ArticleZeitschrift fur medizinische Physik2024
Preconditioning of clinical data for intraocular lens formula constant optimisation using Random Forest Quantile Regression Trees.
Article in Zeitschrift fur medizinische Physik, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Impact of the corneal epithelium on the corneal power using 3D raytracing with OCT data.Zeitschrift fur medizinische Physik · 2026Article
- Deciphering the best shape aspheric intraocular lens - A raytracing based optimisation study.Zeitschrift fur medizinische Physik · 2026Article
- Short-term forecasting of solar irradiance using decision tree-based models and non-parametric quantile regression.PloS one · 2024Article
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5 authors.
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Abstract
purposeTo implement a fully data driven strategy for identifying outliers in clinical datasets used for formula constant optimisation, in order to achieve proper formula predicted refraction after cataract surgery, and to assess the capabilities of this outlier detection method.
methods2 clinical datasets (DS1/DS2: N = 888/403) of eyes treated with a monofocal aspherical intraocular lens (Hoya XY1/Johnson&Johnson Vision Z9003) containing preoperative biometric data, power of the lens implant and postoperative spherical equivalent (SEQ) were transferred to us for formula constant optimisation. Original datasets were used to generate baseline formula constants. A random forest quantile regression algorithm was set up using bootstrap resampling with replacement. Quantile regression trees were grown and the 25% and 75% quantile, and the interquartile range were extracted from SEQ and formula predicted refraction REF for the SRKT, Haigis and Castrop formulae. Fences were defined from the quantiles and data points outside the fences were marked and removed as outliers before recalculating the formula constants.
resultsN
conclusionWe were able to prove that with random forest quantile regression trees a fully data driven outlier identification strategy acting in the response space is achievable. In a real life scenario this strategy has to be complemented by an outlier identification method acting in the parameter space for a proper qualification of datasets prior to formula constant optimisation.
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