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Your search keyword '"Ting, Daniel S.W."' showing total 32 results
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32 results on '"Ting, Daniel S.W."'

8. Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision Academy retinal expert consensus

Author

Chou, Yu-Bai, Kale, Aditya U., Lanzetta, Paolo, Aslam, Tariq, Barratt, Jane, Danese, Carla, Eldem, Bora, Eter, Nicole, Gale, Richard, Korobelnik, Jean-François, Kozak, Igor, Li, Xiaorong, Li, Xiaoxin, Loewenstein, Anat, Ruamviboonsuk, Paisan, Sakamoto, Taiji, Ting, Daniel S.W., van Wijngaarden, Peter, Waldstein, Sebastian M., Wong, David, Wu, Lihteh, Zapata, Miguel A., and Zarranz-Ventura, Javier

Published
2023
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9. The impact of artificial intelligence on retinal disease management: Vision Academy retinal expert consensus

Author

Danese, Carla, Kale, Aditya U., Aslam, Tariq, Lanzetta, Paolo, Barratt, Jane, Chou, Yu-Bai, Eldem, Bora, Eter, Nicole, Gale, Richard, Korobelnik, Jean-François, Kozak, Igor, Li, Xiaorong, Li, Xiaoxin, Loewenstein, Anat, Ruamviboonsuk, Paisan, Sakamoto, Taiji, Ting, Daniel S.W., van Wijngaarden, Peter, Waldstein, Sebastian M., Wong, David, Wu, Lihteh, Zapata, Miguel A., and Zarranz-Ventura, Javier

Published
2023
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12. Digital Gonioscopy Based on Three-dimensional Anterior-Segment OCT: An International Multicenter Study

Author

Li, Fei, Yang, Yifan, Sun, Xu, Qiu, Zhen, Zhang, Shihao, Tun, Tin Aung, Mani, Baskaran, Nongpiur, Monisha Esther, Chansangpetch, Sunee, Ratanawongphaibul, Kitiya, Manassakorn, Anita, Tantisevi, Visanee, Rojanapongpun, Prin, Lin, Fengbin, Cheng, Weijing, Zhou, Rouxi, Liu, Yuhong, Chen, Yu, Xiong, Jian, Tan, Mingkui, Aung, Tin, Xu, Yanwu, Ting, Daniel S.W., and Zhang, Xiulan

Published
2022
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21. Deep Learning System Outperforms Clinicians in Identifying Optic Disc Abnormalities.

Author

Vasseneix, Caroline, Nusinovici, Simon, Xu, Xinxing, Hwang, Jeong-Min, Hamann, Steffen, Chen, John J., Loo, Jing Liang, Milea, Leonard, Tan, Kenneth B.K., Ting, Daniel S.W., Liu, Yong, Newman, Nancy J., Biousse, Valerie, Wong, Tien Ying, Milea, Dan, Najjar, Raymond P., Gohier, Philippe, Miller, Neil, Vanikieti, Kavin, and La Morgia, Chiara

Abstract

Supplemental Digital Content is Available in the Text. Background: The examination of the optic nerve head (optic disc) is mandatory in patients with headache, hypertension, or any neurological symptoms, yet it is rarely or poorly performed in general clinics. We recently developed a brain and optic nerve study with artificial intelligence-deep learning system (BONSAI-DLS) capable of accurately detecting optic disc abnormalities including papilledema (swelling due to elevated intracranial pressure) on digital fundus photographs with a comparable classification performance to expert neuro-ophthalmologists, but its performance compared to first-line clinicians remains unknown. Methods: In this international, cross-sectional multicenter study, the DLS, trained on 14,341 fundus photographs, was tested on a retrospectively collected convenience sample of 800 photographs (400 normal optic discs, 201 papilledema and 199 other abnormalities) from 454 patients with a robust ground truth diagnosis provided by the referring expert neuro-ophthalmologists. The areas under the receiver-operating-characteristic curves were calculated for the BONSAI-DLS. Error rates, accuracy, sensitivity, and specificity of the algorithm were compared with those of 30 clinicians with or without ophthalmic training (6 general ophthalmologists, 6 optometrists, 6 neurologists, 6 internists, 6 emergency department [ED] physicians) who graded the same testing set of images. Results: With an error rate of 15.3%, the DLS outperformed all clinicians (average error rates 24.4%, 24.8%, 38.2%, 44.8%, 47.9% for general ophthalmologists, optometrists, neurologists, internists and ED physicians, respectively) in the overall classification of optic disc appearance. The DLS displayed significantly higher accuracies than 100%, 86.7% and 93.3% of clinicians (n = 30) for the classification of papilledema, normal, and other disc abnormalities, respectively. Conclusions: The performance of the BONSAI-DLS to classify optic discs on fundus photographs was superior to that of clinicians with or without ophthalmic training. A trained DLS may offer valuable diagnostic aid to clinicians from various clinical settings for the screening of optic disc abnormalities harboring potentially sight- or life-threatening neurological conditions. [ABSTRACT FROM AUTHOR]

Published
2023
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26. Automated and Computer-Assisted Detection, Classification, and Diagnosis of Diabetic Retinopathy.

Author

Abràmoff, Michael D., Leng, Theodore, Ting, Daniel S.W., Rhee, Kyu, Horton, Mark B., Brady, Christopher J., and Chiang, Michael F.

Subjects
MEDICAL personnel, DIABETIC retinopathy, RETINAL imaging, ARTIFICIAL intelligence, DIAGNOSIS, SYSTEMS design
Abstract

Background:The introduction of artificial intelligence (AI) in medicine has raised significant ethical, economic, and scientific controversies. Introduction:Because an explicit goal of AI is to perform processes previously reserved for human clinicians and other health care personnel, there is justified concern about the impact on patient safety, efficacy, equity, and liability. Discussion:Systems for computer-assisted and fully automated detection, triage, and diagnosis of diabetic retinopathy (DR) from retinal images show great variation in design, level of autonomy, and intended use. Moreover, the degree to which these systems have been evaluated and validated is heterogeneous. We use the term DR AI system as a general term for any system that interprets retinal images with at least some degree of autonomy from a human grader. We put forth these standardized descriptors to form a means to categorize systems for computer-assisted and fully automated detection, triage, and diagnosis of DR. The components of the categorization system include level of device autonomy, intended use, level of evidence for diagnostic accuracy, and system design. Conclusion:There is currently minimal empirical basis to assert that certain combinations of autonomy, accuracy, or intended use are better or more appropriate than any other. Therefore, at the current stage of development of this document, we have been descriptive rather than prescriptive, and we treat the different categorizations as independent and organized along multiple axes. [ABSTRACT FROM AUTHOR]

Published
2020
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29. Novel technique of dog tick removal from the eyelid.

Author

Foo, Li Lian, Ting, Daniel S.W., Ng, Wei Yan, and Quah, Boon Long

Subjects
*TICKS, *SURGICAL excision, *INFECTION prevention
Abstract

A letter to the editor is presented regarding the removal of ticks from ocular tissues using en bloc removal technique with excision to prevent inflammation and infection.

Published
2016
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32. Preparedness among Ophthalmologists: During and Beyond the COVID-19 Pandemic.

Author

Olivia Li, Ji-Peng, Shantha, Jessica, Wong, Tien Y., Wong, Edmund Y., Mehta, Jod, Lin, Haotian, Lin, Xiaofeng, Strouthidis, Nicholas G., Park, Ki Ho, Fung, Adrian T., McLeod, Stephen D., Busin, Massimo, Parke II, David W., Holland, Gary N., Chodosh, James, Yeh, Steven, and Ting, Daniel S.W.

Subjects
*MEDICAL personnel, *OPHTHALMOLOGISTS, *COVID-19, *EMERGENCY physicians, *COVID-19 pandemic, *SARS Epidemic, 2002-2003
Published
2020
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