IDOCS: Intelligent Distributed Ontology Consensus System—The Use of Machine Learning in Retinal Drusen Phenotyping

Predicting Implantable Collamer Lens Vault Using Machine Learning Based on Various Preoperative Biometric Factors

Purpose

To predict the vault size after Implantable Collamer Lens (ICL) V4c implantation using machine learning methods and to compare the predicted vault with the conventional manufacturer's nomogram.

Methods

This study included 707 patients (707 eyes) who underwent ICL V4c implantation at the Department of Ophthalmology, Peking Union Medical College Hospital, from September 2019 to January 2022. Random Forest Regression (RFR), XGBoost, and linear regression (LR) were used to predict the vault size 1 week after ICL V4c implantation. The mean absolute error (MAE), median absolute error (MedAE), root mean square error (RMSE), symmetric mean absolute percentage error (SMAPE), and Bland-Altman plot were utilized to compare the prediction performance of these machine learning methods.

Results

The dataset was divided into a training set of 180 patients (180 eyes) and a test set of 527 patients (527 eyes). XGBoost had the lowest prediction error, with mean MAE, RMSE, and SMAPE values of 121.70 µm, 148.87 µm, and 19.13%, respectively. The Bland‒Altman plots of RFR and XGBoost showed better prediction consistency than LR. However, XGBoost showed narrower 95% limits of agreement (LoA) than RFR, ranging from -307.12 to 256.59 µm.

Conclusions

XGBoost demonstrated better predictive performance than RFR and LR, as it had the lowest prediction error and the narrowest 95% LoA. Machine learning may be applicable for vault prediction, and it might be helpful for reducing the complications and the secondary surgery rate.

Translational Relevance

Using the proposed machine learning model, surgeons can consider the postoperative vault to reduce the surgical complications.

Applying Machine Learning Techniques in Nomogram Prediction and Analysis for SMILE Treatment

PURPOSE

To analyze the outcome of machine learning technique for prediction of small incision lenticule extraction (SMILE) nomogram.

DESIGN

Prospective, comparative clinical study.

METHODS

A comparative study was conducted on the outcomes of SMILE surgery between surgeon group (nomogram set by surgeon) and machine learning group (nomogram predicted by machine learning model). The machine learning model was trained by 865 ideal cases (spherical equivalent [SE] within ±0.5 diopter [D] 3 months postoperatively) from an experienced surgeon. The visual outcomes of both groups were compared for safety, efficacy, predictability, and SE correction.

RESULTS

There was no statistically significant difference between the baseline data in both groups. The efficacy index in the machine learning group (1.48 ± 1.08) was significantly higher than in the surgeon group (1.3 ± 0.27) (t = -2.17, P < .05). Eighty-three percent of eyes in the surgeon group and 93% of eyes in the machine learning group were within ±0.50 D, while 98% of eyes in the surgeon group and 96% of eyes in the machine learning group were within ±1.00 D. The error of SE correction was -0.09 ± 0.024 and -0.23 ± 0.021 for machine learning and surgeon groups, respectively.

CONCLUSIONS

The machine learning technique performed as well as surgeon in safety, but significantly better than surgeon in efficacy. As for predictability, the machine learning technique was comparable to surgeon, although less predictable for high myopia and astigmatism.