Chapter-51 Problems of IOL Power Calculation in Pediatric Cataract Surgery

Abstract The aim of this study is to determine the customized refractive index of ectatic corneas and also propose a method for determining the corneal and IOL power in these eyes. Seven eyes with moderate and severe corneal ectatic disorders, which had been under cataract surgery, were included. At least three months after cataract surgery, axial length, cornea, IOL thickness and the distance between IOL from cornea, and aberrometry were measured. All the measured points of the posterior and anterior parts of the cornea converted to points cloud and surface by using the MATLAB and Solidworks software. The implanted IOLs were designed by Zemax software. The ray tracing analysis was performed on the customized eye models, and the corneal refractive index was determined by minimizing the difference between the measured aberrations from the device and resulted aberrations from the simulation. Then, by the use of preoperative corneal images, corneal power was calculated by considering the anterior and posterior parts of the cornea and refractive index of 1.376 and the customized corneal refractive index in different regions and finally it was entered into the IOL power calculation formulas. The corneal power in the 4 mm region and the Barrett formula resulted the prediction error of six eyes within ± 1 diopter. It seems that using the total corneal power along with the Barrett formula can prevent postoperative hyperopic shift, especially in eyes with advanced ectatic disorders.

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Cataract Surgery following Sequential Myopic and Hyperopic LASIK

Case Reports in Ophthalmology ◽

10.1159/000488849 ◽

2018 ◽

Vol 9 (2) ◽

pp. 264-268

Author(s):

Tao Ming Thomas Chia ◽

Hoon C. Jung

Keyword(s):

Cataract Surgery ◽

Intraocular Lens ◽

Calculation Method ◽

Cataract Extraction ◽

Power Calculation ◽

Iol Implantation ◽

Patient Dissatisfaction ◽

Iol Power Calculation ◽

Iol Power

We report a case of patient dissatisfaction after sequential myopic and hyperopic LASIK in the same eye. We discuss the course of management for this patient involving eventual cataract extraction and intraocular lens (IOL) implantation with attention to the IOL power calculation method used.

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Improving Intraocular Lens Calculation in Fuchs Endothelial Dystrophy According To Preoperative Degree of Corneal Edema

10.21203/rs.3.rs-727137/v1 ◽

2021 ◽

Author(s):

Beatriz Gargallo-Martinez ◽

Amanda Ortiz-Gomariz ◽

Ana Maria Gomez-Ramirez ◽

Angel Ramon Gutiérrez-Ortega ◽

Jose Javier Garcia-Medina

Keyword(s):

Cataract Surgery ◽

Intraocular Lens ◽

Refractive Error ◽

Target Selection ◽

Corneal Transplantation ◽

Corneal Edema ◽

Power Calculation ◽

Iol Power Calculation ◽

Iol Power ◽

Fuchs Endothelial Dystrophy

Abstract Fuchs endothelial dystrophy (FED) is a bilateral, asymmetric, progressive corneal endothelium disorder that causes corneal edema. Resolution of corneal edema is only possible by corneal transplantation. Cataract surgery is a common surgery that replaces the natural lens of the eye by an artificial intraocular lens (IOL). The IOL-power calculation depends mainly on the anterior corneal keratometry and the axial length. In patients with FED, anterior keratometry may be affected by corneal edema and calculations may be less accurate. Therefore, the aim of this study is to establish the theorical postoperative refractive error due to corneal edema resolution after Descemet stripping endothelial keratoplasty combined with cataract surgery and IOL implantation. For this, anterior keratometry was measure preoperatively with edematous cornea and postoperatively after corneal edema resolution. Both keratometries were compared and used to calculate the respective theorical IOL-powers. The difference between target IOLs was used to establish the theorical refractive error due to corneal edema resolution. The results showed that corneal edema resolution induces a change in anterior keratometry, which affects IOL-power calculations and causes a hyperopic shift. The patients with moderate-to-severe preoperative corneal edema had higher theorical refractive error so their target selection should be adjusted for additional − 0.50D.

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Improving the prediction of effective lens position for intraocular lens power calculations

Asian Journal of Ophthalmology ◽

10.35119/asjoo.v17i2.585 ◽

2020 ◽

Vol 17 (2) ◽

pp. 233-242

Author(s):

Juanita Noeline Chui ◽

Keith Ong

Keyword(s):

Cataract Surgery ◽

Intraocular Lens ◽

Sagittal Plane ◽

Anterior Chamber Depth ◽

Posterior Chamber ◽

Power Calculation ◽

Iol Power Calculation ◽

Lens Position ◽

Iol Power ◽

Corneal Apex

Purpose: Achieving the desired post-operative refraction in cataract surgery requires accurate calculations for intraocular lens (IOL) power. Latest-generation formulae use anterior-chamber depth (ACD)—the distance from the corneal apex to the anterior surface of the lens—as one of the parameters to predict the post-operative IOL position within the eye, termed the effective lens position (ELP). Significant discrepancies between predicted and actual ELP result in refractive surprise. This study aims to improve the predictability of ELP. We hypothesise that predictions based on the distance from the corneal apex to the mid-sagittal plane of the cataractous lens would more accurately reflect the position of the principal plane of the non-angulated IOL within the capsular bag. Accordingly, we propose that predictions derived from ACD + ½LT (length thickness) would be superior to those from ACD alone. Design: Retrospective cohort study, comparing ELP predictions derived from ACD to aproposed prediction parameter. Method: This retrospective study includes data from 162 consecutive cataract surgery cases, with posterior-chamber IOL (AlconSN60WF) implantation. Pre- and postoperative biometric measurements were made using the IOLMaster700 (ZEISS, Jena, Germany). The accuracy and reliability of ELP predictions derived from ACD and ACD + ½LT were compared using software-aided analyses. Results: An overall reduction in average ELP prediction error (PEELP) was achieved using the proposed parameter (root-mean-square-error [RMSE] = 0.50 mm), compared to ACD (RMSE = 1.57 mm). The mean percentage PEELP, comparing between eyes of different axial lengths, was 9.88% ± 3.48% and −34.9% ± 4.79% for predictions derived from ACD + ½LT and ACD, respectively. A 44.10% ± 5.22% mean of differences was observed (p < 0.001). Conclusion: ACD + ½LT predicts ELP with greater accuracy and reliability than ACD alone; its use in IOL power calculation formulae may improve refractive outcomes.

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Faculty Opinions recommendation of Comparison of IOL power calculation and refractive outcome after laser refractive cataract surgery with a femtosecond laser versus conventional phacoemulsification.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717970178.793468915 ◽

2013 ◽

Author(s):

Karl Stonecipher

Keyword(s):

Femtosecond Laser ◽

Cataract Surgery ◽

Power Calculation ◽

Refractive Outcome ◽

Iol Power Calculation ◽

Iol Power

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Use of a Machine Learning Method in Predicting Refraction after Cataract Surgery

Journal of Clinical Medicine ◽

10.3390/jcm10051103 ◽

2021 ◽

Vol 10 (5) ◽

pp. 1103

Author(s):

Tomofusa Yamauchi ◽

Hitoshi Tabuchi ◽

Kosuke Takase ◽

Hiroki Masumoto

Keyword(s):

Machine Learning ◽

Cataract Surgery ◽

Test Data ◽

Training Data ◽

Gradient Boosting ◽

Support Vector ◽

Power Calculation ◽

Iol Power Calculation ◽

Significant Difference ◽

Iol Power

The present study aims to describe the use of machine learning (ML) in predicting the occurrence of postoperative refraction after cataract surgery and compares the accuracy of this method to conventional intraocular lens (IOL) power calculation formulas. In total, 3331 eyes from 2010 patients were assessed. The objects were divided into training data and test data. The constants for the IOL power calculation formulas and model training for ML were optimized using training data. Then, the occurrence of postoperative refraction was predicted using conventional formulas, or ML models were calculated using the test data. We evaluated the SRK/T formula, Haigis formula, Holladay 1 formula, Hoffer Q formula, and Barrett Universal II formula (BU-II); similar to ML methods, we assessed support vector regression (SVR), random forest regression (RFR), gradient boosting regression (GBR), and neural network (NN). Among the conventional formulas, BU-II had the lowest mean and median absolute error of prediction. Therefore, we compared the accuracy of our method with that of BU-II. The absolute errors of some ML methods were lower than those of BU-II. However, no statistically significant difference was observed. Thus, the accuracy of our method was not inferior to that of BU-II.

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