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

Yamauchi, Tomofusa; Tabuchi, Hitoshi; Takase, Kosuke; Masumoto, Hiroki

doi:10.3390/jcm10051103

Cited by 11 publications

(8 citation statements)

References 24 publications

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“…The adapted IOL power calculation was designed for refining the predicted refractions delivered from the conventional SRK/T formula, while most of the previous approaches did not utilize the conventional formulas [ 3 , 5 , 17 , 19 , 20 ]. As the training was focused on refining the prediction of the SRK/T formula, the accuracy was obtained with a limited size of the training dataset, as demonstrated previously [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

“…The effectiveness for other types of IOL was a concern. Previous assessments of machine-learning based calculations demonstrated the use for other IOL models with open-loop design [ 5 , 17 , 19 ]. In contrast, for IOLs with plate or loop haptics, postoperative position varies with the geometric difference in crystalline lens [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

“…Ladas et al revealed that SVR supervised nonlinear regression machine learning was suitable for optimization of existing IOL power calculation formulas, compared with extreme gradient boosting (XGBoost) and artificial neural network (ANN) [ 18 ]. In the evaluation of various machine learning algorithms with a training dataset of 2,831 eyes of 1,659 patients with 13 kinds of IOLs by Yamauchi et al, superior performances were obtained with SVR over conventional formulas [ 19 ]. While the sample size was insufficient and/or multiple IOL models were used for training and evaluation, the previous approaches demonstrated the potential of machine learning for obtaining a power calculation suitable for a particular patient group.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning adaptation of intraocular lens power calculation for a patient group

Mori

Yamauchi²,

Tokuda

et al. 2021

Eye and Vis

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Background To examine the effectiveness of the use of machine learning for adapting an intraocular lens (IOL) power calculation for a patient group. Methods In this retrospective study, the clinical records of 1,611 eyes of 1,169 Japanese patients who received a single model of monofocal IOL (SN60WF, Alcon) at Miyata Eye Hospital were reviewed and analyzed. Using biometric metrics and postoperative refractions of 1211 eyes of 769 patients, constants of the SRK/T and Haigis formulas were optimized. The SRK/T formula was adapted using a support vector regressor. Prediction errors in the use of adapted formulas as well as the SRK/T, Haigis, Hill-RBF and Barrett Universal II formulas were evaluated with data from 395 eyes of 395 distinct patients. Mean prediction errors, median absolute errors, and percentages of eyes within ± 0.25 D, ± 0.50 D, and ± 1.00 D, and over + 0.50 D of errors were compared among formulas. Results The mean prediction errors in the use of the SRT/K and adapted formulas were smaller than the use of other formulas (P < 0.001). In the absolute errors, the Hill-RBF and adapted methods were better than others. The performance of the Barrett Universal II was not better than the others for the patient group. There were the least eyes with hyperopic refractive errors (16.5%) in the use of the adapted formula. Conclusions Adapting IOL power calculations using machine learning technology with data from a particular patient group was effective and promising.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning adaptation of intraocular lens power calculation for a patient group

Mori

Yamauchi²,

Tokuda

et al. 2021

Eye and Vis

Self Cite

View full text Add to dashboard Cite

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“…To reduce the time needed for this process, machine learning has been recently used to predict refractive prescription from physical eye data obtained by wavefront aberrometers, 19 , 20 photorefraction images, 21 retinal fundus images, 22 other ophthalmologic devices, 23 and intraocular lenses characteristics. 24 The aim of this work is to develop a machine learning regression model that predicts patients’ subjective refractive prescription from the anterior corneal surface and ocular biometry. The first step consists of choosing physiologic descriptors as the model's features.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Subjective Refraction From Anterior Corneal Surface, Eye Lengths, and Age Using Machine Learning Algorithms

Espinosa

Pérez

Villanueva

2022

Trans. Vis. Sci. Tech.

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Purpose To develop a machine learning regression model of subjective refractive prescription from minimum ocular biometry and corneal topography features. Methods Anterior corneal surface parameters (Zernike coefficients and keratometry), axial length, anterior chamber depth, and age were posed as features to predict subjective refractions. Measurements from 355 eyes were split into training (75%) and test (25%) sets. Different machine learning regression algorithms were trained by 10-fold cross-validation, optimized, and tested. A neighborhood component analysis provided features’ normalized weights in predictions. Results Gaussian process regression algorithms provided the best models with mean absolute errors of around 1.00 diopters (D) in the spherical component and 0.15 D in the astigmatic components. Conclusions The normalized weights showed that subjective refraction can be predicted by only keratometry, age, and axial length. Increasing the topographic description detail of the anterior corneal surface implied by a high-order Zernike decomposition versus adjustment to a spherocylindrical surface is not reflected as improved subjective refraction prediction, which is poor, mainly in the spherical component. However, the highest achievable accuracy differs by only 0.75 D from that of other works with a more exhaustive eye refractive elements description. Although the chosen parameters may have not been the most efficient, applying machine learning and big data to predict subjective refraction can be risky and impractical when evaluating a particular subject at statistical extremes. Translational Relevance This work evaluates subjective refraction prediction by machine learning from the anterior corneal surface and ocular biometry. It shows the minimum biometric information required and the highest achievable accuracy. RESUMEN Objetivo El desarrollo de un modelo de regresión de aprendizaje automático prescripción refractiva subjetiva a partir de las características mínimas de la biometría ocular y la superficie corneal. Métodos Los parámetros de la superficie corneal anterior (coeficientes de Zernike y queratometría), además de longitudes axiales y de cámara anterior, edades y las refracciones subjetivas no ciclopléjicas de 355 ojos se dividieron en un conjunto de entrenamiento (75%) y otro de test (25%) y se entrenaron diferentes algoritmos de regresión de aprendizaje automático mediante validación cruzada 10 veces, se optimizaron y se probaron sobre el conjunto test. Resultados Los algoritmos de regresión del proceso gaussiano proporcionaron los mejores modelos con un error absoluto medio fue de alrededor de 1.00 D en el componente esférico y de 0.25 D en los componentes astigmáticos. Conclusiones Los pesos normalizados mostraron que la refracción sub...

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“…However, key limitations exist among recently-published ML-based IOL calculation methods: (1) performance comparisons limited to older generation formulas, [6] (2) failure to achieve statistically significant improvement over current generation formulas, [7] and (3) small datasets that leave the robustness and generalizability of methods in question. [8] With a goal of advancing the understanding of IOL power selection for general cataract patients and improving refraction prediction accuracy, in the presented study, we developed a novel machine learningbased IOL power calculation method, the Nallasamy formula, based on a large dataset of 5016 cataract patients.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Nallasamy Formula: A Stacking Ensemble Machine Learning Method for Refraction Prediction in Cataract Surgery

Stein

Nallasamy

2021

Preprint

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Aims: To develop a new intraocular lens (IOL) power selection method with improved accuracy for general cataract patients receiving Alcon SN60WF lenses. Methods and Analysis: A total of 5016 patients (6893 eyes) who underwent cataract surgery at University of Michigan's Kellogg Eye Center and received the Alcon SN60WF lens were included in the study. A machine learning-based method was developed using a training dataset of 4013 patients (5890 eyes), and evaluated on a testing dataset of 1003 patients (1003 eyes). Each eye had a complete profile of preoperative biometry, the implanted IOL power, and postoperative refraction. The performance of our method was compared to that of Barrett Universal II, Haigis, Hoffer Q, Holladay 1, and SRK/T. Results: MAE of the Nallasamy formula in the testing dataset was 0.312 Diopters (MedAE = 0.242 D). Performance of existing methods were as follows: Barrett Universal II MAE = 0.328 D (MedAE = 0.256 D), Haigis MAE = 0.363 D (MedAE = 0.289 D), Hoffer Q MAE = 0.404 D (MedAE = 0.331 D), Holladay 1 MAE = 0.371 D (MedAE = 0.298 D) and SRK/T MAE = 0.376 D (MedAE = 0.300 D). The Nallasamy formula performed significantly better than all five existing methods based on the paired Wilcoxon test with Bonferroni correction (p-value < 0.05). Conclusions: Nallasamy formula outperformed the five methods studied (including Barrett Universal II) on overall MAE and MedAE, percentage of eyes within 0.5 D of prediction, as well as MAE in short, medium, and long axial length eyes.

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

Cited by 11 publications

References 24 publications

Machine learning adaptation of intraocular lens power calculation for a patient group

Machine learning adaptation of intraocular lens power calculation for a patient group

Prediction of Subjective Refraction From Anterior Corneal Surface, Eye Lengths, and Age Using Machine Learning Algorithms

Evaluation of the Nallasamy Formula: A Stacking Ensemble Machine Learning Method for Refraction Prediction in Cataract Surgery

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