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Pucchio A, Krance S, Pur DR, Bassi A, Miranda R, Felfeli T. The role of artificial intelligence in analysis of biofluid markers for diagnosis and management of glaucoma: A systematic review. Eur J Ophthalmol 2023; 33:1816-1833. [PMID: 36426575 PMCID: PMC10469503 DOI: 10.1177/11206721221140948] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 11/01/2022] [Indexed: 08/31/2023]
Abstract
PURPOSE This review focuses on utility of artificial intelligence (AI) in analysis of biofluid markers in glaucoma. We detail the accuracy and validity of AI in the exploration of biomarkers to provide insight into glaucoma pathogenesis. METHODS A comprehensive search was conducted across five electronic databases including Embase, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science. Studies pertaining to biofluid marker analysis using AI or bioinformatics in glaucoma were included. Identified studies were critically appraised and assessed for risk of bias using the Joanna Briggs Institute Critical Appraisal tools. RESULTS A total of 10,258 studies were screened and 39 studies met the inclusion criteria, including 23 cross-sectional studies (59%), nine prospective cohort studies (23%), six retrospective cohort studies (15%), and one case-control study (3%). Primary open angle glaucoma (POAG) was the most commonly studied subtype (55% of included studies). Twenty-four studies examined disease characteristics, 10 explored treatment decisions, and 5 provided diagnostic clarification. While studies examined at entire metabolomic or proteomic profiles to determine changes in POAG, there was heterogeneity in the data with over 175 unique, differentially expressed biomarkers reported. Discriminant analysis and artificial neural network predictive models displayed strong differentiating ability between glaucoma patients and controls, although these tools were untested in a clinical context. CONCLUSION The use of AI models could inform glaucoma diagnosis with high sensitivity and specificity. While insight into differentially expressed biomarkers is valuable in pathogenic exploration, no clear pathogenic mechanism in glaucoma has emerged.
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Affiliation(s)
- Aidan Pucchio
- School of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Saffire Krance
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Daiana R Pur
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Arshpreet Bassi
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Rafael Miranda
- Toronto Health Economics and Technology Assessment Collaborative, University of Toronto, Toronto, Ontario, Canada
- The Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Tina Felfeli
- Toronto Health Economics and Technology Assessment Collaborative, University of Toronto, Toronto, Ontario, Canada
- Department of Ophthalmology and Visual Sciences, University of Toronto, Toronto, Ontario, Canada
- The Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
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Chen JS, Baxter SL, van den Brandt A, Lieu A, Camp AS, Do JL, Welsbie DS, Moghimi S, Christopher M, Weinreb RN, Zangwill LM. Usability and Clinician Acceptance of a Deep Learning-Based Clinical Decision Support Tool for Predicting Glaucomatous Visual Field Progression. J Glaucoma 2023; 32:151-158. [PMID: 36877820 PMCID: PMC9996451 DOI: 10.1097/ijg.0000000000002163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/19/2023] [Indexed: 03/08/2023]
Abstract
PRCIS We updated a clinical decision support tool integrating predicted visual field (VF) metrics from an artificial intelligence model and assessed clinician perceptions of the predicted VF metric in this usability study. PURPOSE To evaluate clinician perceptions of a prototyped clinical decision support (CDS) tool that integrates visual field (VF) metric predictions from artificial intelligence (AI) models. METHODS Ten ophthalmologists and optometrists from the University of California San Diego participated in 6 cases from 6 patients, consisting of 11 eyes, uploaded to a CDS tool ("GLANCE", designed to help clinicians "at a glance"). For each case, clinicians answered questions about management recommendations and attitudes towards GLANCE, particularly regarding the utility and trustworthiness of the AI-predicted VF metrics and willingness to decrease VF testing frequency. MAIN OUTCOMES AND MEASURES Mean counts of management recommendations and mean Likert scale scores were calculated to assess overall management trends and attitudes towards the CDS tool for each case. In addition, system usability scale scores were calculated. RESULTS The mean Likert scores for trust in and utility of the predicted VF metric and clinician willingness to decrease VF testing frequency were 3.27, 3.42, and 2.64, respectively (1=strongly disagree, 5=strongly agree). When stratified by glaucoma severity, all mean Likert scores decreased as severity increased. The system usability scale score across all responders was 66.1±16.0 (43rd percentile). CONCLUSIONS A CDS tool can be designed to present AI model outputs in a useful, trustworthy manner that clinicians are generally willing to integrate into their clinical decision-making. Future work is needed to understand how to best develop explainable and trustworthy CDS tools integrating AI before clinical deployment.
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Affiliation(s)
- Jimmy S Chen
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Sally L Baxter
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | | | - Alexander Lieu
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
| | - Andrew S Camp
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
| | - Jiun L Do
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
| | - Derek S Welsbie
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
| | - Sasan Moghimi
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
| | - Mark Christopher
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
| | - Robert N Weinreb
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
| | - Linda M Zangwill
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
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Gandy C, Petrakos P, Van Tassel SH. Is Telehealth Here to Stay? The Role of Telehealth in the Screening, Diagnosis, and Management of Glaucoma. Int Ophthalmol Clin 2022; 62:45-50. [PMID: 36170221 DOI: 10.1097/iio.0000000000000432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Bhartiya S. Glaucoma Screening: Is AI the Answer? J Curr Glaucoma Pract 2022; 16:71-73. [PMID: 36128081 PMCID: PMC9452706 DOI: 10.5005/jp-journals-10078-1380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Affiliation(s)
- Shibal Bhartiya
- Department of Ophthalmology, Glaucoma Services, Fortis Memorial Research Institute, Gurugram, Haryana, India
- Shibal Bhartiya, Department of Ophthalmology, Glaucoma Services, Fortis Memorial Research Institute, Gurugram, Haryana, India, e-mail:
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Alexopoulos P, Madu C, Wollstein G, Schuman JS. The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques. Front Med (Lausanne) 2022; 9:891369. [PMID: 35847772 PMCID: PMC9279625 DOI: 10.3389/fmed.2022.891369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/23/2022] [Indexed: 11/22/2022] Open
Abstract
The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.
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Affiliation(s)
- Palaiologos Alexopoulos
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Chisom Madu
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Gadi Wollstein
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Center for Neural Science, College of Arts & Science, New York University, New York, NY, United States
| | - Joel S. Schuman
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Center for Neural Science, College of Arts & Science, New York University, New York, NY, United States
- Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
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Wu Y, Szymanska M, Hu Y, Fazal MI, Jiang N, Yetisen AK, Cordeiro MF. Measures of disease activity in glaucoma. Biosens Bioelectron 2021; 196:113700. [PMID: 34653715 DOI: 10.1016/j.bios.2021.113700] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/01/2021] [Accepted: 10/08/2021] [Indexed: 12/13/2022]
Abstract
Glaucoma is the leading cause of irreversible blindness globally which significantly affects the quality of life and has a substantial economic impact. Effective detective methods are necessary to identify glaucoma as early as possible. Regular eye examinations are important for detecting the disease early and preventing deterioration of vision and quality of life. Current methods of measuring disease activity are powerful in describing the functional and structural changes in glaucomatous eyes. However, there is still a need for a novel tool to detect glaucoma earlier and more accurately. Tear fluid biomarker analysis and new imaging technology provide novel surrogate endpoints of glaucoma. Artificial intelligence is a post-diagnostic tool that can analyse ophthalmic test results. A detail review of currently used clinical tests in glaucoma include intraocular pressure test, visual field test and optical coherence tomography are presented. The advanced technologies for glaucoma measurement which can identify specific disease characteristics, as well as the mechanism, performance and future perspectives of these devices are highlighted. Applications of AI in diagnosis and prediction in glaucoma are mentioned. With the development in imaging tools, sensor technologies and artificial intelligence, diagnostic evaluation of glaucoma must assess more variables to facilitate earlier diagnosis and management in the future.
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Affiliation(s)
- Yue Wu
- Department of Surgery and Cancer, Imperial College London, South Kensington, London, United Kingdom; Department of Chemical Engineering, Imperial College London, South Kensington, London, United Kingdom
| | - Maja Szymanska
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, United Kingdom
| | - Yubing Hu
- Department of Chemical Engineering, Imperial College London, South Kensington, London, United Kingdom.
| | - M Ihsan Fazal
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, United Kingdom
| | - Nan Jiang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Ali K Yetisen
- Department of Chemical Engineering, Imperial College London, South Kensington, London, United Kingdom
| | - M Francesca Cordeiro
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, United Kingdom; The Western Eye Hospital, Imperial College Healthcare NHS Trust (ICHNT), London, United Kingdom; Glaucoma and Retinal Neurodegeneration Group, Department of Visual Neuroscience, UCL Institute of Ophthalmology, London, United Kingdom.
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