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Chen JS, Copado IA, Vallejos C, Kalaw FGP, Soe P, Cai CX, Toy BC, Borkar D, Sun CQ, Shantha JG, Baxter SL. Variations in Electronic Health Record-Based Definitions of Diabetic Retinopathy Cohorts: A Literature Review and Quantitative Analysis. OPHTHALMOLOGY SCIENCE 2024; 4:100468. [PMID: 38560278 PMCID: PMC10973665 DOI: 10.1016/j.xops.2024.100468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 01/04/2024] [Accepted: 01/11/2024] [Indexed: 04/04/2024]
Abstract
Purpose Use of the electronic health record (EHR) has motivated the need for data standardization. A gap in knowledge exists regarding variations in existing terminologies for defining diabetic retinopathy (DR) cohorts. This study aimed to review the literature and analyze variations regarding codified definitions of DR. Design Literature review and quantitative analysis. Subjects Published manuscripts. Methods Four graders reviewed PubMed and Google Scholar for peer-reviewed studies. Studies were included if they used codified definitions of DR (e.g., billing codes). Data elements such as author names, publication year, purpose, data set type, and DR definitions were manually extracted. Each study was reviewed by ≥ 2 authors to validate inclusion eligibility. Quantitative analyses of the codified definitions were then performed to characterize the variation between DR cohort definitions. Main Outcome Measures Number of studies included and numeric counts of billing codes used to define codified cohorts. Results In total, 43 studies met the inclusion criteria. Half of the included studies used datasets based on structured EHR data (i.e., data registries, institutional EHR review), and half used claims data. All but 1 of the studies used billing codes such as the International Classification of Diseases 9th or 10th edition (ICD-9 or ICD-10), either alone or in addition to another terminology for defining disease. Of the 27 included studies that used ICD-9 and the 20 studies that used ICD-10 codes, the most common codes used pertained to the full spectrum of DR severity. Diabetic retinopathy complications (e.g., vitreous hemorrhage) were also used to define some DR cohorts. Conclusions Substantial variations exist among codified definitions for DR cohorts within retrospective studies. Variable definitions may limit generalizability and reproducibility of retrospective studies. More work is needed to standardize disease cohorts. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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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, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Ivan A. Copado
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Cecilia Vallejos
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Fritz Gerald P. Kalaw
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Priyanka Soe
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Cindy X. Cai
- Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Brian C. Toy
- Department of Ophthalmology, Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Durga Borkar
- Department of Ophthalmology, Duke Eye Center, Duke University, Durham, North Carolina
| | - Catherine Q. Sun
- F.I. Proctor Foundation, University of California San Francisco, San Francisco, California
- Department of Ophthalmology, University of California San Francisco, San Francisco, California
| | - Jessica G. Shantha
- F.I. Proctor Foundation, University of California San Francisco, San Francisco, California
- Department of Ophthalmology, University of California San Francisco, San Francisco, California
| | - Sally L. Baxter
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
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Ong J, Jang KJ, Baek SJ, Hu D, Lin V, Jang S, Thaler A, Sabbagh N, Saeed A, Kwon M, Kim JH, Lee S, Han YS, Zhao M, Sokolsky O, Lee I, Al-Aswad LA. Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians. Asia Pac J Ophthalmol (Phila) 2024; 13:100095. [PMID: 39209216 DOI: 10.1016/j.apjo.2024.100095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 08/19/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024] Open
Abstract
Artificial Intelligence (AI) is transforming healthcare, notably in ophthalmology, where its ability to interpret images and data can significantly enhance disease diagnosis and patient care. Recent developments in oculomics, the integration of ophthalmic features to develop biomarkers for systemic diseases, have demonstrated the potential for providing rapid, non-invasive methods of screening leading to enhance in early detection and improve healthcare quality, particularly in underserved areas. However, the widespread adoption of such AI-based technologies faces challenges primarily related to the trustworthiness of the system. We demonstrate the potential and considerations needed to develop trustworthy AI in oculomics through a pilot study for HbA1c assessment using an AI-based approach. We then discuss various challenges, considerations, and solutions that have been developed for powerful AI technologies in the past in healthcare and subsequently apply these considerations to the oculomics pilot study. Building upon the observations in the study we highlight the challenges and opportunities for advancing trustworthy AI in oculomics. Ultimately, oculomics presents as a powerful and emerging technology in ophthalmology and understanding how to optimize transparency prior to clinical adoption is of utmost importance.
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Affiliation(s)
- Joshua Ong
- Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, MI, United States
| | - Kuk Jin Jang
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Seung Ju Baek
- Department of AI Convergence Engineering, Republic of Korea
| | - Dongyin Hu
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Vivian Lin
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Sooyong Jang
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Alexandra Thaler
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Nouran Sabbagh
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Almiqdad Saeed
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States; St John Eye Hospital-Jerusalem, Department of Ophthalmology, Israel
| | - Minwook Kwon
- Department of AI Convergence Engineering, Republic of Korea
| | - Jin Hyun Kim
- Department of Intelligence and Communication Engineering, Republic of Korea
| | - Seongjin Lee
- Department of AI Convergence Engineering, Republic of Korea
| | - Yong Seop Han
- Department of Ophthalmology, Gyeongsang National University College of Medicine, Institute of Health Sciences, Republic of Korea
| | - Mingmin Zhao
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Oleg Sokolsky
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Insup Lee
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States.
| | - Lama A Al-Aswad
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States; Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.
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Young JA, Chang CW, Scales CW, Menon SV, Holy CE, Blackie CA. Machine Learning Methods Using Artificial Intelligence Deployed on Electronic Health Record Data for Identification and Referral of At-Risk Patients From Primary Care Physicians to Eye Care Specialists: Retrospective, Case-Controlled Study. JMIR AI 2024; 3:e48295. [PMID: 38875582 PMCID: PMC11041486 DOI: 10.2196/48295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/11/2023] [Accepted: 02/10/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND Identification and referral of at-risk patients from primary care practitioners (PCPs) to eye care professionals remain a challenge. Approximately 1.9 million Americans suffer from vision loss as a result of undiagnosed or untreated ophthalmic conditions. In ophthalmology, artificial intelligence (AI) is used to predict glaucoma progression, recognize diabetic retinopathy (DR), and classify ocular tumors; however, AI has not yet been used to triage primary care patients for ophthalmology referral. OBJECTIVE This study aimed to build and compare machine learning (ML) methods, applicable to electronic health records (EHRs) of PCPs, capable of triaging patients for referral to eye care specialists. METHODS Accessing the Optum deidentified EHR data set, 743,039 patients with 5 leading vision conditions (age-related macular degeneration [AMD], visually significant cataract, DR, glaucoma, or ocular surface disease [OSD]) were exact-matched on age and gender to 743,039 controls without eye conditions. Between 142 and 182 non-ophthalmic parameters per patient were input into 5 ML methods: generalized linear model, L1-regularized logistic regression, random forest, Extreme Gradient Boosting (XGBoost), and J48 decision tree. Model performance was compared for each pathology to select the most predictive algorithm. The area under the curve (AUC) was assessed for all algorithms for each outcome. RESULTS XGBoost demonstrated the best performance, showing, respectively, a prediction accuracy and an AUC of 78.6% (95% CI 78.3%-78.9%) and 0.878 for visually significant cataract, 77.4% (95% CI 76.7%-78.1%) and 0.858 for exudative AMD, 79.2% (95% CI 78.8%-79.6%) and 0.879 for nonexudative AMD, 72.2% (95% CI 69.9%-74.5%) and 0.803 for OSD requiring medication, 70.8% (95% CI 70.5%-71.1%) and 0.785 for glaucoma, 85.0% (95% CI 84.2%-85.8%) and 0.924 for type 1 nonproliferative diabetic retinopathy (NPDR), 82.2% (95% CI 80.4%-84.0%) and 0.911 for type 1 proliferative diabetic retinopathy (PDR), 81.3% (95% CI 81.0%-81.6%) and 0.891 for type 2 NPDR, and 82.1% (95% CI 81.3%-82.9%) and 0.900 for type 2 PDR. CONCLUSIONS The 5 ML methods deployed were able to successfully identify patients with elevated odds ratios (ORs), thus capable of patient triage, for ocular pathology ranging from 2.4 (95% CI 2.4-2.5) for glaucoma to 5.7 (95% CI 5.0-6.4) for type 1 NPDR, with an average OR of 3.9. The application of these models could enable PCPs to better identify and triage patients at risk for treatable ophthalmic pathology. Early identification of patients with unrecognized sight-threatening conditions may lead to earlier treatment and a reduced economic burden. More importantly, such triage may improve patients' lives.
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Affiliation(s)
- Joshua A Young
- Department of Ophthalmology, New York University School of Medicine, New York, NY, United States
| | - Chin-Wen Chang
- Data Science, Johnson & Johnson MedTech, Raritan, NJ, United States
| | - Charles W Scales
- Medical and Scientific Operations, Johnson & Johnson Medtech, Vision, Jacksonville, FL, United States
| | - Saurabh V Menon
- Mu Sigma Business Solutions Private Limited, Bangalore, India
| | - Chantal E Holy
- Epidemiology and Real-World Data Sciences, Johnson & Johnson MedTech, New Brunswick, NJ, United States
| | - Caroline Adrienne Blackie
- Medical and Scientific Operations, Johnson & Johnson MedTech, Vision, Jacksonville, FL, United States
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Ahuja AS, Rahimy E, Sridhar J. Tracking Online Interest in Artificial Intelligence in Ophthalmology Using Google Trends. Semin Ophthalmol 2023; 38:644-647. [PMID: 37095683 DOI: 10.1080/08820538.2023.2204919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 02/15/2023] [Accepted: 04/17/2023] [Indexed: 04/26/2023]
Abstract
PURPOSE To examine trends in internet search queries related to artificial intelligence (AI) in ophthalmology and determine the correlation between online interest in AI, capital investment in AI, and peer-reviewed indexed publications regarding AI and ophthalmology. METHODS Online search trends for "AI retina", "AI eye", and "AI healthcare" were obtained via Google Trends from 2016 to 2022 on a relative interest scale in 1-week intervals. Global venture financing of AI- and machine learning (ML)-focused companies in healthcare was tracked from 2010 to 2019 from the consulting company, Klynveld Peat Marwick Goerdeler (KPMG), and the technology market intelligence company, CB Insights. Citation count from pubmed.gov was determined using the search query "artificial intelligence retina" from 2012 to 2021. RESULTS An increasingly linear growth in online search trends for "AI retina", "AI eye", and "AI healthcare" keyword searches was observed between 2016 and 2022. Global venture financing of AI and ML companies in healthcare also increased exponentially over the same time frame. There was an exponential increase in citations with nearly a 10-fold increase as reported by PubMed from 2015 onwards for the "artificial intelligence retina" search query. There was a significant and positive correlation between online search trends and investment trends (correlation coefficients of 0.98-0.99 and p-values <0.05) and between online search trends and citation count trends (correlation coefficients of 0.98-0.99 and p-values <0.05). CONCLUSIONS These results demonstrate that the applications of AI and ML in ophthalmology are increasingly being investigated, financed, and formally researched, suggesting a prominent role for AI-derived tools in ophthalmology clinical practice in the near future.
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Affiliation(s)
- Abhimanyu S Ahuja
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, USA
| | - Ehsan Rahimy
- Department of Ophthalmology, Palo Alto Medical Foundation, Palo Alto, CA, USA
- Byers Eye Institute, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Jayanth Sridhar
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL, USA
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Chew EY. Publication of Datasets, a Step toward Advancing Data Science. OPHTHALMOLOGY SCIENCE 2023; 3:100381. [PMID: 37810588 PMCID: PMC10556280 DOI: 10.1016/j.xops.2023.100381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
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Knapp AN, Leng T, Rahimy E. Ophthalmology at the Forefront of Big Data Integration in Medicine: Insights from the IRIS Registry Database. THE YALE JOURNAL OF BIOLOGY AND MEDICINE 2023; 96:421-426. [PMID: 37780991 PMCID: PMC10524808 DOI: 10.59249/vupm2510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Ophthalmology stands at the vanguard of incorporating big data into medicine, as exemplified by the integration of The Intelligent Research in Sight (IRIS) Registry. This synergy cultivates patient-centered care, demonstrates real world efficacy and safety data for new therapies, and facilitates comprehensive population health insights. By evaluating the creation and utilization of the world's largest specialty clinical data registry, we underscore the transformative capacity of data-driven medical paradigms, current shortcomings, and future directions. We aim to provide a scaffold for other specialties to adopt big data integration into medicine.
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Affiliation(s)
- Austen N. Knapp
- Department of Ophthalmology, Byers Eye Institute,
Stanford University School of Medicine, Palo Alto, CA, USA
| | - Theodore Leng
- Department of Ophthalmology, Byers Eye Institute,
Stanford University School of Medicine, Palo Alto, CA, USA
| | - Ehsan Rahimy
- Department of Ophthalmology, Byers Eye Institute,
Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Ophthalmology, Palo Alto Medical
Foundation, Palo Alto, CA, USA
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7
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Chen JS, Baxter SL. Applications of natural language processing in ophthalmology: present and future. Front Med (Lausanne) 2022; 9:906554. [PMID: 36004369 PMCID: PMC9393550 DOI: 10.3389/fmed.2022.906554] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
Advances in technology, including novel ophthalmic imaging devices and adoption of the electronic health record (EHR), have resulted in significantly increased data available for both clinical use and research in ophthalmology. While artificial intelligence (AI) algorithms have the potential to utilize these data to transform clinical care, current applications of AI in ophthalmology have focused mostly on image-based deep learning. Unstructured free-text in the EHR represents a tremendous amount of underutilized data in big data analyses and predictive AI. Natural language processing (NLP) is a type of AI involved in processing human language that can be used to develop automated algorithms using these vast quantities of available text data. The purpose of this review was to introduce ophthalmologists to NLP by (1) reviewing current applications of NLP in ophthalmology and (2) exploring potential applications of NLP. We reviewed current literature published in Pubmed and Google Scholar for articles related to NLP and ophthalmology, and used ancestor search to expand our references. Overall, we found 19 published studies of NLP in ophthalmology. The majority of these publications (16) focused on extracting specific text such as visual acuity from free-text notes for the purposes of quantitative analysis. Other applications included: domain embedding, predictive modeling, and topic modeling. Future ophthalmic applications of NLP may also focus on developing search engines for data within free-text notes, cleaning notes, automated question-answering, and translating ophthalmology notes for other specialties or for patients, especially with a growing interest in open notes. As medicine becomes more data-oriented, NLP offers increasing opportunities to augment our ability to harness free-text data and drive innovations in healthcare delivery and treatment of ophthalmic conditions.
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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, University of California San Diego, La Jolla, CA, United States
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, United States
| | - Sally L. Baxter
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, United States
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, United States
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Lee CS. American Academy of Ophthalmology Intelligent Research in Sight (IRIS®) Registry and the IRIS Registry Analytic Center Consortium. OPHTHALMOLOGY SCIENCE 2022; 2:100112. [PMID: 36246182 PMCID: PMC9560568 DOI: 10.1016/j.xops.2022.100112] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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