Comparison of implantation of posterior chamber phakic IOL implantation and laser vision correction in terms of corneal endothelial cells: 3-year observational paired-eye study

Paired-eye comparison of endothelial cell density and vault height after implantable collamer lens implantation

In clinical practice, the effect of a high vault on corneal endothelial cells after implantable collamer lens (ICL) implantation remains unclear. Many clinicians theoretically assume that a high postoperative vault leads to rapid endothelial damage, but no study has yet proven this hypothesis. We conducted a paired-eye study to compare changes in corneal endothelial cell density (ECD) between high and low postoperative vault groups. This retrospective study included 150 eyes of 75 patients with bilateral postoperative vault levels differing by more than 200 μm after ICL implantation. Patients were followed up for 7 years with ECD measurements, and changes in ECD were assessed between 6 months and 7 years post-surgery. Over the 7-year follow-up period, the percentage of ECD loss was 15.04% and 14.45% in the high- and low-vault groups, respectively. The bilateral paired-eye comparison revealed a significant reduction in ECD in the high-vault group at 3, 5, and 7 years postoperatively (P-value < 0.001). In this paired-eye comparison of long-term observations, a higher vault was associated with greater ECD loss. Our study confirms that a high vault level may be an important risk factor for ECD loss following ICL implantation.

Comparison of automated corneal endothelial cell analysis in healthy and postoperative eyes with phakic intraocular lens: a cross-sectional study and literature review

PURPOSE

Measurement of corneal endothelial cells is critical for postoperative evaluation of phakic intraocular lens (pIOL) surgery. However, inter-instrument differences in corneal endothelial cell density (ECD) after pIOL implantation have not yet been reported. This study aimed to compare automated corneal endothelial cell analysis between CellChek-20 (Konan Medical, Hyogo, Japan) and EM-4000 (Tomey, Nagoya, Japan) in healthy and postoperative eyes with pIOL.

METHODS

We retrospectively analyzed 154 healthy and 236 postoperative eyes after pIOL surgery. Endothelial cell measurements were performed using CellChek-20 and EM-4000 with autofocusing and automated image analysis. ECD, percentage of hexagonal cells (HEX), coefficient of variation in cell size (CoV), and central corneal thickness (CCT) were compared between the two devices.

RESULTS

The ECDs of the two devices were highly correlated in both healthy (Spearman's correlation coefficient [r] = 0.805; p < 0.001) and postoperative (r = 0.901; p < 0.001) groups. ECD from CellChek-20 was higher than EM-4000 in both healthy (mean difference = 228.9 cells/mm2; p < 0.001) and postoperative (mean difference = 115.6 cells/mm2; p < 0.001) groups. The CCT values also showed a strong correlation in healthy eyes (r = 0.974; p < 0.001) and in postoperative eyes (r = 0.936; p < 0.001); however, significant inter-instrument differences were observed. HEX and CV showed significant differences and relatively weak correlations (r < 0.7) between the two devices in both healthy and postoperative groups.

CONCLUSION

The ECD values between the two instruments were correlated, but that of the CellChek-20 was significantly higher than that of the EM-4000 in both healthy and postoperative eyes after pIOL surgery. Most previous studies have also shown that the Konan software overestimated the ECD compared to other products in automatic measurement mode. The possibility of measurement bias should be considered when replacing equipment used for corneal endothelial cell measurements.

A comparative study of two phakic posterior chamber implantable lenses

This study aimed to compare the postimplantation clinical outcomes of 2 types of posterior chamber phakic intraocular lenses (IOLs): Visian™ implantable collamer lenses (ICL; EVO+ V5; Staar Surgical, Monrovia, CA) and an implantable phakic contact lens (IPCL) (IPCL V2.0, Care Group Sight Solution, India) to correct high myopia and myopic astigmatism. This retrospective study included patients who had undergone phakic IOL implantation performed by a single surgeon between March 2021 and March 2022. Preoperative assessments included slit-lamp examination, fundus examination, spherical equivalent, uncorrected distant visual acuity (UDVA), corrected distant visual acuity (CDVA), specular microscope parameters, and optical quality analysis system parameters. Postoperative assessments after 1 year included slit-lamp examination to detect adverse effects and spherical equivalent, UDVA, CDVA, specular microscope, optical quality analysis system, and anterior-segment optical coherence tomography. Eighty eyes from 42 patients (47 eyes from 24 patients in the ICL group and 33 eyes from 18 patients in the IPCL group) were included. No statistically significant differences were observed between the 2 groups regarding preoperative parameters. At 1-year postoperatively, the mean UDVA in the ICL and IPCL groups was 0.019 ± 0.040 logMAR and 0.019 ± 0.041 logMAR, respectively. The mean CDVA was 0.001 ± 0.008 logMAR and 0.001 ± 0.007 logMAR in the ICL and IPCL groups, respectively, showing no statistically significant differences. The postoperative parameters did not differ significantly between the 2 groups. The visual acuity and refractive results of both groups were excellent, and both groups exhibited similar efficacy and safety profiles.

Implantable Collamer Lens Procedure Planning: A Review of Global Approaches

More than 2 million implantable collamer lenses (ICLs) have been implanted worldwide. With a central port to improve aqueous flow through the ICL, the latest iteration of this phakic intraocular lens (pIOL) has been shown to have stable outcomes with very low rates of adverse events. However, correct planning and ICL size selection continue to be important to achieve an optimal vault. Shallow or excessive vaults are not complications in and of themselves but may increase the risk of complications. Historically, surgeons have relied on measurements of anterior chamber depth (ACD) and manual, caliper-measured white-to-white (WTW) distance to select the ICL size. New diagnostic and imaging technologies such as optical coherence tomography (OCT) and ultrasound biomicroscopy (UBM) provide additional opportunities for visualization and measurement of the intraocular dimensions involved in phakic intraocular lens implantation, including sulcus-to-sulcus (STS) and angle-to-angle (ATA) diameters. This paper reviews various approaches to ICL planning and sizing that have been published in the peer-reviewed literature, all of which produce acceptable results for predicting vault and size selection. Surgeons may also want to identify a methodology for patient evaluation and ICL size selection that best aligns with their personal preferences, diagnostic technology, and familiarity with analytical optimization tools.

Code-Free Machine Learning Approach for EVO-ICL Vault Prediction: A Retrospective Two-Center Study

Purpose

Establishing a development environment for machine learning is difficult for medical researchers because learning to code is a major barrier. This study aimed to improve the accuracy of a postoperative vault value prediction model for implantable collamer lens (ICL) sizing using machine learning without coding experience.

Methods

We used Orange data mining, a recently developed open-source, code-free machine learning tool. This study included eye-pair data from 294 patients from the B&VIIT Eye Center and 26 patients from Kim's Eye Hospital. The model was developed using OCULUS Pentacam data from the B&VIIT Eye Center and was internally evaluated through 10-fold cross-validation. External validation was performed using data from Kim's Eye Hospital.

Results

The machine learning model was successfully trained using the data collected without coding. The random forest showed mean absolute errors of 124.8 µm and 152.4 µm for the internal 10-fold cross-validation and the external validation, respectively. For high vault prediction (>750 µm), the random forest showed areas under the curve of 0.725 and 0.760 for the internal and external validation datasets, respectively. The developed model performed better than the classic statistical regression models and the Google no-code platform.

Conclusions

Applying a no-code machine learning tool to our ICL implantation datasets showed a more accurate prediction of the postoperative vault than the classic regression and Google no-code models.

Translational Relevance

Because of significant bias in measurements and surgery between clinics, the no-code development of a customized machine learning nomogram will improve the accuracy of ICL implantation.

Choice of refractive surgery types for myopia assisted by machine learning based on doctors' surgical selection data

In recent years, corneal refractive surgery has been widely used in clinics as an effective means to restore vision and improve the quality of life. When choosing myopia-refractive surgery, it is necessary to comprehensively consider the differences in equipment and technology as well as the specificity of individual patients, which heavily depend on the experience of ophthalmologists. In our study, we took advantage of machine learning to learn about the experience of ophthalmologists in decision-making and assist them in the choice of corneal refractive surgery in a new case. Our study was based on the clinical data of 7,081 patients who underwent corneal refractive surgery between 2000 and 2017 at the Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences. Due to the long data period, there were data losses and errors in this dataset. First, we cleaned the data and deleted the samples of key data loss. Then, patients were divided into three groups according to the type of surgery, after which we used SMOTE technology to eliminate imbalance between groups. Six statistical machine learning models, including NBM, RF, AdaBoost, XGBoost, BP neural network, and DBN were selected, and a ten-fold cross-validation and grid search were used to determine the optimal hyperparameters for better performance. When tested on the dataset, the multi-class RF model showed the best performance, with agreement with ophthalmologist decisions as high as 0.8775 and Macro F1 as high as 0.8019. Furthermore, the results of the feature importance analysis based on the SHAP technique were consistent with an ophthalmologist's practical experience. Our research will assist ophthalmologists in choosing appropriate types of refractive surgery and will have beneficial clinical effects.

Comparison of the Accuracy of Seven Vault Prediction Formulae for Implantable Collamer Lens Implantation

INTRODUCTION

This study aimed to compare the accuracy of seven implantable collamer lens (ICL) implantation vault prediction formulae.

METHODS

We retrospectively analyzed 328 patients (328 eyes) who underwent ICL implantation and the prediction accuracy of seven formulae: NK, KS, WH, Luo, Zhu, Hun, and ZZ were compared. Moreover, the accuracy of the seven formulae for different ICL sizes was compared. The formulae were tested using mean absolute prediction error (MAE), median absolute prediction error (MedAE), prediction error (PE) percentages at ± 50 µm, ± 100 µm, ± 200 µm, and ± 300 µm, and Bland-Altman analysis.

RESULTS

The PE of the seven formulae were statistically significant (P < 0.001). The KS (101.00 µm) and WH formulae (116.65 µm) had the smallest MedAE, followed by the Luo (123.62 µm), NK (141.50 µm), Hun (152.68 µm), ZZ (196.00 µm) and Zhu formula (225.98 µm). The highest percentage of PE in the range of ± 300µm was 94.3% and 93% for the KS and WH formulae, respectively. Among the different ICL size groupings, the KS formula predicted the smallest MedAE for 12.1 mm and 12.6 mm, whereas the Luo and WH formulae predicted the smallest MedAE for 13.2 mm and 13.7 mm, respectively.

CONCLUSIONS

The KS and WH formulae provided better outcomes by predicting the vault with higher accuracy than of the NK, Hun, Luo, ZZ, and Zhu formulae.

TRIAL REGISTRATION

ChiCTR2200065501.