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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

The relationship between preoperative biometrics and postoperative equivalent power appears predominantly linear, Though larger datasets may enhance machine-learning performance, 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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