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.

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.

PURPOSE: To compare the postoperative endothelial cell counts of EVO-implantable collamer lenses (ICLs) with a central hole (V4c and V5) and laser vision correction surgery (laser in situ keratomileusis or photorefractive keratectomy). SETTING: B&VIIT Eye Center, Seoul, South Korea. DESIGN: Retrospective observational and paired contralateral study. METHODS: 62 eyes of 31 patients who underwent EVO-ICLs with a central hole implantation in one eye (phakic intraocular lens [pIOL] group) and laser vision correction in the contralateral eye (LVC group) to correct refractive errors were retrospectively reviewed. Central endothelial cell density (ECD), percentage of hexagonal cells (HEX), coefficient of variation (CoV) in cell size, and adverse events were evaluated for at least 3 years. The endothelial cells were observed using a noncontact specular microscope. RESULTS: All surgeries were performed, without complications during the follow-up period. The mean ECD loss values compared with the preoperative measurements were 6.65% and 4.95% during the 3 years after pIOL and LVC, respectively. There was no significant difference in ECD loss compared with the preoperative values (paired t test, P = .188) between the 2 groups. No significant loss in ECD was observed at any timepoint. The pIOL group showed higher HEX ( P = .018) and lower CoV ( P = .006) values than the LVC group at the last visit. CONCLUSIONS: According to the authors' experience, the EVO-ICL with a central hole implantation was a safe and stable vision correction method. Moreover, it did not induce statistically significant changes in ECD at 3 years postoperatively compared with LVC. However, further long-term follow-up studies are required to confirm these results.

Predicting Postoperative Anterior Chamber Angle for Phakic Intraocular Lens Implantation Using Preoperative Anterior Segment Metrics.

Purpose: The anterior chamber angle (ACA) is a critical factor in posterior chamber phakic intraocular lens (EVO Implantable Collamer Lens [ICL]) implantation. Herein, we predicted postoperative ACAs to select the optimal ICL size to reduce narrow ACA-related complications. Methods: Regression models were constructed using pre-operative anterior segment optical coherence tomography metrics to predict postoperative ACAs, including trabecular-iris angles (TIAs) and scleral-spur angles (SSAs) at 500 µm and 750 µm from the scleral spur (TIA500, TIA750, SSA500, and SSA750). Data from three expert surgeons were assigned to the development (N = 430 eyes) and internal validation (N = 108 eyes) datasets. Additionally, data from a novice surgeon (N = 42 eyes) were used for external validation. Results: Postoperative ACAs were highly predictable using the machine-learning (ML) technique (extreme gradient boosting regression [XGBoost]), with mean absolute errors (MAEs) of 4.42 degrees, 3.77 degrees, 5.25 degrees, and 4.30 degrees for TIA500, TIA750, SSA500, and SSA750, respectively, in internal validation. External validation also showed MAEs of 3.93 degrees, 3.86 degrees, 5.02 degrees, and 4.74 degrees for TIA500, TIA750, SSA500, and SSA750, respectively. Linear regression using the pre-operative anterior chamber depth, anterior chamber width, crystalline lens rise, TIA, and ICL size also exhibited good performance, with no significant difference compared with XGBoost in the validation sets. Conclusions: We developed linear regression and ML models to predict postoperative ACAs for ICL surgery anterior segment metrics. These will prevent surgeons from overlooking the risks associated with the narrowing of the ACA. Translational Relevance: Using the proposed algorithms, surgeons can consider the postoperative ACAs to increase surgical accuracy and safety.

Explainable Machine Learning Approach as a Tool to Understand Factors Used to Select the Refractive Surgery Technique on the Expert Level.

Purpose: Recently, laser refractive surgery options, including laser epithelial keratomileusis, laser in situ keratomileusis, and small incision lenticule extraction, successfully improved patients' quality of life. Evidence-based recommendation for an optimal surgery technique is valuable in increasing patient satisfaction. We developed an interpretable multiclass machine learning model that selects the laser surgery option on the expert level. Methods: A multiclass XGBoost model was constructed to classify patients into four categories including laser epithelial keratomileusis, laser in situ keratomileusis, small incision lenticule extraction, and contraindication groups. The analysis included 18,480 subjects who intended to undergo refractive surgery at the B&VIIT Eye center. Training (n = 10,561) and internal validation (n = 2640) were performed using subjects who visited between 2016 and 2017. The model was trained based on clinical decisions of highly experienced experts and ophthalmic measurements. External validation (n = 5279) was conducted using subjects who visited in 2018. The SHapley Additive ex-Planations technique was adopted to explain the output of the XGBoost model. Results: The multiclass XGBoost model exhibited an accuracy of 81.0% and 78.9% when tested on the internal and external validation datasets, respectively. The SHapley Additive ex-Planations explanations for the results were consistent with prior knowledge from ophthalmologists. The explanation from one-versus-one and one-versus-rest XGBoost classifiers was effective for easily understanding users in the multicategorical classification problem. Conclusions: This study suggests an expert-level multiclass machine learning model for selecting the refractive surgery for patients. It also provided a clinical understanding in a multiclass problem based on an explainable artificial intelligence technique. Translational Relevance: Explainable machine learning exhibits a promising future for increasing the practical use of artificial intelligence in ophthalmic clinics.