Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Inna Bujor"'
Autor:
Chi Li, Jacqueline Chua, Florian Schwarzhans, Rahat Husain, Michaël J. A. Girard, Shivani Majithia, Yih-Chung Tham, Ching-Yu Cheng, Tin Aung, Georg Fischer, Clemens Vass, Inna Bujor, Chee Keong Kwoh, Alina Popa-Cherecheanu, Leopold Schmetterer, Damon Wong
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Abstract Studies using machine learning (ML) approaches have reported high diagnostic accuracies for glaucoma detection. However, none assessed model performance across ethnicities. The aim of the study is to externally validate ML models for glaucom
Externí odkaz:
https://doaj.org/article/99feac07d77e4a24b6cd44bf8d4ad50d
Autor:
Mihai Bostan, Jacqueline Chua, Yin Ci Sim, Bingyao Tan, Inna Bujor, Damon Wong, Gerhard Garhöfer, Cristina Tiu, Leopold Schmetterer, Alina Popa-Cherecheanu
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract Retinal imaging has been proposed as a biomarker for neurological diseases such as multiple sclerosis (MS). Recently, a technique for non-invasive assessment of the retinal microvasculature called optical coherence tomography angiography (OC
Externí odkaz:
https://doaj.org/article/c0b8fdb3ba9445c19d402c1bee22757a
Autor:
Jacqueline Chua, Mihai Bostan, Chi Li, Yin Ci Sim, Inna Bujor, Damon Wong, Bingyao Tan, Xinwen Yao, Florian Schwarzhans, Gerhard Garhöfer, Georg Fischer, Clemens Vass, Cristina Tiu, Ruxandra Pirvulescu, Alina Popa-Cherecheanu, Leopold Schmetterer
Publikováno v:
NeuroImage: Clinical, Vol 34, Iss , Pp 103010- (2022)
Background: Optical coherence tomography (OCT) is a retinal imaging system that may improve the diagnosis of multiple sclerosis (MS) persons, but the evidence is currently equivocal. To assess whether compensating the peripapillary retinal nerve fibe
Externí odkaz:
https://doaj.org/article/3865ee5e303e4b95848b11b1f6ae3641
Autor:
Damon Wong, Ashish Jith, Rachel Chong, Jonathan Crowston, Jacqueline Chua, Inna Bujor, Rahat Hussain, Eranga Vithana, Michael Girard, Daniel Ting, Cheng Ching‐Yu, Tin Aung, Alina Popa‐Cherecheanu, Leopold Schmetterer
Publikováno v:
Acta Ophthalmologica. 100
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9b8d1efa4686da4f405458ba4c6052a7
https://doi.org/10.1111/j.1755-3768.2022.0131
https://doi.org/10.1111/j.1755-3768.2022.0131
Autor:
Alina Popa‐Cherecheanu, Mihai Bostan, Jacqueline Chua, Yin Ci Sim, Bingyao Tan, Inna Bujor, Gerhard Garhofer, Leopold Schmetterer
Publikováno v:
Acta Ophthalmologica. 100
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c59f500902877094767801f316834b77
https://doi.org/10.1111/j.1755-3768.2022.0449
https://doi.org/10.1111/j.1755-3768.2022.0449
Autor:
Jacqueline Chua, Mihai Bostan, Yin Ci Sim, Inna Bujor, Leopold Schmetterer, Alina Popa‐Cherecheanu
Publikováno v:
Acta Ophthalmologica. 100
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3b8bc54290d607b39c25abad4a97aee7
https://doi.org/10.1111/j.1755-3768.2022.0120
https://doi.org/10.1111/j.1755-3768.2022.0120
Autor:
Damon Wong, Jacqueline Chua, Inna Bujor, Rachel S. Chong, Monisha E. Nongpiur, Eranga N. Vithana, Rahat Husain, Tin Aung, Alina Popa‐Cherecheanu, Leopold Schmetterer
Publikováno v:
Annals of the New York Academy of SciencesREFERENCES. 1515(1)
To evaluate machine learning (ML) approaches for structure-function modeling to estimate visual field (VF) loss in glaucoma, models from different ML approaches were trained on optical coherence tomography thickness measurements to estimate global VF
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49a32cdbe8cbca6a93f532db2ced3c31
https://pubmed.ncbi.nlm.nih.gov/35729796
https://pubmed.ncbi.nlm.nih.gov/35729796
Autor:
Ashish Jith Sreejith Kumar, Rachel S. Chong, Jonathan G. Crowston, Jacqueline Chua, Inna Bujor, Rahat Husain, Eranga N. Vithana, Michaël J. A. Girard, Daniel S. W. Ting, Ching-Yu Cheng, Tin Aung, Alina Popa-Cherecheanu, Leopold Schmetterer, Damon Wong
Importance: Deep learning (DL) networks require large data sets for training, which can be challenging to collect clinically. Generative models could be used to generate large numbers of synthetic optical coherence tomography (OCT) images to train su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::44f13a28a5887e20180a28af9b3dceff
https://hdl.handle.net/10356/165014
https://hdl.handle.net/10356/165014
