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Machine learning predicting myopic regression after corneal refractive surgery using preoperative data and fundus photography

  • Refractive Surgery
  • Published:
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Abstract

Purpose

Myopic regression after surgery is the most common long-term complication of refractive surgery, but it is difficult to identify myopic regression without long-term observation. This study aimed to develop machine learning models to identify high-risk patients for refractive regression based on preoperative data and fundus photography.

Methods

This retrospective study assigned subjects to the training (n = 1606 eyes) and validation (n = 403 eyes) datasets with chronological data splitting. Machine learning models with ResNet50 (for image analysis) and XGBoost (for integration of all variables and fundus photography) were developed based on subjects who underwent corneal refractive surgery. The primary outcome was the predictive performance for the presence of myopic regression at 4 years of follow-up examination postoperatively.

Results

By integrating all factors and fundus photography, the final combined machine learning model showed good performance to predict myopic regression of more than 0.5 D (area under the receiver operating characteristic curve [ROC-AUC], 0.753; 95% confidence interval [CI], 0.710–0.793). The performance of the final model was better than the single ResNet50 model only using fundus photography (ROC-AUC, 0.673; 95% CI, 0.627–0.716). The top-five most important input features were fundus photography, preoperative anterior chamber depth, planned ablation thickness, age, and preoperative central corneal thickness.

Conclusion

Our machine learning algorithm provides an efficient strategy to identify high-risk patients with myopic regression without additional labor, cost, and time. Surgeons might benefit from preoperative risk assessment of myopic regression, patient counseling before surgery, and surgical option decisions.

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Acknowledgements

Bo Young Lee, Hee Jin Kim, and Hee Su Kim played a significant role in pre-processing data.

Author information

Author notes

  1. Juntae Kim and Ik Hee Ryu contributed equally to the work presented here and should, therefore, be regarded as equivalent authors.

Authors and Affiliations

  1. DATARIZE, Seoul, Korea

    Juntae Kim

  2. B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea

    Ik Hee Ryu, Jin Kuk Kim, In Sik Lee & Tae Keun Yoo

  3. VISUWORKS, Seoul, South Korea

    Ik Hee Ryu, Jin Kuk Kim & Tae Keun Yoo

  4. Department of Ophthalmology, Dankook University Hospital, Dankook University College of Medicine, Cheonan, South Korea

    Hong Kyu Kim

  5. Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea

    Eoksoo Han

  6. Department of Ophthalmology, Aerospace Medical Center, Republic of Korea Air Force, Cheongju, South Korea

    Tae Keun Yoo

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  1. Juntae Kim
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Contributions

Juntae Kim, Ik Hee Ryu, and Tae Keun Yoo had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Juntae Kim and Ik Hee Ryu contributed equally to the work presented here and should, therefore, be regarded as equivalent authors. JK, IHR, and TKY developed the algorithm. JKK, ISL, HKK, EH, and TKY consolidated data and performed data analyses. IHR and TKY drafted the manuscript. IHR and JKK conceived of and designed the study. All authors contributed to revisions and finalization of the submitted manuscript. All authors meet the following criteria: (1) substantial contributions to the conception or design of the work or the acquisition, analysis or interpretation of the data; (2) drafting the work or revising it critically for important intellectual content; (3) final approval of the completed version; and (4) accountability for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to Tae Keun Yoo.

Ethics declarations

Ethical approval

All procedures of studies involving human participants were performed in accordance with the ethical standards of the Institutional Review Board of the Korean National Institute for Bioethics Policy (KoNIBP, 2019–1685-003) and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

The Institutional Review Board waived the requirement for informed consent because the data were fully deidentified to protect patient confidentiality.

Conflict of interest

Juntae Kim is an employee of DATARIZE. Ik Hee Ryu and Jin Kuk Kim are directors of VISUWORKS, and own company stock. Ik Hee Ryu serves on the Advisory Board for Carl Zeiss Meditec AG and Avellino Lab USA/MAB for Avellino Lab Korea. Jin Kuk Kim is an executive of the Korea Intelligent Medical Industry Association (KIMIA). Tae Keun Yoo is an employee of VISUWORKS, and received a salary or stock as part of the standard compensation package. The remaining authors declare no conflicts of interest. VISUWORKS received research grants for SMILE surgery from Carl Zeiss Meditec AG. The research grants did not affect this study.

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Kim, J., Ryu, I.H., Kim, J.K. et al. Machine learning predicting myopic regression after corneal refractive surgery using preoperative data and fundus photography. Graefes Arch Clin Exp Ophthalmol 260, 3701–3710 (2022). https://doi.org/10.1007/s00417-022-05738-y

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  • DOI: https://doi.org/10.1007/s00417-022-05738-y

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