ArticleTranslational vision science & technology2020
Gradient Boosting Decision Tree Algorithm for the Prediction of Postoperative Intraocular Lens Position in Cataract Surgery.
Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Data Volume and the Need for Clinical Decision Support in Glaucoma Care.Ophthalmology science · 2026Article
- A comparative study on the influence of age-specific ocular anatomical differences on the prediction of post-ICL surgery vault.BMC ophthalmology · 2026Article
- An explainable machine learning framework for accurate prediction of postoperative anterior chamber depth in highly myopic cataract surgery.Frontiers in cell and developmental biology · 2026Article
- Using Natural Language Processing to Identify Different Lens Pathology in Electronic Health Records.American journal of ophthalmology · 2024Article
- Research progress on prediction of postoperative intraocular lens position.Indian journal of ophthalmology · 2024Article
- A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records.BMC cardiovascular disorders · 2024Article
- Evaluation of the Nallasamy formula: a stacking ensemble machine learning method for refraction prediction in cataract surgery.The British journal of ophthalmology · 2023Article
- Ray tracing intraocular lens calculation performance improved by AI-powered postoperative lens position prediction.The British journal of ophthalmology · 2023Article
- Identification of essential tremor based on resting-state functional connectivity.Human brain mapping · 2023Article
- MAEPI and CIR: New Metrics for Robust Evaluation of the Prediction Performance of AI-Based IOL Formulas.Translational vision science & technology · 2023Article
- An explainable artificial intelligence system for diagnosingTherapeutic advances in gastroenterology · 2023Article
- Radiomics Analysis of Breast Lesions in Combination with Coronal Plane of ABVS and Strain Elastography.Breast cancer (Dove Medical Press) · 2023Article
- Data Sources for Evaluating Health Disparities in Ophthalmology: Where We Are and Where We Need to Go.Ophthalmology · 2022Article
- AI-powered effective lens position prediction improves the accuracy of existing lens formulas.The British journal of ophthalmology · 2022Article
- Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study.Healthcare (Basel, Switzerland) · 2022Article
- Differential Diagnosis of Type 1 and Type 2 Papillary Renal Cell Carcinoma Based on Enhanced CT Radiomics Nomogram.Frontiers in oncology · 2022Article
- A Novel Clinical Radiomics Nomogram to Identify Crohn's Disease from Intestinal Tuberculosis.Journal of inflammation research · 2021Article
- AI-Powered Effective Lens Position Prediction Improves the Accuracy of Existing Lens Formulas.medRxiv : the preprint server for health sciences · 2020Article
- CatSkill: Artificial Intelligence-Based Metrics for the Assessment of Surgical Skill Level from Intraoperative Cataract Surgery Video Recordings.Ophthalmology scienceArticle
- Incidence of Acute Cystoid Macular Edema after Starting a Prostaglandin Analog Compared with Other Classes of Glaucoma Medications.Ophthalmology. GlaucomaArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Purpose: To develop a method for predicting postoperative anterior chamber depth (ACD) in cataract surgery patients based on preoperative biometry, demographics, and intraocular lens (IOL) power. Methods: Patients who underwent cataract surgery and had both preoperative and postoperative biometry measurements were included. Patient demographics and IOL power were collected from the Sight Outcomes Research Collaborative (SOURCE) database. A gradient-boosting decision tree model was developed to predict the postoperative ACD. The mean absolute error (MAE) and median absolute error (MedAE) were used as evaluation metrics. The performance of the proposed method was compared with five existing formulas. Results: In total, 847 patients were assigned randomly in a 4:1 ratio to a training/validation set (678 patients) and a testing set (169 patients). Using preoperative biometry and patient sex as predictors, the presented method achieved an MAE of 0.106 ± 0.098 (SD) on the testing set, and a MedAE of 0.082. MAE was significantly lower than that of the five existing methods ( Conclusions: The presented machine learning method achieved greater accuracy than previously reported methods for the prediction of postoperative ACD. Translational Relevance: Increasing accuracy of postoperative ACD prediction with the presented algorithm has the potential to improve refractive outcomes in cataract surgery.
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