Dataset Information

Comprehensive evaluation of total corneal refractive power by ray tracing in predicting corneal power in eyes after small incision lenticule extraction.

ABSTRACT:

Purpose

To assess the prediction accuracy of four variations of total corneal refractive power (TCRP) by the ray tracing method in determining corneal power in eyes after myopic small incision lenticule extraction (SMILE).

Methods

Forty eyes of forty patients who had undergone myopic SMILE were enrolled in this prospective study. Manifest refraction and Pentacam HR were performed preoperatively and three months or more postoperatively. Mean keratometry (Km), true net power (TNP), equivalent keratometry readings (EKR) and 4 subtypes of TCRP (pupil centered or apex centered within a ring or a zone)-TCRPpupil,ring, TCRPpupil,zone, TCRPapex,ring and TCRPapex,zone-were recorded and compared to the theoretical postoperative keratometry value using the clinical history method (CHM).

Results

The only keratometric values that showed no statistically significant differences from the CHM were 4.0 mm and 4.5 mm EKR, 6.0 mm TCRPpupil,zone and TCRPapex,zone. Pearson's correlation test revealed that 4.0 mm TCRPpupil,zone exhibited the highest correlation coefficient (r = 0.974) followed by TCRPapex,zone 4.0 mm (0.972) and EKR 4.5 mm (0.970). The 95% limits of agreement (LOA) of the 4.0 mm EKR and CHM, the 4.5 mm EKR and CHM, the 6.0 mm TCRPpupil,zone and CHM, the 6.0 mm TCRPapex,zone and CHM were (-1.27 to 1.22 D), (-1.04 to 0.98 D), (-1.39 to 1.08 D) and (-1.38 to 0.96 D), respectively, while the modified 4.0 mm TCRPpupil,zone (TCRPpuil,zone + 0.70 D) and TCRPapex,zone (TCRPapex,zone+0.70 D) yielded the narrowest 95% LOA of (-0.96 to 0.95 D) and (-0.96D, 1.05 D).

Conclusions

Total corneal refractive power using the ray tracing method could predict corrected corneal power derived from the CHM in eyes following SMILE surgery after simple modification.

SUBMITTER: Pan C

PROVIDER: S-EPMC6553727 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Comprehensive evaluation of total corneal refractive power by ray tracing in predicting corneal power in eyes after small incision lenticule extraction.

Pan Chao C Tan Weina W Hua Yanjun Y Lei Xiaohua X

PloS one 20190606 6

<h4>Purpose</h4>To assess the prediction accuracy of four variations of total corneal refractive power (TCRP) by the ray tracing method in determining corneal power in eyes after myopic small incision lenticule extraction (SMILE).<h4>Methods</h4>Forty eyes of forty patients who had undergone myopic SMILE were enrolled in this prospective study. Manifest refraction and Pentacam HR were performed preoperatively and three months or more postoperatively. Mean keratometry (Km), true net power (TNP), ...[more]

PMID: 31170272

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Project description:AimTo evaluate the accuracy of the raytracing method for the calculation of intraocular lens (IOL) power in myopic eyes after small incision extraction of the lenticule (SMILE).MethodsRetrospective study. All patients undergoing surgery for myopic SMILE between May 1, 2020, and December 31, 2020, with Scheimpflug tomography optical biometry were eligible for inclusion. Manifest refraction was performed before and 6 months after refractive surgery. One eye from each patient was included in the final analysis. A theoretical model was invited to predict the accuracy of multiple methods of lens power calculation by comparing the IOL-induced refractive error at the corneal plane (IOL-Dif) and the SMILE-induced change of spherical equivalent (SMILE-Dif) before and after SMILE surgery. The prediction error (PE) was calculated as the difference between SMILE-Dif-IOL-Dif. IOL power calculations were performed using raytracing (Olsen Raytracing, Pentacam AXL, software version 1.22r05, Wetzlar, Germany) and other formulae with historical data (Barrett True-K, Double-K SRK/T, Masket, Modified Masket) and without historical data (Barrett True-K no history, Haigis-L, Hill Potvin Shammas PM, Shammas-PL) for the same IOL power and model. In addition, subgroup analysis was performed in different anterior chamber depths, axial lengths, back-to-front corneal radius ratio, keratometry, lens thickness, and preoperative spherical equivalents.ResultsA total of 70 eyes of 70 patients were analyzed. The raytracing method had the smallest mean absolute PE (0.26 ± 0.24 D) and median absolute PE (0.16 D), and also had the largest percentage of eyes within a PE of ± 0.25 D (64.3%), ± 0.50 D (81.4%), ± 0.75 D (95.7%), and ± 1.00 D (100.0%). The raytracing method was significantly better than Double-K SRK/T, Haigis, Haigis-L, and Shammas-PL formulae in postoperative refraction prediction (all p < 0.001), but not better than the following formulae: Barrett True-K (p = 0.314), Barrett True-K no history (p = 0.163), Masket (p = 1.0), Modified Masket (p = 0.806), and Hill Potvin Shammas PM (p = 0.286). Subgroup analysis showed that refractive outcomes exhibited no statistically significant differences in the raytracing method (all p < 0.05).ConclusionRaytracing was the most accurate method in predicting target refraction and had a good consistency in calculating IOL power for myopic eyes after SMILE.

Project description:BackgroundThe design of cap thickness for small incision lenticule extraction (SMILE) plays a role in post-laser vision correction (post-LVC) corneal biomechanics. This study aimed to compare the corneal biomechanical characteristics following SMILE with different cap thicknesses of 110 μm, 120 μm, and 130 μm for myopia and myopic astigmatism correction.MethodsSeventy-five patients (146 eyes) who underwent SMILE with designed cap thickness of 110 μm, 120 μm, and 130 μm were recruited at the Eye Center of Beijing Tongren Hospital between August 2020 and November 2021. Visual acuity, refraction, and corneal biomechanical parameters were measured preoperatively, 1 week and 1, 3, 6 months postoperatively. One-way analysis of variances (ANOVA) with Bonferroni correction or Kruskal-Wallis test was performed to compare the parameters among different groups. Repeated-measures analysis of variance with Bonferroni correction or Friedman test was applied for comparing the parameters within different follow-up times.ResultsUncorrected distance visual acuity of 110-μm group was better only at 1-week and 1-month postoperatively (P = 0.012, 0.037). There were no significant differences in spherical equivalent, nor in Corvis biomechanical index-laser vision correction (CBI-LVC). All the parameters reached stability at 3-month postoperatively. Integrated radius (IR) and deformation amplitude ratio 2 mm (DA ratio 2 mm) in 120-μm and 130-μm groups were higher than 110-μm group at 1-month postoperatively (P = 0.019, 0.002). So was Ambrósio relational thickness (ARTh) at 6-month postoperatively (P = 0.011). Stiffness parameter at applanation A1 (SP-A1), stress-strain index (SSI), biomechanically corrected intraocular pressure (bIOP) and central corneal thickness (CCT) were highest in 130-μm group, followed by 120-μm group, then 110-μm group at 3-month (P<0.001, P = 0.030, P = 0.027, P = 0.008) and 6-month (P<0.001, P = 0.002, P = 0.0023, P = 0.001) postoperatively.ConclusionsThe corneal stiffness following SMILE was greatest with 130-μm cap, followed by 120-μm cap, then 110-μm cap. 130-μm cap might have advantages in terms of corneal biomechanics and retreatment option. The SMILE-designed protocol should be customized in practice.

Project description:BackgroundSmall-incision lenticule extraction (SMILE) is a surgical procedure for the refractive correction of myopia and astigmatism, which has been reported as safe and effective. However, over- and under-correction still occur after SMILE. The necessity of nomograms is emphasized to achieve optimal refractive results. Ophthalmologists diagnose nomograms by analyzing the preoperative refractive data with their individual knowledge which they accumulate over years of experience. Our aim was to predict the nomograms of sphere, cylinder, and astigmatism axis for SMILE accurately by applying machine learning algorithm.MethodsWe retrospectively analyzed the data of 3,034 eyes composed of four categorical features and 28 numerical features selected from 46 features. The multiple linear regression, decision tree, AdaBoost, XGBoost, and multi-layer perceptron were employed in developing the nomogram models for sphere, cylinder, and astigmatism axis. The scores of the root-mean-square error (RMSE) and accuracy were evaluated and compared. Subsequently, the feature importance of the best models was calculated.ResultsAdaBoost achieved the highest performance with RMSE of 0.1378, 0.1166, and 5.17 for the sphere, cylinder, and astigmatism axis, respectively. The accuracies of which error below 0.25 D for the sphere and cylinder nomograms and 25° for the astigmatism axis nomograms were 0.969, 0.976, and 0.994, respectively. The feature with the highest importance was preoperative manifest refraction for all the cases of nomograms. For the sphere and cylinder nomograms, the following highly important feature was the surgeon.ConclusionsAmong the diverse machine learning algorithms, AdaBoost exhibited the highest performance in the prediction of the sphere, cylinder, and astigmatism axis nomograms for SMILE. The study proved the feasibility of applying artificial intelligence (AI) to nomograms for SMILE. Also, it may enhance the quality of the surgical result of SMILE by providing assistance in nomograms and preventing the misdiagnosis in nomograms.

Dataset Information

Comprehensive evaluation of total corneal refractive power by ray tracing in predicting corneal power in eyes after small incision lenticule extraction.

Purpose

Methods

Results

Conclusions

Publications

Comprehensive evaluation of total corneal refractive power by ray tracing in predicting corneal power in eyes after small incision lenticule extraction.

Similar Datasets

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