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Improving clinical refractive results of cataract surgery by machine learning
M. Sramka, M. Slovak, J. Tuckova, P. Stodulka,
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články
NLK
Directory of Open Access Journals
od 2013
Free Medical Journals
od 2013
PubMed Central
od 2013
Europe PubMed Central
od 2013
ProQuest Central
od 2013-02-12
Open Access Digital Library
od 2013-01-01
Open Access Digital Library
od 2013-01-01
ROAD: Directory of Open Access Scholarly Resources
od 2013
PubMed
31304064
DOI
10.7717/peerj.7202
Knihovny.cz E-zdroje
- Publikační typ
- časopisecké články MeSH
Aim: To evaluate the potential of the Support Vector Machine Regression model (SVM-RM) and Multilayer Neural Network Ensemble model (MLNN-EM) to improve the intraocular lens (IOL) power calculation for clinical workflow. Background: Current IOL power calculation methods are limited in their accuracy with the possibility of decreased accuracy especially in eyes with an unusual ocular dimension. In case of an improperly calculated power of the IOL in cataract or refractive lens replacement surgery there is a risk of re-operation or further refractive correction. This may create potential complications and discomfort for the patient. Methods: A dataset containing information about 2,194 eyes was obtained using data mining process from the Electronic Health Record (EHR) system database of the Gemini Eye Clinic. The dataset was optimized and split into the selection set (used in the design for models and training), and the verification set (used in the evaluation). The set of mean prediction errors (PEs) and the distribution of predicted refractive errors were evaluated for both models and clinical results (CR). Results: Both models performed significantly better for the majority of the evaluated parameters compared with the CR. There was no significant difference between both evaluated models. In the ±0.50 D PE category both SVM-RM and MLNN-EM were slightly better than the Barrett Universal II formula, which is often presented as the most accurate calculation formula. Conclusion: In comparison to the current clinical method, both SVM-RM and MLNN-EM have achieved significantly better results in IOL calculations and therefore have a strong potential to improve clinical cataract refractive outcomes.
Citace poskytuje Crossref.org
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