Daniel Ting

1.2k total citations
20 papers, 545 citations indexed

About

Daniel Ting is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Epidemiology. According to data from OpenAlex, Daniel Ting has authored 20 papers receiving a total of 545 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Radiology, Nuclear Medicine and Imaging, 17 papers in Ophthalmology and 4 papers in Epidemiology. Recurrent topics in Daniel Ting's work include Retinal Imaging and Analysis (16 papers), Retinal Diseases and Treatments (13 papers) and Glaucoma and retinal disorders (7 papers). Daniel Ting is often cited by papers focused on Retinal Imaging and Analysis (16 papers), Retinal Diseases and Treatments (13 papers) and Glaucoma and retinal disorders (7 papers). Daniel Ting collaborates with scholars based in Singapore, Australia and United Kingdom. Daniel Ting's co-authors include Tien Yin Wong, Gavin Siew Wei Tan, Wanfen Yip, Charumathi Sabanayagam, Yuchen Xie, Yogesan Kanagasingam, Gilbert Lim, Valentina Bellemo, Marcus Ang and Carol Y. Cheung and has published in prestigious journals such as Scientific Reports, Ophthalmology and British Journal of Ophthalmology.

In The Last Decade

Daniel Ting

17 papers receiving 538 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daniel Ting Singapore	11	408	402	51	46	28	20	545
Dana M. Blumberg United States	19	939 2.3×	746 1.9×	36 0.7×	69 1.5×	3 0.1×	46	1.1k
Bin Lv China	13	114 0.3×	196 0.5×	38 0.7×	36 0.8×	9 0.3×	43	394
Zhi Da Soh Singapore	14	356 0.9×	373 0.9×	11 0.2×	96 2.1×	14 0.5×	39	494
Ryan T. Yanagihara United States	11	387 0.9×	339 0.8×	9 0.2×	88 1.9×	25 0.9×	22	516
Sonja Karst Austria	14	310 0.8×	320 0.8×	25 0.5×	44 1.0×	19 0.7×	24	430
Srinivasan Kavitha India	13	306 0.8×	239 0.6×	14 0.3×	30 0.7×	4 0.1×	63	453
Hagar Khalid United Kingdom	12	518 1.3×	528 1.3×	6 0.1×	28 0.6×	13 0.5×	37	688
Seng‐Chee Loon Singapore	10	888 2.2×	740 1.8×	7 0.1×	186 4.0×	6 0.2×	12	995
Peng Guan Ong Singapore	15	405 1.0×	363 0.9×	17 0.3×	136 3.0×	10 0.4×	19	576

Countries citing papers authored by Daniel Ting

Since Specialization

Citations

This map shows the geographic impact of Daniel Ting's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Ting with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Ting more than expected).

Fields of papers citing papers by Daniel Ting

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Ting. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Ting. The network helps show where Daniel Ting may publish in the future.

Co-authorship network of co-authors of Daniel Ting

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Ting. A scholar is included among the top collaborators of Daniel Ting based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel Ting. Daniel Ting is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Tan, Jin, Daniel Yan Zheng Lim, Quang A. Le, et al.. (2025). ChatGPT performance in assessing musculoskeletal MRI scan appropriateness based on ACR appropriateness criteria. Scientific Reports. 15(1). 7140–7140.

Foo, Valencia Hui Xian, Yu‐Chi Liu, Hon Shing Ong, et al.. (2024). Deep learning for detection of Fuchs endothelial dystrophy from widefield specular microscopy imaging: a pilotstudy. Eye and Vision. 11(1). 11–11. 5 indexed citations

Wu, Lihteh, Steven Bloom, Paulo Stanga, et al.. (2023). RWC Update: Artificial Intelligence and Smart Eyewearables for Healthy Longevity; Choroidal Hemangioma Widefield Optical Coherence Tomography. Ophthalmic surgery, lasers & imaging retina. 54(2). 74–77. 3 indexed citations

Zheng, Feihui, Li Lian Foo, Daniel Ting, et al.. (2022). Advances in OCT Imaging in Myopia and Pathologic Myopia. Diagnostics. 12(6). 1418–1418. 21 indexed citations

Ting, Daniel, Tien Yin Wong, Ki Ho Park, et al.. (2021). Ocular Imaging Standardization for Artificial Intelligence Applications in Ophthalmology: the Joint Position Statement and Recommendations From the Asia-Pacific Academy of Ophthalmology and the Asia-Pacific Ocular Imaging Society. Asia-Pacific Journal of Ophthalmology. 10(4). 348–349. 12 indexed citations

Xiong, Jian, Fei Li, Diping Song, et al.. (2021). Multimodal Machine Learning Using Visual Fields and Peripapillary Circular OCT Scans in Detection of Glaucomatous Optic Neuropathy. Ophthalmology. 129(2). 171–180. 61 indexed citations

Lim, Gilbert, et al.. (2020). Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: a review. Eye and Vision. 7(1). 21–21. 62 indexed citations

Sim, Ralene, Bingyao Tan, Damon Wing Kee Wong, et al.. (2020). Optical Coherence Tomography Angiography in Diabetes and Diabetic Retinopathy. Journal of Clinical Medicine. 9(6). 1723–1723. 80 indexed citations

Ting, Daniel, et al.. (2020). Artificial Intelligence: A Singapore Response. Annals of the Academy of Medicine Singapore. 49(4). 256–258.

10.

Tham, Yih Chung, Yong Liu, Daniel Ting, et al.. (2019). Estimation of Haemoglobin A1c from Retinal photographs via Deep Learning.. 60(9). 1456–1456. 2 indexed citations

11.

Ting, Daniel, Ching‐Yu Cheng, Carol Y. Cheung, et al.. (2018). Classic Risk Factors for Diabetic Retinopathy: Deep Learning versus Human Graders. 59(9). 1706–1706.

12.

Cheung, Carol Y., Robyn J. Tapp, Haslina Hamzah, et al.. (2017). Availability and variability in guidelines on diabetic retinopathy screening in Asian countries. British Journal of Ophthalmology. 101(10). 1352–1360. 64 indexed citations

13.

Sabanayagam, Charumathi, Wanfen Yip, Daniel Ting, Gavin Siew Wei Tan, & Tien Yin Wong. (2016). Ten Emerging Trends in the Epidemiology of Diabetic Retinopathy. Ophthalmic Epidemiology. 23(4). 209–222. 114 indexed citations

14.

Ting, Daniel, Mei‐Ling Tay‐Kearney, Ian J. Constable, Janardhan Vignarajan, & Yogesan Kanagasingam. (2013). Retinal video recordings at different compression levels: a novel video-based imaging technology for diabetic retinopathy screening. Eye. 27(7). 848–853. 3 indexed citations

15.

Ting, Daniel, et al.. (2012). Diabetic retinopathy screening: can the viewing monitor influence the reading and grading outcomes. Eye. 26(12). 1511–1516. 1 indexed citations

16.

Ting, Darren Shu Jeng, Alex Pollock, Gordon N. Dutton, et al.. (2011). Visual Neglect Following Stroke: Current Concepts and Future Focus. Survey of Ophthalmology. 56(2). 114–134. 55 indexed citations

17.

Ting, Daniel, et al.. (2011). Retinal Video Recording. Ophthalmology. 118(8). 1588–1593. 8 indexed citations

18.

Ting, Daniel, et al.. (2011). Light and portable novel device for diabetic retinopathy screening. Clinical and Experimental Ophthalmology. 40(1). e40–6. 24 indexed citations

19.

Ting, Daniel, Jonathon Q. Ng, Nigel Morlet, et al.. (2011). Diabetic retinopathy--screening and management by Australian GPs.. PubMed. 40(4). 233–8. 14 indexed citations

20.

Ting, Daniel, Jonathon Q. Ng, Nigel Morlet, et al.. (2010). Diabetic retinopathy management by Australian optometrists. Clinical and Experimental Ophthalmology. 39(3). 230–235. 16 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact