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Research
Ik Hee Ryu
B&VIIT Eye Center
Corresponding Author
Youngjun Kim
Korea Institute of Science and Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7832-3200
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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 an 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 pre-operative 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 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.
Keywords
SMILE, Nomogram, Artificial-intelligence, Machine learning
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CURRENT STATUS: Published
View the published article at BioMedical Engineering OnLine.
Version 2
Posted 11 Jan, 2021
No community comments so far
Editorial decision: Accept
On 10 Mar, 2021
Review #2 received
Received 09 Mar, 2021
Reviewer #2 agreed
On 04 Mar, 2021
Review #1 received
Received 02 Feb, 2021
Reviewers invited
Invitations sent on 31 Jan, 2021
Reviewer #1 agreed
On 31 Jan, 2021
Editor assigned
On 26 Dec, 2020
Submission checks complete
On 26 Dec, 2020
Editor invited
On 26 Dec, 2020
No comments provided
Editorial decision: Major revision
On 06 Dec, 2020
Review #3 received
Received 04 Dec, 2020
Reviewer #3 agreed
On 21 Nov, 2020
Review #1 received
Received 20 Nov, 2020
Reviewer #2 agreed
On 11 Nov, 2020
Review #2 received
Received 11 Nov, 2020
Reviewers invited
Invitations sent on 08 Nov, 2020
Reviewer #1 agreed
On 08 Nov, 2020
Submission checks complete
On 05 Aug, 2020
Editor assigned
On 05 Aug, 2020
First submitted
On 04 Aug, 2020
Editor invited
On 04 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biomedical Engineering
Learn more about our company.
See the published version of this preprint at BioMedical Engineering OnLine.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Ik Hee Ryu
B&VIIT Eye Center
Corresponding Author
Youngjun Kim
Korea Institute of Science and Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7832-3200
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BioMedical Engineering OnLine.
Version 2
Posted 11 Jan, 2021
No community comments so far
Editorial decision: Accept
On 10 Mar, 2021
Review #2 received
Received 09 Mar, 2021
Reviewer #2 agreed
On 04 Mar, 2021
Review #1 received
Received 02 Feb, 2021
Reviewers invited
Invitations sent on 31 Jan, 2021
Reviewer #1 agreed
On 31 Jan, 2021
Editor assigned
On 26 Dec, 2020
Submission checks complete
On 26 Dec, 2020
Editor invited
On 26 Dec, 2020
No comments provided
Editorial decision: Major revision
On 06 Dec, 2020
Review #3 received
Received 04 Dec, 2020
Reviewer #3 agreed
On 21 Nov, 2020
Review #1 received
Received 20 Nov, 2020
Reviewer #2 agreed
On 11 Nov, 2020
Review #2 received
Received 11 Nov, 2020
Reviewers invited
Invitations sent on 08 Nov, 2020
Reviewer #1 agreed
On 08 Nov, 2020
Submission checks complete
On 05 Aug, 2020
Editor assigned
On 05 Aug, 2020
First submitted
On 04 Aug, 2020
Editor invited
On 04 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biomedical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BioMedical Engineering OnLine.
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 an 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 pre-operative 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 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.
Figure 1
Figure 2
Figure 3
This is a list of supplementary files associated with this preprint. Click to download.
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