Application of Artificial Intelligence in Ophthalmology
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8441-1_20
Reference35 articles.
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2. Ambrósio R Jr, Machado AP, Leão E, et al. Optimized artificial intelligence for enhanced ectasia detection using Scheimpflug-based corneal tomography and biomechanical data. Am J Ophthalmol. 2023;251:126–42. https://doi.org/10.1016/j.ajo.2022.12.016.
3. Saad A, Gatinel D. Combining placido and corneal wavefront data for the detection of Forme Fruste keratoconus. J Refract Surg. 2016;32(8):510–6. https://doi.org/10.3928/1081597X-20160523-01.
4. Yoo TK, Ryu IH, Lee G, et al. Adopting machine learning to automatically identify candidate patients for corneal refractive surgery. NPJ Digit Med. 2019;2:59. Published 2019 Jun 20. https://doi.org/10.1038/s41746-019-0135-8.
5. Achiron A, Gur Z, Aviv U, et al. Predicting refractive surgery outcome: machine learning approach with big data. J Refract Surg. 2017;33(9):592–7. https://doi.org/10.3928/1081597X-20170616-03.
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