Mongkol Tadarati
Overview
Explore the profile of Mongkol Tadarati including associated specialties, affiliations and a list of published articles.
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Articles
15
Citations
248
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0
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Recent Articles
1.
Kumar G, Velu S, Pardhan S, Sapkota R, Ruamviboonsuk P, Tadarati M, et al.
PLoS One
. 2023 Aug;
18(8):e0289618.
PMID: 37535658
Objectives: Diabetic retinopathy (DR) can cause significant visual impairment which can be largely avoided by early detection through proper screening and treatment. People with DR face a number of challenges...
2.
Kaothanthong N, Limwattanayingyong J, Silpa-Archa S, Tadarati M, Amphornphruet A, Singhanetr P, et al.
Diagnostics (Basel)
. 2023 Jan;
13(2).
PMID: 36672999
We compared the performance of deep learning (DL) in the classification of optical coherence tomography (OCT) images of macular diseases between automated classification alone and in combination with automated segmentation....
3.
Silpa-Archa S, Limwattanayingyong J, Tadarati M, Amphornphruet A, Ruamviboonsuk P
Indian J Ophthalmol
. 2021 Oct;
69(11):2959-2967.
PMID: 34708730
The focus of capacity building for screening and treatment of diabetic retinopathy (DR) is on health professionals who are nonophthalmologists. Both physicians and nonphysicians are recruited for screening DR. Although...
4.
Varadarajan A, Bavishi P, Ruamviboonsuk P, Chotcomwongse P, Venugopalan S, Narayanaswamy A, et al.
Nat Commun
. 2020 Jan;
11(1):130.
PMID: 31913272
Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in...
5.
Ruamviboonsuk P, Krause J, Chotcomwongse P, Sayres R, Raman R, Widner K, et al.
NPJ Digit Med
. 2019 Jul;
2:68.
PMID: 31341955
[This corrects the article DOI: 10.1038/s41746-019-0099-8.].
6.
Raumviboonsuk P, Krause J, Chotcomwongse P, Sayres R, Raman R, Widner K, et al.
NPJ Digit Med
. 2019 Jul;
2:25.
PMID: 31304372
Deep learning algorithms have been used to detect diabetic retinopathy (DR) with specialist-level accuracy. This study aims to validate one such algorithm on a large-scale clinical population, and compare the...
7.
Linz M, Bressler N, Chaikitmongkol V, Sivaprasad S, Patikulsila D, Choovuthayakorn J, et al.
JAMA Ophthalmol
. 2018 May;
136(6):658-664.
PMID: 29800983
Importance: While symptomatic vitreomacular interface abnormalities (VIAs) are common, assessment of vision preference values and treatment preferences of these may guide treatment recommendations by physicians and influence third-party payers. Objective:...
8.
Genome-wide association study of neovascular age-related macular degeneration in the Thai population
Ruamviboonsuk P, Tadarati M, Singhanetr P, Wattanapokayakit S, Kunhapan P, Wanitchanon T, et al.
J Hum Genet
. 2017 Jul;
62(11):957-962.
PMID: 28703135
We performed a genome-wide association study on 377 cases of neovascular age-related macular degeneration (AMD) and 1074 controls to determine the association of previously reported genetic variants associated with neovascular...
9.
Han I, Tadarati M, Pacheco K, Scott A
Am J Ophthalmol
. 2017 Feb;
177:90-99.
PMID: 28212878
Purpose: To evaluate macular vascular flow abnormalities identified by optical coherence tomography angiography (OCT-A) in patients with various sickle cell genotypes. Design: Prospective, observational case series. Methods: This is a...
10.
Chaikitmongkol V, Tadarati M, Bressler N
Curr Opin Ophthalmol
. 2016 Feb;
27(3):217-23.
PMID: 26866953
Purpose Of Review: Given the increasing prevalence of geographic atrophy from age-related macular degeneration as the number of individuals over 85 increases throughout the world, as well as the recent...