Considerations on the Castrop formula for calculation of intraocular lens power

Autor: Achiron, Asaf, Langenbucher, Achim, Szentmáry, Nóra, Cayless, Alan, Weisensee, Johannes, Fabian, Ekkehard, Wendelstein, Jascha, Hoffmann, Peter
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Optics and Photonics
Visual Acuity
Intraocular Lens Implantation
Standard deviation
law.invention
Nonlinear programming
Cornea
Root mean square
0302 clinical medicine
law
Medicine and Health Sciences
Postoperative Period
Limit (mathematics)
Mathematics
Lenses
Intraocular
Eye Lens
Multidisciplinary
Vision Tests
Ophthalmic Procedures
Optical Lenses
Lens (optics)
Data point
Optical Equipment
Biometrics
Physical Sciences
Medicine
Engineering and Technology
Anatomy
Algorithms
Research Article
Optimization
Biometry
Science
Ocular Anatomy
Equipment
Geometry
Surgical and Invasive Medical Procedures
Research and Analysis Methods
Refraction
Ocular
Cataract
03 medical and health sciences
Ocular System
Computational Techniques
Humans
Applied mathematics
Phacoemulsification
Biology and Life Sciences
Radii
030221 ophthalmology & optometry
Eyes
Constant (mathematics)
Head
030217 neurology & neurosurgery
Test data
Zdroj: PLoS ONE
PLoS ONE, Vol 16, Iss 6, p e0252102 (2021)
ISSN: 1932-6203
Popis: Background To explain the concept of the Castrop lens power calculation formula and show the application and results from a large dataset compared to classical formulae. Methods The Castrop vergence formula is based on a pseudophakic model eye with 4 refractive surfaces. This was compared against the SRKT, Hoffer-Q, Holladay1, simplified Haigis with 1 optimized constant and Haigis formula with 3 optimized constants. A large dataset of preoperative biometric values, lens power data and postoperative refraction data was split into training and test sets. The training data were used for formula constant optimization, and the test data for cross-validation. Constant optimization was performed for all formulae using nonlinear optimization, minimising root mean squared prediction error. Results The constants for all formulae were derived with the Levenberg-Marquardt algorithm. Applying these constants to the test data, the Castrop formula showed a slightly better performance compared to the classical formulae in terms of prediction error and absolute prediction error. Using the Castrop formula, the standard deviation of the prediction error was lowest at 0.45 dpt, and 95% of all eyes in the test data were within the limit of 0.9 dpt of prediction error. Conclusion The calculation concept of the Castrop formula and one potential option for optimization of the 3 Castrop formula constants (C, H, and R) are presented. In a large dataset of 1452 data points the performance of the Castrop formula was slightly superior to the respective results of the classical formulae such as SRKT, Hoffer-Q, Holladay1 or Haigis.
Databáze: OpenAIRE
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