Adopting machine learning to automatically identify candidate patients for corneal refractive surgery

Author:

Yoo Tae KeunORCID,Ryu Ik Hee,Lee Geunyoung,Kim Youngnam,Kim Jin Kuk,Lee In Sik,Kim Jung Sub,Rim Tyler Hyungtaek

Publisher

Springer Science and Business Media LLC

Subject

Health Information Management,Health Informatics,Computer Science Applications,Medicine (miscellaneous)

Reference45 articles.

1. Price, M. O. et al. Three-year longitudinal survey comparing visual satisfaction with LASIK and contact lenses. Ophthalmology 123, 1659–1666 (2016).

2. Yuen, L. H. et al. A 10-year prospective audit of LASIK outcomes for myopia in 37,932 eyes at a single institution in Asia. Ophthalmology 117, 1236–1244.e1 (2010).

3. Alarcón, A., Rubiño, M., Pééérez-Ocón, F. & Jiménez, J. R. Theoretical analysis of the effect of pupil size, initial myopic level, and optical zone on quality of vision after corneal refractive surgery. J. Refract. Surg. Thorofare NJ 1995 28, 901–906 (2012).

4. Díaz, J. A., Anera, R. G., Jiménez, J. R. & Del Barco, L. J. Optimum corneal asphericity of myopic eyes for refractive surgery. J. Mod. Opt. 50, 1903–1915 (2003).

5. Sayegh, F. N. Age and refraction in 46,000 patients as a potential predictor of refractive stability after refractive surgery. J. Refract. Surg. Thorofare NJ 1995 25, 747–751 (2009).

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