Corneal Clarity and Visual Outcomes after Small-Incision Lenticule Extraction and Comparison to Femtosecond Laser-Assisted In Situ Keratomileusis

et al. 2020

Preprint

Background: Small-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. Methods: We 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.Results: Adaboost achieved the highest performance with an 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 pre-operative manifest refraction for all the cases of nomograms. For the sphere and cylinder nomograms, the following highly important feature was the surgeon. Conclusions: Among the diverse machine learning algorithms, Adaboost exhibited the highest performance in 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.

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Nomogram for Small-Incision Lenticule Extraction

Park

et al. 2020

Preprint

“…There has been no comprehensive report describing the AD or LT difference between LASIK and SMILE, but some differences have been reported. Lazaridis A et al 14 compared the corneal clarity and visual outcomes between FS-LASIK and SMILE, reporting that the preoperative mean spherical equivalent refraction did not show a significant difference (-4.80 ± 2.4 D and -5.51 ± 1.86 D, respectively; p = 0.136), but the mean estimated LT was significantly thicker in the SMILE group (99 ± 38 µm and 116 ± 28 µm, respectively; p = 0.017). Wang et al 15 reported postoperative differences in the degree of corneal biomechanical change between SMILE and LASIK, where the estimated LT was significantly thicker in the SMILE group when the spherical equivalent refraction was less than -6.0 D even if there was no significant difference in preoperative spherical equivalent refraction and postoperative biomechanical strength of cornea between 2 groups.…”

Section: Discussionmentioning

confidence: 99%

Comparison of anterior segment changes after femtosecond laser LASIK and SMILE using a dual rotating Scheimpflug analyzer

Mun

Yang

et al. 2019

Preprint

Purpose To compare the changes in the anterior segment after femtosecond laser in situ keratomileusis (FS-LASIK) and small incision lenticule extraction (SMILE) using a dual rotating Scheimpflug (DRS) analyzer (Galilei®; Ziemer Ophthalmology, Port, Switzerland). Setting Onnuri smile eye clinic, Seoul, South Korea Design Retrospective Method 218 eyes of 109 patients who underwent FS-LASIK or SMILE for myopic correction were retrospectively studied. Ninety-eight eyes of 49 patients who underwent FS-LASIK were compared to 120 eyes of 60 patients treated with SMILE. A DRS analyzer was used for preoperative and 6-month postoperative anterior segment analyses. Measured variables included the central corneal thickness (CCT), anterior chamber depth (ACD), anterior and posterior keratometry (K), anterior and posterior best-fit sphere radius, and maximum posterior elevation (MPE). Results The estimated lenticule thickness (LT; 87.20 ± 22.87 µm) was significantly thicker in the SMILE group than the ablation depth (69.23 ± 22.24 µm) in the FS-LASIK group. However, there was no significant difference in the CCT decrease using the DRS analyzer. The MPE was significantly increased after both procedures, and there was a statistically significant difference in the change in MPE between the two groups (-1.10 ± 2.12 µm after FS-LASIK; -0.75 ± 2.33 µm after SMILE; p = 0.035). Conclusions The preoperative LT was overestimated using SMILE. Regarding changes in the MPE and posterior K, changes in the posterior corneal surface were greater after FS-LASIK than after SMILE.

“…There has been no comprehensive report describing the AD or LT difference between LASIK and SMILE, but some differences have been reported. Lazaridis A et al 15 compared the corneal clarity and visual outcomes between FS-LASIK and SMILE, reporting that the preoperative mean spherical equivalent refraction did not show a significant difference (-4.80 ± 2.4 D and -5.51 ± 1.86 D, respectively; p = 0.136), but the mean estimated LT was significantly thicker in the SMILE group (99 ± 38 µm and 116 ± 28 µm, respectively; p = 0.017). Wang et al 16 reported postoperative differences in the degree of corneal biomechanical change between SMILE and LASIK, where the estimated LT was significantly thicker in the SMILE group when the spherical equivalent refraction was less than -6.0 D even if there was no significant difference in preoperative spherical equivalent refraction and postoperative biomechanical strength of the cornea between the two groups.…”

Section: Discussionmentioning

confidence: 99%

Comparison of anterior segment changes after femtosecond laser LASIK and SMILE using a dual rotating Scheimpflug analyzer

Mun

Yang

et al. 2019

Preprint

Background: To compare the changes in the anterior segment after femtosecond laser in situ keratomileusis (FS-LASIK) and small incision lenticule extraction (SMILE) using a dual rotating Scheimpflug (DRS) analyzer (Galilei®; Ziemer Ophthalmology, Port, Switzerland).Methods: A total of 218 eyes of 109 patients who underwent FS-LASIK or SMILE for myopic correction were retrospectively studied. Ninety-eight eyes of 49 patients who underwent FS-LASIK were compared to 120 eyes of 60 patients treated with SMILE. A DRS analyzer was used for preoperative and 6-month postoperative anterior segment analyses. Measured variables included the central corneal thickness (CCT), anterior chamber depth (ACD), anterior and posterior keratometry (K), anterior and posterior best-fit sphere radius, and maximum posterior elevation (MPE).Results: After the procedure, the amount of CCT decrease was higher in the SMILE group than in the FS-LASIK group, but it was not statistically significant. The MPE was significantly increased after both procedures (p < 0.001 and p = 0.001 in the FS-LASIK and SMILE groups, respectively), with the amount of elevation being higher after FS-LASIK than after SMILE even though it was not statistically significant. And there was a significant change in the steep and average posterior K in the FS-LASIK group (p = 0.006 and 0.001, respectively), but not in the SMILE group.Conclusions: Regarding changes in the MPE and posterior K, changes in the posterior corneal surface were greater after FS-LASIK than after SMILE.