Scheetz J - Search Results

1

Real-world assessment of topical glaucoma medication persistence rates based on national pharmaceutical claim data in a defined population.

Zhu Z, Jiang Y, Wang W, Scheetz J, Shang X, Zhang L, He M. Zhu Z, et al. Among authors: scheetz j. Clin Exp Ophthalmol. 2019 Sep;47(7):881-891. doi: 10.1111/ceo.13524. Epub 2019 May 16. Clin Exp Ophthalmol. 2019. PMID: 31034700

2

Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study.

Keel S, Lee PY, Scheetz J, Li Z, Kotowicz MA, MacIsaac RJ, He M. Keel S, et al. Among authors: scheetz j. Sci Rep. 2018 Mar 12;8(1):4330. doi: 10.1038/s41598-018-22612-2. Sci Rep. 2018. PMID: 29531299 Free PMC article.

3

An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs.

Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J, Lee PY, Shaw J, Ting D, Wong TY, Taylor H, Chang R, He M. Li Z, et al. Among authors: scheetz j. Diabetes Care. 2018 Dec;41(12):2509-2516. doi: 10.2337/dc18-0147. Epub 2018 Oct 1. Diabetes Care. 2018. PMID: 30275284

4

Longitudinal refractive changes in adults: evidence from population-based studies.

Han X, Scheetz J, He M. Han X, et al. Among authors: scheetz j. Clin Exp Ophthalmol. 2018 Nov;46(8):849-850. doi: 10.1111/ceo.13410. Clin Exp Ophthalmol. 2018. PMID: 30426682 No abstract available.

5

Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma.

Keel S, Wu J, Lee PY, Scheetz J, He M. Keel S, et al. Among authors: scheetz j. JAMA Ophthalmol. 2019 Mar 1;137(3):288-292. doi: 10.1001/jamaophthalmol.2018.6035. JAMA Ophthalmol. 2019. PMID: 30570648 Free PMC article.

6

Disparities in the Global Burden of Age-Related Macular Degeneration: An Analysis of Trends from 1990 to 2015.

Wang D, Jiang Y, He M, Scheetz J, Wang W. Wang D, et al. Among authors: scheetz j. Curr Eye Res. 2019 Jun;44(6):657-663. doi: 10.1080/02713683.2019.1576907. Epub 2019 Feb 18. Curr Eye Res. 2019. PMID: 30703332

7

Utilisation and perceptions towards smart device visual acuity assessment in Australia: a mixed methods approach.

Keel S, Scheetz J, Holloway E, Han X, Yan W, Mueller A, He M. Keel S, et al. Among authors: scheetz j. BMJ Open. 2019 Mar 23;9(3):e024266. doi: 10.1136/bmjopen-2018-024266. BMJ Open. 2019. PMID: 30904849 Free PMC article.

8

Artificial Intelligence in Ophthalmology: Accuracy, Challenges, and Clinical Application.

Tan Z, Scheetz J, He M. Tan Z, et al. Among authors: scheetz j. Asia Pac J Ophthalmol (Phila). 2019 May-Jun;8(3):197-199. doi: 10.22608/APO.2019122. Asia Pac J Ophthalmol (Phila). 2019. PMID: 31179666 Free article. No abstract available.

9

Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs.

Keel S, Li Z, Scheetz J, Robman L, Phung J, Makeyeva G, Aung K, Liu C, Yan X, Meng W, Guymer R, Chang R, He M. Keel S, et al. Among authors: scheetz j. Clin Exp Ophthalmol. 2019 Nov;47(8):1009-1018. doi: 10.1111/ceo.13575. Epub 2019 Jul 25. Clin Exp Ophthalmol. 2019. PMID: 31215760

10

Prevalence and risk profile of retinopathy in non-diabetic subjects: National Health and Nutrition Examination Survey 2005 to 2008.

Zhu Z, Wang W, Scheetz J, Zhang J, He M. Zhu Z, et al. Among authors: scheetz j. Clin Exp Ophthalmol. 2019 Dec;47(9):1173-1181. doi: 10.1111/ceo.13595. Epub 2019 Aug 2. Clin Exp Ophthalmol. 2019. PMID: 31317610

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