ORCID Profile
0000-0002-6882-2444
Current Organisation
Federation University Australia - Berwick Campus
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Publisher: SPIE
Date: 13-03-2017
DOI: 10.1117/12.2254050
Publisher: SPIE
Date: 13-03-2014
DOI: 10.1117/12.2043203
Publisher: SLACK, Inc.
Date: 03-2021
Publisher: Public Library of Science (PLoS)
Date: 13-05-2019
Publisher: Public Library of Science (PLoS)
Date: 17-08-2017
Publisher: SPIE
Date: 13-03-2014
DOI: 10.1117/12.2043523
Publisher: IEEE
Date: 13-04-2021
Publisher: Association for Research in Vision and Ophthalmology (ARVO)
Date: 19-11-2020
Publisher: Association for Research in Vision and Ophthalmology (ARVO)
Date: 09-08-2023
DOI: 10.1167/TVST.12.8.6
Publisher: MDPI AG
Date: 20-04-2023
DOI: 10.3390/JCM12083013
Abstract: Patients diagnosed with exudative neovascular age-related macular degeneration are commonly treated with anti-vascular endothelial growth factor (anti-VEGF) agents. However, response to treatment is heterogeneous, without a clinical explanation. Predicting suboptimal response at baseline will enable more efficient clinical trial designs for novel, future interventions and facilitate in idualised therapies. In this multicentre study, we trained a multi-modal artificial intelligence (AI) system to identify suboptimal responders to the loading-phase of the anti-VEGF agent aflibercept from baseline characteristics. We collected clinical features and optical coherence tomography scans from 1720 eyes of 1612 patients between 2019 and 2021. We evaluated our AI system as a patient selection method by emulating hypothetical clinical trials of different sizes based on our test set. Our method detected up to 57.6% more suboptimal responders than random selection, and up to 24.2% more than any alternative selection criteria tested. Applying this method to the entry process of candidates into randomised controlled trials may contribute to the success of such trials and further inform personalised care.
Publisher: Springer International Publishing
Date: 2019
Publisher: The Optical Society
Date: 13-11-2018
DOI: 10.1364/BOE.9.006205
Publisher: Elsevier BV
Date: 05-2022
Publisher: SPIE
Date: 23-02-2012
DOI: 10.1117/12.911491
Publisher: The Optical Society
Date: 27-07-2011
DOI: 10.1364/BOE.2.002403
Publisher: SPIE
Date: 04-03-2010
DOI: 10.1117/12.843928
Publisher: Elsevier BV
Date: 09-2022
Publisher: IEEE
Date: 04-2018
Publisher: Springer International Publishing
Date: 2022
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 08-2019
DOI: 10.1016/J.TIPS.2019.05.005
Abstract: Clinical trials consume the latter half of the 10 to 15 year, 1.5-2.0 billion USD, development cycle for bringing a single new drug to market. Hence, a failed trial sinks not only the investment into the trial itself but also the preclinical development costs, rendering the loss per failed clinical trial at 800 million to 1.4 billion USD. Suboptimal patient cohort selection and recruiting techniques, paired with the inability to monitor patients effectively during trials, are two of the main causes for high trial failure rates: only one of 10 compounds entering a clinical trial reaches the market. We explain how recent advances in artificial intelligence (AI) can be used to reshape key steps of clinical trial design towards increasing trial success rates.
Publisher: Optica Publishing Group
Date: 04-02-2019
Publisher: SPIE
Date: 13-03-2017
DOI: 10.1117/12.2257432
Publisher: The Optical Society
Date: 11-2013
DOI: 10.1364/BOE.4.002712
Publisher: Elsevier BV
Date: 2021
Publisher: Portland Press Ltd.
Date: 18-10-2019
DOI: 10.1042/BIO04105010
Abstract: Artificial intelligence (AI) is certainly not a panacea for solving the ‘Pharma Dilemma’, in which the cost of producing new drugs continues to spiral. However, AI can be used to fundamentally change the way we perform essential steps in clinical trial design and execution, from cohort selection to patient monitoring. Merging AI and clinical expertise across engineering and medical disciplines to explore the impact of these changes on trial performance and success rates is one of the most promising leads we have for restoring efficiency and sustainability to the drug development cycle.
Publisher: Cold Spring Harbor Laboratory
Date: 26-10-2023
Publisher: Elsevier BV
Date: 2020
Publisher: Informa UK Limited
Date: 14-12-2018
Publisher: Association for Research in Vision and Ophthalmology (ARVO)
Date: 11-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Publisher: SPIE
Date: 29-03-2016
DOI: 10.1117/12.2216096
Publisher: Elsevier BV
Date: 03-2023
Publisher: IEEE
Date: 12-2006
Publisher: Elsevier BV
Date: 07-2023
Publisher: Cambridge University Press (CUP)
Date: 2016
DOI: 10.1017/S0952523816000067
Abstract: Studies into the mechanisms underlying the active emmetropization process by which neonatal refractive errors are corrected, have described rapid, compensatory changes in the thickness of the choroidal layer in response to imposed optical defocus. While high frequency A-scan ultrasonography, as traditionally used to characterize such changes, offers good resolution of central (on-axis) changes, evidence of local retinal control mechanisms make it imperative that more peripheral, off-axis changes also be tracked. In this study, we used in vivo high resolution spectral domain-optical coherence tomography (SD-OCT) imaging in combination with the Iowa Reference Algorithms for 3-dimensional segmentation, to more fully characterize these changes, both spatially and temporally, in young, 7-day old chicks ( n = 15), which were fitted with monocular +15 D defocusing lenses to induce choroidal thickening. With these tools, we were also able to localize the retinal area centralis, which was used as a landmark along with the ocular pectin in standardizing the location of scans and aligning them for subsequent analyses of choroidal thickness (CT) changes across time and between eyes. Values were derived for each of four quadrants, centered on the area centralis, and global CT values were also derived for all eyes. Data were compared with on-axis changes measured using ultrasonography. There were significant on-axis choroidal thickening that was detected after just one day of lens wear (∼190 µ m), and regional (quadrant-related) differences in choroidal responses were also found, as well as global thickness changes 1 day after treatment. The ratio of global to on-axis choroidal thicknesses, used as an index of regional variability in responses, was also found to change significantly, reflecting the significant central changes. In summary, we demonstrated in vivo high resolution SD-OCT imaging, used in combination with segmentation algorithms, to be a viable and informative approach for characterizing regional (spatial), time-sensitive changes in CT in small animals such as the chick.
Publisher: Springer International Publishing
Date: 2018
Publisher: Public Library of Science (PLoS)
Date: 07-2019
Publisher: SPIE
Date: 21-03-2016
DOI: 10.1117/12.2214676
Location: United States of America
No related grants have been discovered for Bhavna Antony.