Dataset Information

The dynamic changes and influencing factors of visual symptoms after small incision lenticule extraction.

ABSTRACT:

Background

To investigate the dynamic changes and influencing factors of visual symptoms after small incision lenticule extraction (SMILE).

Methods

This was a prospective observational study. Visual symptoms including glare, haloes, starbursts, hazy vision, fluctuation, blurred vision, double vision and focusing difficulties were evaluated before and 1, 3, 6 months after SMILE using a questionnaire. Generalized linear mixed models were used to assess the effects of preoperative characteristics and objective visual quality parameters on postoperative visual symptoms.

Results

73 patients/146 eyes were enrolled. Preoperatively, the most common symptoms were glare (55% of eyes), haloes (48%), starbursts (44%) and blurred vision (37%). At 1 month postoperatively, the incidence and extent scores of glare, haloes, hazy vision and fluctuation rose significantly. At 3 months, the incidence and extent scores of glare, haloes and hazy vision restored to baseline. And at 6 months, the extent scores of fluctuation returned to baseline. Other symptoms (e.g., starbursts) did not change before and 1, 3, 6 months after SMILE. Preoperative visual symptoms were associated with postoperative symptoms, as patients with a symptom preoperatively had higher postoperative scores for that symptom. Age was related to postoperative extent of double vision (coefficient = 0.12, P = 0.046). There were no significant associations between postoperative visual symptoms and preoperative SE, scotopic pupil size, angle kappa (with intraoperative adjustment), postoperative HOAs or scattering indexes.

Conclusions

The incidence and extent scores of hazy vision, glare, haloes and fluctuation increased at the first month after SMILE, and recovered to baseline at 3 or 6 months. Preoperative visual symptoms were associated with the postoperative symptoms and should be fully considered before SMILE.

SUBMITTER: Chen H

PROVIDER: S-EPMC10197487 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Publications

The dynamic changes and influencing factors of visual symptoms after small incision lenticule extraction.

Chen Han H Mao Xiuyu X Xu Dongye D Guo Chenwen C Dai Jinhui J

BMC ophthalmology 20230519 1

<h4>Background</h4>To investigate the dynamic changes and influencing factors of visual symptoms after small incision lenticule extraction (SMILE).<h4>Methods</h4>This was a prospective observational study. Visual symptoms including glare, haloes, starbursts, hazy vision, fluctuation, blurred vision, double vision and focusing difficulties were evaluated before and 1, 3, 6 months after SMILE using a questionnaire. Generalized linear mixed models were used to assess the effects of preoperative ch ...[more]

PMID: 37208645

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Project description:IntroductionDeep learning (DL) has been widely used to estimate clinical images. The objective of this project was to create DL models to predict the early postoperative visual acuity after small-incision lenticule extraction (SMILE) surgery.MethodsWe enrolled three independent patient cohorts (a retrospective cohort and two prospective SMILE cohorts) who underwent the SMILE refractive correction procedure at two different refractive surgery centers from July to September 2022. The medical records and surgical videos were collected for further analysis. Based on the uncorrected visual acuity (UCVA) at 24 h postsurgery, the eyes were divided into two groups: those showing good recovery and those showing poor recovery. We then trained a DL model (Resnet50) to predict eyes with early postoperative visual acuity of patients in the retrospective cohort who had undergone SMILE surgery from surgical videos and subsequently validated the model's performance in the two prospective cohorts. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) was performed for interpretation of the model.ResultsAmong the 318 eyes (159 patients) enrolled in the study, 10,176 good quality femtosecond laser scanning images were obtained from the surgical videos. We observed that the developed DL model achieved a high accuracy of 96% for image prediction. The area under the curve (AUC) value of the DL model in the retrospective cohort was 0.962 and 0.998 in the training and validation datasets, respectively. The AUC values in two prospective cohorts were 0.959 and 0.936. At the video level, the trained machine learning (ML) model (XGBoost) also accurately distinguished patients with good or poor recovery. The AUC value of the ML model was 0.998 and 0.889 in the retrospective cohort (training and test datasets, respectively) and 1.000 and 0.984 in the two prospective cohorts. We also trained a DL model which can accurately distinguish suction loss (100%), black spots (85%), and opaque bubble layer (96%). The Grad-CAM heatmap indicated that our models can recognize the area of scanning and precisely identify intraoperative complications.ConclusionsOur findings suggest that artificial intelligence (DL and ML model) can accurately predict the early postoperative visual acuity and intraoperative complications after SMILE surgery just using surgical videos or images, which may display a great importance for artificial intelligence in application of refractive surgeries.

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

The dynamic changes and influencing factors of visual symptoms after small incision lenticule extraction.

Background

Methods

Results

Conclusions

Publications

The dynamic changes and influencing factors of visual symptoms after small incision lenticule extraction.

Similar Datasets

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