Literature DB >> 25375842

The effect of testing distance on intraocular lens power calculation.

Michael J Simpson, W Neil Charman.   

Abstract

Mesh：

Year:  2014        PMID： 25375842     DOI： 10.3928/1081597X-20141021-01

Source DB:  PubMed          Journal:  J Refract Surg        ISSN： 1081-597X            Impact factor:   3.573


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  6 in total

1.  Comparison of the Barrett Universal II, Kane and VRF-G formulas with existing intraocular lens calculation formulas in eyes with short axial lengths.

Authors:  Oleksiy V Voytsekhivskyy; Larysa Tutchenko; Diogo Hipólito-Fernandes
Journal:  Eye (Lond)       Date:  2022-01-15       Impact factor: 3.775

2.  Evaluation of the Nallasamy formula: a stacking ensemble machine learning method for refraction prediction in cataract surgery.

Authors:  Tingyang Li; Joshua Stein; Nambi Nallasamy
Journal:  Br J Ophthalmol       Date:  2022-04-04       Impact factor: 5.908

3.  Accuracy of Intraocular Lens Power Calculation Formulas in Myopic Eyes with Target Refractions of Emmetropia and Intentional Myopia.

Authors:  Daiki Sakai; Yasuhiko Hirami; Makoto Nakamura; Yasuo Kurimoto
Journal:  Clin Ophthalmol       Date:  2021-11-27

4.  Intraocular lens power calculations in eyes with pseudoexfoliation syndrome.

Authors:  Aleksandra Wlaź; Agnieszka Kustra; Agnieszka Rozegnał-Madej; Tomasz Żarnowski
Journal:  Sci Rep       Date:  2021-09-24       Impact factor: 4.379

5.  AI-powered effective lens position prediction improves the accuracy of existing lens formulas.

Authors:  Tingyang Li; Joshua Stein; Nambi Nallasamy
Journal:  Br J Ophthalmol       Date:  2021-04-09       Impact factor: 5.908

6.  AI-Powered Effective Lens Position Prediction Improves the Accuracy of Existing Lens Formulas.

Authors:  Tingyang Li; Joshua D Stein; Nambi Nallasamy
Journal:  medRxiv       Date:  2020-11-03
  6 in total

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