Artificial intelligence-based nomogram for small-incision lenticule extraction
Abstract 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 ach...
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doaj-0e150a2af069433884fe981ec68e28692021-04-25T11:50:14ZengBMCBioMedical Engineering OnLine1475-925X2021-04-0120111010.1186/s12938-021-00867-7Artificial intelligence-based nomogram for small-incision lenticule extractionSeungbin Park0Hannah Kim1Laehyun Kim2Jin-kuk Kim3In Sik Lee4Ik Hee Ryu5Youngjun Kim6Center for Bionics, Korea Institute of Science and TechnologyCenter for Bionics, Korea Institute of Science and TechnologyCenter for Bionics, Korea Institute of Science and TechnologyB&VIIT Eye CenterB&VIIT Eye CenterB&VIIT Eye CenterCenter for Bionics, Korea Institute of Science and TechnologyAbstract 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 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. Conclusions Among 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.https://doi.org/10.1186/s12938-021-00867-7SMILENomogramArtificial intelligenceMachine learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Seungbin Park Hannah Kim Laehyun Kim Jin-kuk Kim In Sik Lee Ik Hee Ryu Youngjun Kim |
spellingShingle |
Seungbin Park Hannah Kim Laehyun Kim Jin-kuk Kim In Sik Lee Ik Hee Ryu Youngjun Kim Artificial intelligence-based nomogram for small-incision lenticule extraction BioMedical Engineering OnLine SMILE Nomogram Artificial intelligence Machine learning |
author_facet |
Seungbin Park Hannah Kim Laehyun Kim Jin-kuk Kim In Sik Lee Ik Hee Ryu Youngjun Kim |
author_sort |
Seungbin Park |
title |
Artificial intelligence-based nomogram for small-incision lenticule extraction |
title_short |
Artificial intelligence-based nomogram for small-incision lenticule extraction |
title_full |
Artificial intelligence-based nomogram for small-incision lenticule extraction |
title_fullStr |
Artificial intelligence-based nomogram for small-incision lenticule extraction |
title_full_unstemmed |
Artificial intelligence-based nomogram for small-incision lenticule extraction |
title_sort |
artificial intelligence-based nomogram for small-incision lenticule extraction |
publisher |
BMC |
series |
BioMedical Engineering OnLine |
issn |
1475-925X |
publishDate |
2021-04-01 |
description |
Abstract 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 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. Conclusions Among 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. |
topic |
SMILE Nomogram Artificial intelligence Machine learning |
url |
https://doi.org/10.1186/s12938-021-00867-7 |
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