Improving clinical refractive results of cataract surgery by machine learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11120%2F19%3A43918447" target="_blank" >RIV/00216208:11120/19:43918447 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/19:00335482
Result on the web
<a href="https://doi.org/10.7717/peerj.7202" target="_blank" >https://doi.org/10.7717/peerj.7202</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.7717/peerj.7202" target="_blank" >10.7717/peerj.7202</a>
Alternative languages
Result language
angličtina
Original language name
Improving clinical refractive results of cataract surgery by machine learning
Original language description
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 an d MLNN-EM have achieved significantly better results in IOL calculations and therefore have a strong potential to improve clinical cataract refractive outcomes.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30207 - Ophthalmology
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PeerJ
ISSN
2167-8359
e-ISSN
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Volume of the periodical
7
Issue of the periodical within the volume
July
Country of publishing house
US - UNITED STATES
Number of pages
23
Pages from-to
"Article e7202"
UT code for WoS article
000473407300004
EID of the result in the Scopus database
2-s2.0-85072020562