DSpace Community:http://hdl.handle.net/10722/1636392024-03-29T09:09:28Z2024-03-29T09:09:28ZRandomized Control Trial of the Impact of Patient Decision Aid Developed for Chinese Primary Open-Angle Glaucoma PatientsZhu, Ming MingChoy, Bonnie NKLam, Wendy WTShum, Jennifer WHhttp://hdl.handle.net/10722/3408822024-03-11T10:48:00Z2023-03-27T00:00:00ZTitle: Randomized Control Trial of the Impact of Patient Decision Aid Developed for Chinese Primary Open-Angle Glaucoma Patients
Authors: Zhu, Ming Ming; Choy, Bonnie NK; Lam, Wendy WT; Shum, Jennifer WH
Abstract: <p><strong><em>Introduction:</em></strong> Patient decision aid (PDA) is a tool to prompt shared decision-making. The aim of this study was to evaluate the impact of a PDA on Chinese primary open-angle glaucoma patients. <strong><em>Methods:</em></strong> All subjects were randomized into control and PDA group. The questionnaires, including 1) glaucoma knowledge; 2) 8-item Morisky medication adherence scale (MMAS-8); 3) 10-item glaucoma medication adherence self-efficacy scale (GMASES-10); and 4) 16-item decision conflict scale (DCS), were evaluated at baseline, 3- and 6-month follow-up. <strong><em>Results:</em></strong> Totally, 156 subjects participated in this study, including 77 in the control group and 79 in the PDA group. Compared to the control group, PDA group showed around 1 point more improvement in disease knowledge at both 3 and 6 months (both <em>p</em> < 0.05), 2.5 (95% CI: [1.0, 4.1]) and 1.9 (95% CI: [0.2, 3.7]) points more improvement in GMASES-10 at 3 and 6 months, respectively, and reduction in DCS by 8.8 (95% CI: [4.6, 12.9]) points more at 3 months and 13.5 (95% CI: [8.9, 18.0]) points more at 6 months. No difference was detected in MMAS-8. <strong><em>Conclusion:</em></strong> PDA led to improvement in disease knowledge and self-confidence in medication adherence and reduced decision conflict compared to control group for at least 6 months.<br></p>2023-03-27T00:00:00ZCrystalline lens nuclear age prediction as a new biomarker of nucleus degenerationGuo, MengjieHigashita, RisaLin, ChenHu, LingxiChen, WanLi, FeiLai, Gilda Wing KiNguyen, AnwellSakata, ReiOkamoto, KeiichiroTang, BoXu, YanwuFu, HuazhuGao, FeiAihara, MakotoZhang, XiulanYuan, JinLin, ShanLeung, Christopher Kai-ShunLiu, Jianghttp://hdl.handle.net/10722/3408782024-03-11T10:47:59Z2023-07-26T00:00:00ZTitle: Crystalline lens nuclear age prediction as a new biomarker of nucleus degeneration
Authors: Guo, Mengjie; Higashita, Risa; Lin, Chen; Hu, Lingxi; Chen, Wan; Li, Fei; Lai, Gilda Wing Ki; Nguyen, Anwell; Sakata, Rei; Okamoto, Keiichiro; Tang, Bo; Xu, Yanwu; Fu, Huazhu; Gao, Fei; Aihara, Makoto; Zhang, Xiulan; Yuan, Jin; Lin, Shan; Leung, Christopher Kai-Shun; Liu, Jiang
Abstract: <p>Background: The crystalline lens is a transparent structure of the eye to focus light on the retina. It becomes muddy, hard and dense with increasing age, which makes the crystalline lens gradually lose its function. We aim to develop a nuclear age predictor to reflect the degeneration of the crystalline lens nucleus. Methods: First we trained and internally validated the nuclear age predictor with a deep-learning algorithm, using 12 904 anterior segment optical coherence tomography (AS-OCT) images from four diverse Asian and American cohorts: Zhongshan Ophthalmic Center with Machine0 (ZOM0), Tomey Corporation (TOMEY), University of California San Francisco and the Chinese University of Hong Kong. External testing was done on three independent datasets: Tokyo University (TU), ZOM1 and Shenzhen People's Hospital (SPH). We also demonstrate the possibility of detecting nuclear cataracts (NCs) from the nuclear age gap. Findings: In the internal validation dataset, the nuclear age could be predicted with a mean absolute error (MAE) of 2.570 years (95% CI 1.886 to 2.863). Across the three external testing datasets, the algorithm achieved MAEs of 4.261 years (95% CI 3.391 to 5.094) in TU, 3.920 years (95% CI 3.332 to 4.637) in ZOM1-NonCata and 4.380 years (95% CI 3.730 to 5.061) in SPH-NonCata. The MAEs for NC eyes were 8.490 years (95% CI 7.219 to 9.766) in ZOM1-NC and 9.998 years (95% CI 5.673 to 14.642) in SPH-NC. The nuclear age gap outperformed both ophthalmologists in detecting NCs, with areas under the receiver operating characteristic curves of 0.853 years (95% CI 0.787 to 0.917) in ZOM1 and 0.909 years (95% CI 0.828 to 0.978) in SPH. Interpretation: The nuclear age predictor shows good performance, validating the feasibility of using AS-OCT images as an effective screening tool for nucleus degeneration. Our work also demonstrates the potential use of the nuclear age gap to detect NCs.</p>2023-07-26T00:00:00ZThe Definition of Glaucomatous Optic Neuropathy in Artificial Intelligence Research and Clinical ApplicationsMedeiros, Felipe ALee, TerryJammal, Alessandro AAl-Aswad, Lama AEydelman, Malvina BSchuman, Joel SAbramoff, MichaelBlumenkranz, MarkChew, EmilyChiang, MichaelEydelman, MalvinaMyung, DavidSchuman, Joel SShields, CarolAbramoff, MichaelAl-Aswad, LamaAntony, Bhavna JAung, TinBoland, MichaelBrunner, TomChang, Robert TChauhan, BalwantrayChiang, MichaelCherwek, D HunterGarway-Heath, DavidGraves, AdrienneGoldberg, Jeffrey LHe, MinguangHammel, NaamaHood, DonaldIshikawa, HiroshiLeung, ChrisMedeiros, FelipePasquale, Louis RQuigley, Harry ARoberts, Calvin WRobin, Alan LSchuman, Joel SSturman, ElenaSusanna, RemoVianna, JaymeZangwill, Lindahttp://hdl.handle.net/10722/3408792024-03-11T10:47:59Z2023-01-31T00:00:00ZTitle: The Definition of Glaucomatous Optic Neuropathy in Artificial Intelligence Research and Clinical Applications
Authors: Medeiros, Felipe A; Lee, Terry; Jammal, Alessandro A; Al-Aswad, Lama A; Eydelman, Malvina B; Schuman, Joel S; Abramoff, Michael; Blumenkranz, Mark; Chew, Emily; Chiang, Michael; Eydelman, Malvina; Myung, David; Schuman, Joel S; Shields, Carol; Abramoff, Michael; Al-Aswad, Lama; Antony, Bhavna J; Aung, Tin; Boland, Michael; Brunner, Tom; Chang, Robert T; Chauhan, Balwantray; Chiang, Michael; Cherwek, D Hunter; Garway-Heath, David; Graves, Adrienne; Goldberg, Jeffrey L; He, Minguang; Hammel, Naama; Hood, Donald; Ishikawa, Hiroshi; Leung, Chris; Medeiros, Felipe; Pasquale, Louis R; Quigley, Harry A; Roberts, Calvin W; Robin, Alan L; Schuman, Joel S; Sturman, Elena; Susanna, Remo; Vianna, Jayme; Zangwill, Linda
Abstract: <p>Objective: Although artificial intelligence (AI) models may offer innovative and powerful ways to use the wealth of data generated by diagnostic tools, there are important challenges related to their development and validation. Most notable is the lack of a perfect reference standard for glaucomatous optic neuropathy (GON). Because AI models are trained to predict presence of glaucoma or its progression, they generally rely on a reference standard that is used to train the model and assess its validity. If an improper reference standard is used, the model may be trained to detect or predict something that has little or no clinical value. This article summarizes the issues and discussions related to the definition of GON in AI applications as presented by the Glaucoma Workgroup from the Collaborative Community for Ophthalmic Imaging (CCOI) US Food and Drug Administration Virtual Workshop, on September 3 and 4, 2020, and on January 28, 2022. Design: Review and conference proceedings. Subjects: No human or animal subjects or data therefrom were used in the production of this article. Methods: A summary of the Workshop was produced with input and approval from all participants. Main Outcome Measures: Consensus position of the CCOI Workgroup on the challenges in defining GON and possible solutions. Results: The Workshop reviewed existing challenges that arise from the use of subjective definitions of GON and highlighted the need for a more objective approach to characterize GON that could facilitate replication and comparability of AI studies and allow for better clinical validation of proposed AI tools. Different tests and combination of parameters for defining a reference standard for GON have been proposed. Different reference standards may need to be considered depending on the scenario in which the AI models are going to be applied, such as community-based or opportunistic screening versus detection or monitoring of glaucoma in tertiary care. Conclusions: The development and validation of new AI-based diagnostic tests should be based on rigorous methodology with clear determination of how the reference standards for glaucomatous damage are constructed and the settings where the tests are going to be applied. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references. Ophthalmology Glaucoma 2023;6:432-438 2023 by the American Academy of Ophthalmology<br></p>2023-01-31T00:00:00ZReverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applicationsMa, DaPasquale, Louis RGirard, Michaël J ALeung, Christopher K SJia, YaliSarunic, Marinko VSappington, Rebecca MChan, Kevin Chttp://hdl.handle.net/10722/3408762024-03-11T10:47:58Z2023-01-04T00:00:00ZTitle: Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications
Authors: Ma, Da; Pasquale, Louis R; Girard, Michaël J A; Leung, Christopher K S; Jia, Yali; Sarunic, Marinko V; Sappington, Rebecca M; Chan, Kevin C
Abstract: <p>Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.<br></p>2023-01-04T00:00:00Z