surgeon seniority was not a significant determinant of surgically induced astigmatism after phacoemulsification cataract surgery, and machine-learning models based on preoperative clinical data provided only limited predictive value, particularly for vector astigmatism outcomes.

Denizcan Özizmirliler, C. Engin, Özlem Özkan et al. · 0 citations
Open access Jul 2026

Predicting gingival embrasure risk after invisible orthodontics using multimodal data and machine learning

A risk prediction model for post-clear aligner gingival embrasures was successfully developed and validated using multimodal oral data, with RF as the optimal algorithm that exhibits good discrimination, calibration, and clinical utility.

Haiyan Wang, Hanfei Shi, Liping Fan et al. · 0 citations
Open access Jul 2026

Development of Machine Learning Models for Predicting Surgical Site Infection After Spinal Surgery

Machine learning models showed acceptable performance for predicting postoperative SSI after spinal surgery, suggesting that conventional statistical approaches may remain clinically useful in structured datasets.

Kwang-Ryeol Kim, Gi-Young Park, Dong Hyuck Kim et al. · 0 citations
Open access Jul 2026

Preoperative artificial intelligence-based risk model for surgical reintervention after microsurgical free flap reconstruction.

XGBoost is retained as the principal model based on combined superiority in discrimination and calibration, with random forest as a robust comparator, and prospective external validation with recalibration is required before clinical adoption.

Luis Arturo Molina Laguna, A. Porras-Ramírez, Giovanni Montealegre et al. · 0 citations

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