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Wu H, Jin K, Yip CC, Koh V, Ye J. A systematic review of economic evaluation of artificial intelligence-based screening for eye diseases: From possibility to reality. Surv Ophthalmol 2024; 69:499-507. [PMID: 38492584 DOI: 10.1016/j.survophthal.2024.03.008] [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: 08/29/2023] [Revised: 03/04/2024] [Accepted: 03/12/2024] [Indexed: 03/18/2024]
Abstract
Artificial Intelligence (AI) has become a focus of research in the rapidly evolving field of ophthalmology. Nevertheless, there is a lack of systematic studies on the health economics of AI in this field. We examine studies from the PubMed, Google Scholar, and Web of Science databases that employed quantitative analysis, retrieved up to July 2023. Most of the studies indicate that AI leads to cost savings and improved efficiency in ophthalmology. On the other hand, some studies suggest that using AI in healthcare may raise costs for patients, especially when taking into account factors such as labor costs, infrastructure, and patient adherence. Future research should cover a wider range of ophthalmic diseases beyond common eye conditions. Moreover, conducting extensive health economic research, designed to collect data relevant to its own context, is imperative.
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Affiliation(s)
- Hongkang Wu
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kai Jin
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Chee Chew Yip
- Department of Ophthalmology & Visual Sciences, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Victor Koh
- Department of Ophthalmology, National University Hospital, National University of Singapore, Singapore
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
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2
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Ranjbari D, Abbasgholizadeh Rahimi S. Implications of conscious AI in primary healthcare. Fam Med Community Health 2024; 12:e002625. [PMID: 38485268 PMCID: PMC10941173 DOI: 10.1136/fmch-2023-002625] [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: 10/31/2023] [Accepted: 02/29/2024] [Indexed: 03/17/2024] Open
Abstract
The conversation about consciousness of artificial intelligence (AI) is an ongoing topic since 1950s. Despite the numerous applications of AI identified in healthcare and primary healthcare, little is known about how a conscious AI would reshape its use in this domain. While there is a wide range of ideas as to whether AI can or cannot possess consciousness, a prevailing theme in all arguments is uncertainty. Given this uncertainty and the high stakes associated with the use of AI in primary healthcare, it is imperative to be prepared for all scenarios including conscious AI systems being used for medical diagnosis, shared decision-making and resource management in the future. This commentary serves as an overview of some of the pertinent evidence supporting the use of AI in primary healthcare and proposes ideas as to how consciousnesses of AI can support or further complicate these applications. Given the scarcity of evidence on the association between consciousness of AI and its current state of use in primary healthcare, our commentary identifies some directions for future research in this area including assessing patients', healthcare workers' and policy-makers' attitudes towards consciousness of AI systems in primary healthcare settings.
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Affiliation(s)
- Dorsai Ranjbari
- McGill University Faculty of Medicine and Health Sciences, Montreal, Quebec, Canada
| | - Samira Abbasgholizadeh Rahimi
- Family Medicine, Faculty of Medicine and Health Sciences and Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, Canada
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
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3
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Posner KM, Bakus C, Basralian G, Chester G, Zeiman M, O'Malley GR, Klein GR. Evaluating ChatGPT's Capabilities on Orthopedic Training Examinations: An Analysis of New Image Processing Features. Cureus 2024; 16:e55945. [PMID: 38601421 PMCID: PMC11005479 DOI: 10.7759/cureus.55945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/11/2024] [Indexed: 04/12/2024] Open
Abstract
Introduction The efficacy of integrating artificial intelligence (AI) models like ChatGPT into the medical field, specifically orthopedic surgery, has yet to be fully determined. The most recent adaptation of ChatGPT that has yet to be explored is its image analysis capabilities. This study assesses ChatGPT's performance in answering Orthopedic In-Training Examination (OITE) questions, including those that require image analysis. Methods Questions from the 2014, 2015, 2021, and 2022 AAOS OITE were screened for inclusion. All questions without images were entered into ChatGPT 3.5 and 4.0 twice. Questions that necessitated the use of images were only entered into ChatGPT 4.0 twice, as this is the only version of the system that can analyze images. The responses were recorded and compared to AAOS's correct answers, evaluating the AI's accuracy and precision. Results A total of 940 questions were included in the final analysis (457 questions with images and 483 questions without images). ChatGPT 4.0 performed significantly better on questions that did not require image analysis (67.81% vs 47.59%, p<0.001). Discussion While the use of AI in orthopedics is an intriguing possibility, this evaluation demonstrates how, even with the addition of image processing capabilities, ChatGPT still falls short in terms of its accuracy. As AI technology evolves, ongoing research is vital to harness AI's potential effectively, ensuring it complements rather than attempts to replace the nuanced skills of orthopedic surgeons.
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Affiliation(s)
- Kevin M Posner
- Department of Orthopedic Surgery, Hackensack Meridian School of Medicine, Nutley, USA
| | - Cassandra Bakus
- Department of Orthopedic Surgery, Hackensack Meridian School of Medicine, Nutley, USA
| | - Grace Basralian
- Department of Orthopedic Surgery, Hackensack Meridian School of Medicine, Nutley, USA
| | - Grace Chester
- Department of Orthopedic Surgery, Hackensack Meridian School of Medicine, Nutley, USA
| | - Mallery Zeiman
- Department of Orthopedic Surgery, Hackensack Meridian School of Medicine, Nutley, USA
| | - Geoffrey R O'Malley
- Department of Orthopedic Surgery, Hackensack University Medical Center, Hackensack, USA
| | - Gregg R Klein
- Department of Orthopedic Surgery, Hackensack University Medical Center, Hackensack, USA
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Rajeswaren V, Lu V, Chen H, Patnaik JL, Manoharan N. Healthcare Resource Utilization and Costs in an At-Risk Population With Diabetic Retinopathy. Transl Vis Sci Technol 2024; 13:12. [PMID: 38359018 PMCID: PMC10876016 DOI: 10.1167/tvst.13.2.12] [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: 05/12/2023] [Accepted: 01/10/2024] [Indexed: 02/17/2024] Open
Abstract
Purpose Several investigators have suggested the cost-effectiveness of earlier screening, management of risk factors, and early treatment for diabetic retinopathy (DR). We aimed to evaluate the extent of health care utilization and cost of delayed care by insurance type in a vulnerable patient population. Methods A retrospective analysis of patients with DR was conducted using electronic medical record (EMR) data from January 2014 to December 2020 at Denver Health Medical Center, a safety net institution. Patients were classified by disease severity and insurance status. DR-specific costs were assessed via Current Procedural Terminology (CPT) codes over a 24-month follow-up period. Results Among the 313 patients, a higher proportion of non-English speaking patients were uninsured. Rates of proliferative DR at presentation differed across insurance groups (62% of uninsured, 42% of discount plan, and 33% of Medicare/Medicaid, P = 0.016). There was a significant difference in the total median cost between discount plan patients ($1258, interquartile range [IQR] = $0 - $5901) and both Medicare patients ($751, IQR = $0, $7148, P = 0.037) and Medicaid patients ($593, IQR = $0 - $6299, P = 0.025). Conclusions There were higher rates of proliferative DR at presentation among the uninsured and discount plan patients and greater total median cost in discount plan patients compared to Medicare or Medicaid. These findings prioritize mitigating gaps in insurance coverage and barriers to preventative care among vulnerable populations. Translational Relevance Advanced diabetic disease and increased downstream health care utilization and cost vary across insurance type, suggesting improved access to preventative care is needed in these specific at-risk populations.
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Affiliation(s)
- Vivian Rajeswaren
- Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Vivian Lu
- Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Hongan Chen
- Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Jennifer L. Patnaik
- Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Niranjan Manoharan
- Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA
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5
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Hark LA, Lin WV, Hirji S, Gorroochurn P, Horowitz JD, Diamond DF, Park L, Wang Q, Auran JD, Maruri SC, Henriquez DR, Sharma T, Valenzuela I, Liebmann JM, Cioffi GA, Friedman DS, Harizman N. Manhattan Vision Screening and Follow-Up Study (NYC-SIGHT): Subanalysis of Referral to Ophthalmology. Curr Eye Res 2024; 49:197-206. [PMID: 37812506 DOI: 10.1080/02713683.2023.2269614] [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: 03/24/2023] [Accepted: 10/08/2023] [Indexed: 10/11/2023]
Abstract
PURPOSE The Manhattan Vision Screening and Follow-up Study aims to provide access to eye care for underserved populations, detect native rates of ocular pathology, and refer participants with eye disease to ophthalmology. This subanalysis describes the reasons for referral to ophthalmology and identifies risk factors associated with being referred. METHODS Enrolled participants were aged ≥40 years, living independently in public housing developments and able to provide consent for eye health screenings. Those with habitual visual acuity 20/40 or worse, intraocular pressure (IOP) 23-29 mmHg, or an unreadable fundus image failed and were scheduled with the on-site optometrist. The optometric exam determined whether further referral to ophthalmology for a clinic exam was warranted. Those with an abnormal image or IOP ≥30 mmHg were referred directly to ophthalmology. Main outcome was factors associated with referral to ophthalmology. RESULTS A total of 708 individuals completed the eye health screening over 15 months. A total of 468 participants were referred to ophthalmology (250 had an abnormal image and 218 were referred by the optometrist). Those referred were predominantly older adults (mean age 70.0 ± 11.4 years), female (66.7%), African American (55.1%) and Hispanic (39.5%). Seventy percent of participants had not had a recent eye exam. Stepwise multivariate logistic regression analysis showed that participants with pre-existing glaucoma (OR 3.14, 95% CI 1.62 to 6.08, p = 0.001), an IOP ≥23 mmHg (OR 5.04, 95% 1.91 to 13.28, p = 0.001), or vision impairment (mild) (OR 2.51, 95% CI 1.68 to 3.77, p = 0.001) had significantly higher odds of being referred to ophthalmology. CONCLUSION This targeted community-based study in Upper Manhattan provided access to eye care and detected a significant amount of ocular pathology requiring referral to ophthalmology in this high-risk population.
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Affiliation(s)
- Lisa A Hark
- Department of Ophthalmology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Weijie Violet Lin
- Department of Ophthalmology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Sitara Hirji
- Department of Ophthalmology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Prakash Gorroochurn
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Jason D Horowitz
- Department of Ophthalmology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Daniel F Diamond
- Department of Ophthalmology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Lisa Park
- Department of Ophthalmology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Qing Wang
- Department of Ophthalmology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - James D Auran
- Department of Ophthalmology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Stefania C Maruri
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Desiree R Henriquez
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Tarun Sharma
- Department of Ophthalmology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Ives Valenzuela
- Department of Ophthalmology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Jeffrey M Liebmann
- Department of Ophthalmology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - George A Cioffi
- Department of Ophthalmology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - David S Friedman
- Harvard Medical School, Massachusetts Eye and Ear Infirmary, Glaucoma Service, Boston, MA, USA
| | - Noga Harizman
- Department of Ophthalmology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA
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Zrubka Z, Kertész G, Gulácsi L, Czere J, Hölgyesi Á, Nezhad HM, Mosavi A, Kovács L, Butte AJ, Péntek M. The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review. J Med Internet Res 2024; 26:e47430. [PMID: 38241075 PMCID: PMC10837761 DOI: 10.2196/47430] [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: 03/20/2023] [Revised: 04/29/2023] [Accepted: 11/17/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Diabetes mellitus (DM) is a major health concern among children with the widespread adoption of advanced technologies. However, concerns are growing about the transparency, replicability, biasedness, and overall validity of artificial intelligence studies in medicine. OBJECTIVE We aimed to systematically review the reporting quality of machine learning (ML) studies of pediatric DM using the Minimum Information About Clinical Artificial Intelligence Modelling (MI-CLAIM) checklist, a general reporting guideline for medical artificial intelligence studies. METHODS We searched the PubMed and Web of Science databases from 2016 to 2020. Studies were included if the use of ML was reported in children with DM aged 2 to 18 years, including studies on complications, screening studies, and in silico samples. In studies following the ML workflow of training, validation, and testing of results, reporting quality was assessed via MI-CLAIM by consensus judgments of independent reviewer pairs. Positive answers to the 17 binary items regarding sufficient reporting were qualitatively summarized and counted as a proxy measure of reporting quality. The synthesis of results included testing the association of reporting quality with publication and data type, participants (human or in silico), research goals, level of code sharing, and the scientific field of publication (medical or engineering), as well as with expert judgments of clinical impact and reproducibility. RESULTS After screening 1043 records, 28 studies were included. The sample size of the training cohort ranged from 5 to 561. Six studies featured only in silico patients. The reporting quality was low, with great variation among the 21 studies assessed using MI-CLAIM. The number of items with sufficient reporting ranged from 4 to 12 (mean 7.43, SD 2.62). The items on research questions and data characterization were reported adequately most often, whereas items on patient characteristics and model examination were reported adequately least often. The representativeness of the training and test cohorts to real-world settings and the adequacy of model performance evaluation were the most difficult to judge. Reporting quality improved over time (r=0.50; P=.02); it was higher than average in prognostic biomarker and risk factor studies (P=.04) and lower in noninvasive hypoglycemia detection studies (P=.006), higher in studies published in medical versus engineering journals (P=.004), and higher in studies sharing any code of the ML pipeline versus not sharing (P=.003). The association between expert judgments and MI-CLAIM ratings was not significant. CONCLUSIONS The reporting quality of ML studies in the pediatric population with DM was generally low. Important details for clinicians, such as patient characteristics; comparison with the state-of-the-art solution; and model examination for valid, unbiased, and robust results, were often the weak points of reporting. To assess their clinical utility, the reporting standards of ML studies must evolve, and algorithms for this challenging population must become more transparent and replicable.
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Affiliation(s)
- Zsombor Zrubka
- HECON Health Economics Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary
| | - Gábor Kertész
- John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary
| | - László Gulácsi
- HECON Health Economics Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary
| | - János Czere
- Doctoral School of Innovation Management, Óbuda University, Budapest, Hungary
| | - Áron Hölgyesi
- HECON Health Economics Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary
- Doctoral School of Molecular Medicine, Semmelweis University, Budapest, Hungary
| | - Hossein Motahari Nezhad
- HECON Health Economics Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary
- Doctoral School of Business and Management, Corvinus University of Budapest, Budapest, Hungary
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary
| | - Levente Kovács
- Physiological Controls Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, United States
| | - Márta Péntek
- HECON Health Economics Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary
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7
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Wolf RM, Channa R, Liu TYA, Zehra A, Bromberger L, Patel D, Ananthakrishnan A, Brown EA, Prichett L, Lehmann HP, Abramoff MD. Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial. Nat Commun 2024; 15:421. [PMID: 38212308 PMCID: PMC10784572 DOI: 10.1038/s41467-023-44676-z] [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: 06/16/2023] [Accepted: 12/21/2023] [Indexed: 01/13/2024] Open
Abstract
Diabetic retinopathy can be prevented with screening and early detection. We hypothesized that autonomous artificial intelligence (AI) diabetic eye exams at the point-of-care would increase diabetic eye exam completion rates in a racially and ethnically diverse youth population. AI for Children's diabetiC Eye ExamS (NCT05131451) is a parallel randomized controlled trial that randomized youth (ages 8-21 years) with type 1 and type 2 diabetes to intervention (autonomous artificial intelligence diabetic eye exam at the point of care), or control (scripted eye care provider referral and education) in an academic pediatric diabetes center. The primary outcome was diabetic eye exam completion rate within 6 months. The secondary outcome was the proportion of participants who completed follow-through with an eye care provider if deemed appropriate. Diabetic eye exam completion rate was significantly higher (100%, 95%CI: 95.5%, 100%) in the intervention group (n = 81) than the control group (n = 83) (22%, 95%CI: 14.2%, 32.4%)(p < 0.001). In the intervention arm, 25/81 participants had an abnormal result, of whom 64% (16/25) completed follow-through with an eye care provider, compared to 22% in the control arm (p < 0.001). Autonomous AI increases diabetic eye exam completion rates in youth with diabetes.
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Affiliation(s)
- Risa M Wolf
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA
| | - T Y Alvin Liu
- Wilmer Eye Institute at the Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Anum Zehra
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Lee Bromberger
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Dhruva Patel
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Elizabeth A Brown
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Laura Prichett
- Johns Hopkins School of Medicine Biostatistics, Epidemiology and Data Management (BEAD) Core, Baltimore, MD, USA
| | - Harold P Lehmann
- Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA
| | - Michael D Abramoff
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA
- Digital Diagnostics Inc, Coralville, IA, USA
- Iowa City VA Medical Center, Iowa City, IA, USA
- Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA
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Trotta MC, Gesualdo C, Russo M, Lepre CC, Petrillo F, Vastarella MG, Nicoletti M, Simonelli F, Hermenean A, D’Amico M, Rossi S. Changes in Circulating Acylated Ghrelin and Neutrophil Elastase in Diabetic Retinopathy. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:118. [PMID: 38256379 PMCID: PMC10820226 DOI: 10.3390/medicina60010118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 01/24/2024]
Abstract
Background and Objectives: The role and the levels of ghrelin in diabetes-induced retinal damage have not yet been explored. The present study aimed to measure the serum levels of total ghrelin (TG), and its acylated (AG) and des-acylated (DAG) forms in patients with the two stages of diabetic retinopathy (DR), non-proliferative (NPDR) and proliferative (PDR). Moreover, the correlation between serum ghrelin and neutrophil elastase (NE) levels was investigated. Materials and Methods: The serum markers were determined via enzyme-linked immunosorbent assays in 12 non-diabetic subjects (CTRL), 15 diabetic patients without DR (Diabetic), 15 patients with NPDR, and 15 patients with PDR. Results: TG and AG serum levels were significantly decreased in Diabetic (respectively, p < 0.05 and p < 0.01 vs. CTRL), NPDR (p < 0.01 vs. Diabetic), and in PDR patients (p < 0.01 vs. NPDR). AG serum levels were inversely associated with DR abnormalities (microhemorrhages, microaneurysms, and exudates) progression (r = -0.83, p < 0.01), serum neutrophil percentage (r = -0.74, p < 0.01), and serum NE levels (r = -0.73, p < 0.01). The latter were significantly increased in the Diabetic (p < 0.05 vs. CTRL), NPDR (p < 0.01 vs. Diabetic), and PDR (p < 0.01 vs. PDR) groups. Conclusions: The two DR stages were characterized by decreased AG and increased NE levels. In particular, serum AG levels were lower in PDR compared to NPDR patients, and serum NE levels were higher in the PDR vs. the NPDR group. Together with the greater presence of retinal abnormalities, this could underline a distinctive role of AG in PDR compared to NPDR.
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Affiliation(s)
- Maria Consiglia Trotta
- Department of Experimental Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (M.C.T.); (C.C.L.); (F.P.); (M.D.)
| | - Carlo Gesualdo
- Multidisciplinary Department of Medical, Surgical and Dental Sciences, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (C.G.); (M.N.); (F.S.)
| | - Marina Russo
- PhD Course in National Interest in Public Administration and Innovation for Disability and Social Inclusion, Department of Mental, Physical Health and Preventive Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy;
- School of Pharmacology and Clinical Toxicology, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Caterina Claudia Lepre
- Department of Experimental Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (M.C.T.); (C.C.L.); (F.P.); (M.D.)
- PhD Course in Translational Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy;
| | - Francesco Petrillo
- Department of Experimental Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (M.C.T.); (C.C.L.); (F.P.); (M.D.)
- PhD Course in Translational Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy;
| | - Maria Giovanna Vastarella
- PhD Course in Translational Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy;
| | - Maddalena Nicoletti
- Multidisciplinary Department of Medical, Surgical and Dental Sciences, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (C.G.); (M.N.); (F.S.)
| | - Francesca Simonelli
- Multidisciplinary Department of Medical, Surgical and Dental Sciences, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (C.G.); (M.N.); (F.S.)
| | - Anca Hermenean
- “Aurel Ardelean” Institute of Life Sciences, Vasile Goldis Western University of Arad, 310144 Arad, Romania;
| | - Michele D’Amico
- Department of Experimental Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (M.C.T.); (C.C.L.); (F.P.); (M.D.)
| | - Settimio Rossi
- Multidisciplinary Department of Medical, Surgical and Dental Sciences, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (C.G.); (M.N.); (F.S.)
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Hu W, Joseph S, Li R, Woods E, Sun J, Shen M, Jan CL, Zhu Z, He M, Zhang L. Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: a cost effectiveness analysis. EClinicalMedicine 2024; 67:102387. [PMID: 38314061 PMCID: PMC10837545 DOI: 10.1016/j.eclinm.2023.102387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/29/2023] [Accepted: 12/05/2023] [Indexed: 02/06/2024] Open
Abstract
Background We aimed to evaluate the cost-effectiveness of an artificial intelligence-(AI) based diabetic retinopathy (DR) screening system in the primary care setting for both non-Indigenous and Indigenous people living with diabetes in Australia. Methods We performed a cost-effectiveness analysis between January 01, 2022 and August 01, 2023. A decision-analytic Markov model was constructed to simulate DR progression in a population of 1,197,818 non-Indigenous and 65,160 Indigenous Australians living with diabetes aged ≥20 years over 40 years. From a healthcare provider's perspective, we compared current practice to three primary care AI-based screening scenarios-(A) substitution of current manual grading, (B) scaling up to patient acceptance level, and (C) achieving universal screening. Study results were presented as incremental cost-effectiveness ratio (ICER), benefit-cost ratio (BCR), and net monetary benefits (NMB). A Willingness-to-pay (WTP) threshold of AU$50,000 per quality-adjusted life year (QALY) and a discount rate of 3.5% were adopted in this study. Findings With the status quo, the non-Indigenous diabetic population was projected to develop 96,269 blindness cases, resulting in AU$13,039.6 m spending on DR screening and treatment during 2020-2060. In comparison, all three intervention scenarios were effective and cost-saving. In particular, if a universal screening program was to be implemented (Scenario C), it would prevent 38,347 blindness cases, gain 172,090 QALYs and save AU$595.8 m, leading to a BCR of 3.96 and NMB of AU$9,200 m. Similar findings were also reported in the Indigenous population. With the status quo, 3,396 Indigenous individuals would develop blindness, which would cost the health system AU$796.0 m during 2020-2060. All three intervention scenarios were cost-saving for the Indigenous population. Notably, universal AI-based DR screening (Scenario C) would prevent 1,211 blindness cases and gain 9,800 QALYs in the Indigenous population, leading to a saving of AU$19.2 m with a BCR of 1.62 and NMB of AU$509 m. Interpretation Our findings suggest that implementing AI-based DR screening in primary care is highly effective and cost-saving in both Indigenous and non-Indigenous populations. Funding This project received grant funding from the Australian Government: the National Critical Research Infrastructure Initiative, Medical Research Future Fund (MRFAI00035) and the NHMRC Investigator Grant (APP1175405). The contents of the published material are solely the responsibility of the Administering Institution, a participating institution or individual authors and do not reflect the views of the NHMRC. This work was supported by the Global STEM Professorship Scheme (P0046113), the Fundamental Research Funds of the State Key Laboratory of Ophthalmology, Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou, China (Z012014075). The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. W.H. is supported by the Melbourne Research Scholarship established by the University of Melbourne. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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Affiliation(s)
- Wenyi Hu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Sanil Joseph
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Rui Li
- Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, 710061, PR China
| | - Ekaterina Woods
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Jason Sun
- Eyetelligence Pty Ltd., Melbourne, Australia
| | - Mingwang Shen
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, 710061, PR China
| | - Catherine Lingxue Jan
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Lei Zhang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Clinical Medical Research Center, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210008, China
- Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia
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10
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Chen JS, Lin MC, Yiu G, Thorne C, Kulasa K, Stewart J, Nudleman E, Freeby M, Han MA, Baxter SL. Barriers to Implementation of Teleretinal Diabetic Retinopathy Screening Programs Across the University of California. Telemed J E Health 2023; 29:1810-1818. [PMID: 37256712 PMCID: PMC10714257 DOI: 10.1089/tmj.2022.0489] [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: 11/17/2022] [Revised: 12/17/2022] [Accepted: 12/19/2022] [Indexed: 06/02/2023] Open
Abstract
Aim: To describe barriers to implementation of diabetic retinopathy (DR) teleretinal screening programs and artificial intelligence (AI) integration at the University of California (UC). Methods: Institutional representatives from UC Los Angeles, San Diego, San Francisco, Irvine, and Davis were surveyed for the year of their program's initiation, active status at the time of survey (December 2021), number of primary care clinics involved, screening image quality, types of eye providers, image interpretation turnaround time, and billing codes used. Representatives were asked to rate perceptions toward barriers to teleretinal DR screening and AI implementation using a 5-point Likert scale. Results: Four UC campuses had active DR teleretinal screening programs at the time of survey and screened between 246 and 2,123 patients at 1-6 clinics per campus. Sites reported variation between poor-quality photos (<5% to 15%) and average image interpretation time (1-5 days). Patient education, resource availability, and infrastructural support were identified as barriers to DR teleretinal screening. Cost and integration into existing technology infrastructures were identified as barriers to AI integration in DR screening. Conclusions: Despite the potential to increase access to care, there remain several barriers to widespread implementation of DR teleretinal screening. More research is needed to develop best practices to overcome these barriers.
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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, USA
| | - Mark C. Lin
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, USA
| | - Glenn Yiu
- Department of Ophthalmology and Vision Science, University of California Davis Health, Sacramento, California, USA
| | - Christine Thorne
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California, USA
| | - Kristen Kulasa
- Department of Endocrinology, University of California San Diego, La Jolla, California, USA
| | - Jay Stewart
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California, USA
- Department of Ophthalmology, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, USA
| | - Eric Nudleman
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, USA
| | - Matthew Freeby
- Department of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Maria A. Han
- Department of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - 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, USA
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA
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11
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Dow ER, Khan NC, Chen KM, Mishra K, Perera C, Narala R, Basina M, Dang J, Kim M, Levine M, Phadke A, Tan M, Weng K, Do DV, Moshfeghi DM, Mahajan VB, Mruthyunjaya P, Leng T, Myung D. AI-Human Hybrid Workflow Enhances Teleophthalmology for the Detection of Diabetic Retinopathy. OPHTHALMOLOGY SCIENCE 2023; 3:100330. [PMID: 37449051 PMCID: PMC10336195 DOI: 10.1016/j.xops.2023.100330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 05/04/2023] [Accepted: 05/08/2023] [Indexed: 07/18/2023]
Abstract
Objective Detection of diabetic retinopathy (DR) outside of specialized eye care settings is an important means of access to vision-preserving health maintenance. Remote interpretation of fundus photographs acquired in a primary care or other nonophthalmic setting in a store-and-forward manner is a predominant paradigm of teleophthalmology screening programs. Artificial intelligence (AI)-based image interpretation offers an alternative means of DR detection. IDx-DR (Digital Diagnostics Inc) is a Food and Drug Administration-authorized autonomous testing device for DR. We evaluated the diagnostic performance of IDx-DR compared with human-based teleophthalmology over 2 and a half years. Additionally, we evaluated an AI-human hybrid workflow that combines AI-system evaluation with human expert-based assessment for referable cases. Design Prospective cohort study and retrospective analysis. Participants Diabetic patients ≥ 18 years old without a prior DR diagnosis or DR examination in the past year presenting for routine DR screening in a primary care clinic. Methods Macula-centered and optic nerve-centered fundus photographs were evaluated by an AI algorithm followed by consensus-based overreading by retina specialists at the Stanford Ophthalmic Reading Center. Detection of more-than-mild diabetic retinopathy (MTMDR) was compared with in-person examination by a retina specialist. Main Outcome Measures Sensitivity, specificity, accuracy, positive predictive value, and gradability achieved by the AI algorithm and retina specialists. Results The AI algorithm had higher sensitivity (95.5% sensitivity; 95% confidence interval [CI], 86.7%-100%) but lower specificity (60.3% specificity; 95% CI, 47.7%-72.9%) for detection of MTMDR compared with remote image interpretation by retina specialists (69.5% sensitivity; 95% CI, 50.7%-88.3%; 96.9% specificity; 95% CI, 93.5%-100%). Gradability of encounters was also lower for the AI algorithm (62.5%) compared with retina specialists (93.1%). A 2-step AI-human hybrid workflow in which the AI algorithm initially rendered an assessment followed by overread by a retina specialist of MTMDR-positive encounters resulted in a sensitivity of 95.5% (95% CI, 86.7%-100%) and a specificity of 98.2% (95% CI, 94.6%-100%). Similarly, a 2-step overread by retina specialists of AI-ungradable encounters improved gradability from 63.5% to 95.6% of encounters. Conclusions Implementation of an AI-human hybrid teleophthalmology workflow may both decrease reliance on human specialist effort and improve diagnostic accuracy. Financial Disclosures Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
- Eliot R. Dow
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
- Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Nergis C. Khan
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Karen M. Chen
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Kapil Mishra
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Chandrashan Perera
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Ramsudha Narala
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Marina Basina
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Jimmy Dang
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Michael Kim
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Marcie Levine
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Anuradha Phadke
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Marilyn Tan
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Kirsti Weng
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Diana V. Do
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Darius M. Moshfeghi
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Vinit B. Mahajan
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
- Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Prithvi Mruthyunjaya
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Theodore Leng
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - David Myung
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
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12
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Curran K, Whitestone N, Zabeen B, Ahmed M, Husain L, Alauddin M, Hossain MA, Patnaik JL, Lanoutee G, Cherwek DH, Congdon N, Peto T, Jaccard N. CHILDSTAR: CHIldren Living With Diabetes See and Thrive with AI Review. Clin Med Insights Endocrinol Diabetes 2023; 16:11795514231203867. [PMID: 37822362 PMCID: PMC10563496 DOI: 10.1177/11795514231203867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 08/23/2023] [Indexed: 10/13/2023] Open
Abstract
Background Artificial intelligence (AI) appears capable of detecting diabetic retinopathy (DR) with a high degree of accuracy in adults; however, there are few studies in children and young adults. Methods Children and young adults (3-26 years) with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) were screened at the Dhaka BIRDEM-2 hospital, Bangladesh. All gradable fundus images were uploaded to Cybersight AI for interpretation. Two main outcomes were considered at a patient level: 1) Any DR, defined as mild non-proliferative diabetic retinopathy (NPDR or more severe; and 2) Referable DR, defined as moderate NPDR or more severe. Diagnostic test performance comparing Orbis International's Cybersight AI with the reference standard, a fully qualified optometrist certified in DR grading, was assessed using the Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), sensitivity, specificity, positive and negative predictive values. Results Among 1274 participants (53.1% female, mean age 16.7 years), 19.4% (n = 247) had any DR according to AI. For referable DR, 2.35% (n = 30) were detected by AI. The sensitivity and specificity of AI for any DR were 75.5% (CI 69.7-81.3%) and 91.8% (CI 90.2-93.5%) respectively, and for referable DR, these values were 84.2% (CI 67.8-100%) and 98.9% (CI 98.3%-99.5%). The MCC, AUC-ROC and the AUC-PR for referable DR were 63.4, 91.2 and 76.2% respectively. AI was most successful in accurately classifying younger children with shorter duration of diabetes. Conclusions Cybersight AI accurately detected any DR and referable DR among children and young adults, despite its algorithms having been trained on adults. The observed high specificity is particularly important to avoid over-referral in low-resource settings. AI may be an effective tool to reduce demands on scarce physician resources for the care of children with diabetes in low-resource settings.
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Affiliation(s)
- Katie Curran
- Centre for Public Health, Queens University Belfast, Belfast, UK
| | | | - Bedowra Zabeen
- Department of Paediatrics, Life for a Child & Changing Diabetes in Children Programme, Bangladesh Institute of Research & Rehabilitation in Diabetes, Endocrine & Metabolic Disorders (BIRDEM), Diabetic Association of Bangladesh, Dhaka, Bangladesh
| | | | | | | | | | - Jennifer L Patnaik
- Orbis International, New York, NY, USA
- Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA
| | | | | | - Nathan Congdon
- Centre for Public Health, Queens University Belfast, Belfast, UK
- Orbis International, New York, NY, USA
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Tunde Peto
- Centre for Public Health, Queens University Belfast, Belfast, UK
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13
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Vaughan N. Review of smartphone funduscopy for diabetic retinopathy screening. Surv Ophthalmol 2023:S0039-6257(23)00132-7. [PMID: 37806567 DOI: 10.1016/j.survophthal.2023.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 09/23/2023] [Accepted: 10/03/2023] [Indexed: 10/10/2023]
Abstract
I detail advances in funduscopy diagnostic systems integrating smartphones. Smartphone funduscopy devices are comprised of lens devices connecting with smartphones and software applications to be used for mobile retinal image capturing and diagnosis of diabetic retinopathy. This is particularly beneficial to automate and mobilize retinopathy screening techniques and methods in remote and rural areas as those diabetes patients are often not meeting the required regular screening for diabetic retinopathy. Smartphone retinal image grading systems enable retinopathy to be screened remotely as teleophthalmology or as a stand-alone point-of-care-testing system. Smartphone funduscopy aims to avoid the need for patients to be seen by expert ophthalmologists, which can reduce patient travel, time taken for images to be processed, appointment backlog, health service overhead costs, and the workload burden for expert ophthalmologists.
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Affiliation(s)
- Neil Vaughan
- Exeter Centre of Excellence for Diabetes (ExCEeD), University of Exeter, Exeter, UK; Faculty of Health and Life Sciences (HLS), University of Exeter, Exeter, UK; Royal Academy of Engineering (RAEng), London, UK; NIHR Exeter Biomedical Research Centre, Exeter, UK.
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14
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Rajesh AE, Davidson OQ, Lee CS, Lee AY. Artificial Intelligence and Diabetic Retinopathy: AI Framework, Prospective Studies, Head-to-head Validation, and Cost-effectiveness. Diabetes Care 2023; 46:1728-1739. [PMID: 37729502 PMCID: PMC10516248 DOI: 10.2337/dci23-0032] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/15/2023] [Indexed: 09/22/2023]
Abstract
Current guidelines recommend that individuals with diabetes receive yearly eye exams for detection of referable diabetic retinopathy (DR), one of the leading causes of new-onset blindness. For addressing the immense screening burden, artificial intelligence (AI) algorithms have been developed to autonomously screen for DR from fundus photography without human input. Over the last 10 years, many AI algorithms have achieved good sensitivity and specificity (>85%) for detection of referable DR compared with human graders; however, many questions still remain. In this narrative review on AI in DR screening, we discuss key concepts in AI algorithm development as a background for understanding the algorithms. We present the AI algorithms that have been prospectively validated against human graders and demonstrate the variability of reference standards and cohort demographics. We review the limited head-to-head validation studies where investigators attempt to directly compare the available algorithms. Next, we discuss the literature regarding cost-effectiveness, equity and bias, and medicolegal considerations, all of which play a role in the implementation of these AI algorithms in clinical practice. Lastly, we highlight ongoing efforts to bridge gaps in AI model data sets to pursue equitable development and delivery.
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Affiliation(s)
- Anand E. Rajesh
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
| | - Oliver Q. Davidson
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
| | - Cecilia S. Lee
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
| | - Aaron Y. Lee
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
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15
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Wolf RM, Channa R, Lehmann HP, Abramoff MD, Liu TA. Clinical Implementation of Autonomous Artificial Intelligence Systems for Diabetic Eye Exams: Considerations for Success. Clin Diabetes 2023; 42:142-149. [PMID: 38230333 PMCID: PMC10788651 DOI: 10.2337/cd23-0019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Affiliation(s)
- Risa M. Wolf
- Department of Pediatric Endocrinology and Diabetes, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI
| | - Harold P. Lehmann
- Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD
| | - Michael D. Abramoff
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA
- Digital Diagnostics, Coralville, IA
| | - T.Y. Alvin Liu
- Wilmer Eye Institute at the Johns Hopkins University School of Medicine, Baltimore, MD
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16
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Abràmoff MD, Tarver ME, Loyo-Berrios N, Trujillo S, Char D, Obermeyer Z, Eydelman MB, Maisel WH. Considerations for addressing bias in artificial intelligence for health equity. NPJ Digit Med 2023; 6:170. [PMID: 37700029 PMCID: PMC10497548 DOI: 10.1038/s41746-023-00913-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 08/21/2023] [Indexed: 09/14/2023] Open
Abstract
Health equity is a primary goal of healthcare stakeholders: patients and their advocacy groups, clinicians, other providers and their professional societies, bioethicists, payors and value based care organizations, regulatory agencies, legislators, and creators of artificial intelligence/machine learning (AI/ML)-enabled medical devices. Lack of equitable access to diagnosis and treatment may be improved through new digital health technologies, especially AI/ML, but these may also exacerbate disparities, depending on how bias is addressed. We propose an expanded Total Product Lifecycle (TPLC) framework for healthcare AI/ML, describing the sources and impacts of undesirable bias in AI/ML systems in each phase, how these can be analyzed using appropriate metrics, and how they can be potentially mitigated. The goal of these "Considerations" is to educate stakeholders on how potential AI/ML bias may impact healthcare outcomes and how to identify and mitigate inequities; to initiate a discussion between stakeholders on these issues, in order to ensure health equity along the expanded AI/ML TPLC framework, and ultimately, better health outcomes for all.
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Affiliation(s)
- Michael D Abràmoff
- Departments of Ophthalmology and Visual Sciences, and Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA.
| | - Michelle E Tarver
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Nilsa Loyo-Berrios
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | | | - Danton Char
- Center for Biomedical Ethics, Stanford University School of Medicine, San Francisco, CA, USA
- Department of Anesthesiology, Stanford University School of Medicine, Division of Pediatric Cardiac Anesthesia, San Francisco, CA, USA
| | - Ziad Obermeyer
- School of Public Health, University of California, Berkeley, CA, USA
| | - Malvina B Eydelman
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - William H Maisel
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
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17
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Ruamviboonsuk P, Ruamviboonsuk V, Tiwari R. Recent evidence of economic evaluation of artificial intelligence in ophthalmology. Curr Opin Ophthalmol 2023; 34:449-458. [PMID: 37459289 DOI: 10.1097/icu.0000000000000987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
PURPOSE OF REVIEW Health economic evaluation (HEE) is essential for assessing value of health interventions, including artificial intelligence. Recent approaches, current challenges, and future directions of HEE of artificial intelligence in ophthalmology are reviewed. RECENT FINDINGS Majority of recent HEEs of artificial intelligence in ophthalmology were for diabetic retinopathy screening. Two models, one conducted in the rural USA (5-year period) and another in China (35-year period), found artificial intelligence to be more cost-effective than without screening for diabetic retinopathy. Two additional models, which compared artificial intelligence with human screeners in Brazil and Thailand for the lifetime of patients, found artificial intelligence to be more expensive from a healthcare system perspective. In the Thailand analysis, however, artificial intelligence was less expensive when opportunity loss from blindness was included. An artificial intelligence model for screening retinopathy of prematurity was cost-effective in the USA. A model for screening age-related macular degeneration in Japan and another for primary angle close in China did not find artificial intelligence to be cost-effective, compared with no screening. The costs of artificial intelligence varied widely in these models. SUMMARY Like other medical fields, there is limited evidence in assessing the value of artificial intelligence in ophthalmology and more appropriate HEE models are needed.
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Affiliation(s)
- Paisan Ruamviboonsuk
- Department of Ophthalmology, Rajavithi Hospital, College of Medicine, Rangsit University
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18
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Zhu W, Qiu P, Dumitrascu OM, Sobczak JM, Farazi M, Yang Z, Nandakumar K, Wang Y. OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing. INFORMATION PROCESSING IN MEDICAL IMAGING : PROCEEDINGS OF THE ... CONFERENCE 2023; 13939:415-427. [PMID: 37426457 PMCID: PMC10329768 DOI: 10.1007/978-3-031-34048-2_32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes. Optimal retinal image quality is mandated for accurate medical diagnoses and automated analyses. Herein, we leveraged the Optimal Transport (OT) theory to propose an unpaired image-to-image translation scheme for mapping low-quality retinal CFPs to high-quality counterparts. Furthermore, to improve the flexibility, robustness, and applicability of our image enhancement pipeline in the clinical practice, we generalized a state-of-the-art model-based image reconstruction method, regularization by denoising, by plugging in priors learned by our OT-guided image-to-image translation network. We named it as regularization by enhancing (RE). We validated the integrated framework, OTRE, on three publicly available retinal image datasets by assessing the quality after enhancement and their performance on various downstream tasks, including diabetic retinopathy grading, vessel segmentation, and diabetic lesion segmentation. The experimental results demonstrated the superiority of our proposed framework over some state-of-the-art unsupervised competitors and a state-of-the-art supervised method.
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Affiliation(s)
- Wenhui Zhu
- School of Computing and Augmented Intelligence, Arizona State Univ., AZ, USA
| | - Peijie Qiu
- McKeley School of Engineering, Washington Univ. in St. Louis, St. Louis, MO, USA
| | | | | | - Mohammad Farazi
- School of Computing and Augmented Intelligence, Arizona State Univ., AZ, USA
| | - Zhangsihao Yang
- School of Computing and Augmented Intelligence, Arizona State Univ., AZ, USA
| | - Keshav Nandakumar
- School of Computing and Augmented Intelligence, Arizona State Univ., AZ, USA
| | - Yalin Wang
- School of Computing and Augmented Intelligence, Arizona State Univ., AZ, USA
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19
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Channa R, Wolf RM, Abràmoff MD, Lehmann HP. Effectiveness of artificial intelligence screening in preventing vision loss from diabetes: a policy model. NPJ Digit Med 2023; 6:53. [PMID: 36973403 PMCID: PMC10042864 DOI: 10.1038/s41746-023-00785-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 02/24/2023] [Indexed: 03/29/2023] Open
Abstract
The effectiveness of using artificial intelligence (AI) systems to perform diabetic retinal exams ('screening') on preventing vision loss is not known. We designed the Care Process for Preventing Vision Loss from Diabetes (CAREVL), as a Markov model to compare the effectiveness of point-of-care autonomous AI-based screening with in-office clinical exam by an eye care provider (ECP), on preventing vision loss among patients with diabetes. The estimated incidence of vision loss at 5 years was 1535 per 100,000 in the AI-screened group compared to 1625 per 100,000 in the ECP group, leading to a modelled risk difference of 90 per 100,000. The base-case CAREVL model estimated that an autonomous AI-based screening strategy would result in 27,000 fewer Americans with vision loss at 5 years compared with ECP. Vision loss at 5 years remained lower in the AI-screened group compared to the ECP group, in a wide range of parameters including optimistic estimates biased toward ECP. Real-world modifiable factors associated with processes of care could further increase its effectiveness. Of these factors, increased adherence with treatment was estimated to have the greatest impact.
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Affiliation(s)
- Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA.
| | - Risa M Wolf
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Michael D Abràmoff
- Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USA
| | - Harold P Lehmann
- Department of Medicine, Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA
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20
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Vujosevic S, Limoli C, Luzi L, Nucci P. Digital innovations for retinal care in diabetic retinopathy. Acta Diabetol 2022; 59:1521-1530. [PMID: 35962258 PMCID: PMC9374293 DOI: 10.1007/s00592-022-01941-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 07/04/2022] [Indexed: 12/02/2022]
Abstract
AIM The purpose of this review is to examine the applications of novel digital technology domains for the screening and management of patients with diabetic retinopathy (DR). METHODS A PubMed engine search was performed, using the terms "Telemedicine", "Digital health", "Telehealth", "Telescreening", "Artificial intelligence", "Deep learning", "Smartphone", "Triage", "Screening", "Home-based", "Monitoring", "Ophthalmology", "Diabetes", "Diabetic Retinopathy", "Retinal imaging". Full-text English language studies from January 1, 2010, to February 1, 2022, and reference lists were considered for the conceptual framework of this review. RESULTS Diabetes mellitus and its eye complications, including DR, are particularly well suited to digital technologies, providing an ideal model for telehealth initiatives and real-world applications. The current development in the adoption of telemedicine, artificial intelligence and remote monitoring as an alternative to or in addition to traditional forms of care will be discussed. CONCLUSIONS Advances in digital health have created an ecosystem ripe for telemedicine in the field of DR to thrive. Stakeholders and policymakers should adopt a participatory approach to ensure sustained implementation of these technologies after the COVID-19 pandemic. This article belongs to the Topical Collection "Diabetic Eye Disease", managed by Giuseppe Querques.
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Affiliation(s)
- Stela Vujosevic
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.
- Eye Clinic, IRCCS MultiMedica, Via San Vittore 12, 20123, Milan, Italy.
| | - Celeste Limoli
- Eye Clinic, IRCCS MultiMedica, Via San Vittore 12, 20123, Milan, Italy
- University of Milan, Milan, Italy
| | - Livio Luzi
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
- Department of Endocrinology, Nutrition and Metabolic Diseases, IRCCS MultiMedica, Milan, Italy
| | - Paolo Nucci
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
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21
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Social Determinants of Health and Impact on Screening, Prevalence, and Management of Diabetic Retinopathy in Adults: A Narrative Review. J Clin Med 2022; 11:jcm11237120. [PMID: 36498694 PMCID: PMC9739502 DOI: 10.3390/jcm11237120] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/24/2022] [Accepted: 11/28/2022] [Indexed: 12/05/2022] Open
Abstract
Diabetic retinal disease (DRD) is the leading cause of blindness among working-aged individuals with diabetes. In the United States, underserved and minority populations are disproportionately affected by diabetic retinopathy and other diabetes-related health outcomes. In this narrative review, we describe racial disparities in the prevalence and screening of diabetic retinopathy, as well as the wide-range of disparities associated with social determinants of health (SDOH), which include socioeconomic status, geography, health-care access, and education.
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22
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Pietris J, Lam A, Bacchi S, Gupta AK, Kovoor JG, Chan WO. Health Economic Implications of Artificial Intelligence Implementation for Ophthalmology in Australia: A Systematic Review. Asia Pac J Ophthalmol (Phila) 2022; 11:554-562. [PMID: 36218837 DOI: 10.1097/apo.0000000000000565] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/15/2022] [Indexed: 11/24/2022] Open
Abstract
PURPOSE The health care industry is an inherently resource-intense sector. Emerging technologies such as artificial intelligence (AI) are at the forefront of advancements in health care. The health economic implications of this technology have not been clearly established and represent a substantial barrier to adoption both in Australia and globally. This review aims to determine the health economic impact of implementing AI to ophthalmology in Australia. METHODS A systematic search of the databases PubMed/MEDLINE, EMBASE, and CENTRAL was conducted to March 2022, before data collection and risk of bias analysis in accordance with preferred reporting items for systematic ceviews and meta-analyses 2020 guidelines (PROSPERO number CRD42022325511). Included were full-text primary research articles analyzing a population of patients who have or are being evaluated for an ophthalmological diagnosis, using a health economic assessment system to assess the cost-effectiveness of AI. RESULTS Seven articles were identified for inclusion. Economic viability was defined as direct cost to the patient that is equal to or less than costs incurred with human clinician assessment. Despite the lack of Australia-specific data, foreign analyses overwhelmingly showed that AI is just as economically viable, if not more so, than traditional human screening programs while maintaining comparable clinical effectiveness. This evidence was largely in the setting of diabetic retinopathy screening. CONCLUSIONS Primary Australian research is needed to accurately analyze the health economic implications of implementing AI on a large scale. Further research is also required to analyze the economic feasibility of adoption of AI technology in other areas of ophthalmology, such as glaucoma and cataract screening.
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Affiliation(s)
- James Pietris
- University of Queensland, Herston, QLD, Australia
- Princess Alexandra Hospital, Woolloongabba, QLD, Australia
| | - Antoinette Lam
- University of Adelaide, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Stephen Bacchi
- University of Adelaide, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Aashray K Gupta
- University of Adelaide, Adelaide, SA, Australia
- Gold Coast University Hospital, Southport, QLD, Australia
| | - Joshua G Kovoor
- University of Adelaide, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Weng Onn Chan
- University of Adelaide, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
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23
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Khan WU, Seto E. A “Do No Harm” Novel Safety Checklist and Research Approach to Determine Whether to Launch an Artificial Intelligence Based Medical Technology – Introducing the Biological-Psychological, Economic, and Social Framework (Preprint). J Med Internet Res 2022; 25:e43386. [PMID: 37018019 PMCID: PMC10131977 DOI: 10.2196/43386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/06/2023] [Accepted: 03/10/2023] [Indexed: 03/12/2023] Open
Abstract
Given the impact artificial intelligence (AI)-based medical technologies (hardware devices, software programs, and mobile apps) can have on society, debates regarding the principles behind their development and deployment are emerging. Using the biopsychosocial model applied in psychiatry and other fields of medicine as our foundation, we propose a novel 3-step framework to guide industry developers of AI-based medical tools as well as health care regulatory agencies on how to decide if a product should be launched-a "Go or No-Go" approach. More specifically, our novel framework places stakeholders' (patients, health care professionals, industry, and government institutions) safety at its core by asking developers to demonstrate the biological-psychological (impact on physical and mental health), economic, and social value of their AI tool before it is launched. We also introduce a novel cost-effective, time-sensitive, and safety-oriented mixed quantitative and qualitative clinical phased trial approach to help industry and government health care regulatory agencies test and deliberate on whether to launch these AI-based medical technologies. To our knowledge, our biological-psychological, economic, and social (BPES) framework and mixed method phased trial approach are the first to place the Hippocratic Oath of "Do No Harm" at the center of developers', implementers', regulators', and users' mindsets when determining whether an AI-based medical technology is safe to launch. Moreover, as the welfare of AI users and developers becomes a greater concern, our framework's novel safety feature will allow it to complement existing and future AI reporting guidelines.
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Affiliation(s)
- Waqas Ullah Khan
- Health Informatics, Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Emily Seto
- Health Informatics, Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
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24
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Ahuja AS, Wagner IV, Dorairaj S, Checo L, Hulzen RT. Artificial Intelligence in Ophthalmology: A Multidisciplinary Approach. Integr Med Res 2022; 11:100888. [PMID: 36212633 PMCID: PMC9539781 DOI: 10.1016/j.imr.2022.100888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/05/2022] [Accepted: 09/06/2022] [Indexed: 11/13/2022] Open
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25
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Abedi V, Kawamura Y, Li J, Phan TG, Zand R. Editorial: Machine Learning in Action: Stroke Diagnosis and Outcome Prediction. Front Neurol 2022; 13:984467. [PMID: 35937051 PMCID: PMC9346061 DOI: 10.3389/fneur.2022.984467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 07/05/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Vida Abedi
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA, United States
- *Correspondence: Vida Abedi
| | - Yuki Kawamura
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Jiang Li
- Department of Molecular and Functional Genomics, Weis Center for Research, Geisinger Health System, Danville, PA, United States
| | - Thanh G. Phan
- Stroke and Aging Research Group, Clinical Trials, Imaging and Informatics Division, School of Clinical Sciences at Monash Health, Melbourne, VIC, Australia
- Department of Neurology, Monash Health, Melbourne, VIC, Australia
| | - Ramin Zand
- Department of Neurology, College of Medicine, The Pennsylvania State University, Hershey, PA, United States
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26
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Abràmoff MD, Roehrenbeck C, Trujillo S, Goldstein J, Graves AS, Repka MX, Silva Iii EZ. A reimbursement framework for artificial intelligence in healthcare. NPJ Digit Med 2022; 5:72. [PMID: 35681002 PMCID: PMC9184542 DOI: 10.1038/s41746-022-00621-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 05/25/2022] [Indexed: 11/09/2022] Open
Affiliation(s)
- Michael D Abràmoff
- Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USA. .,AI Healthcare Coalition, Washington, DC, USA. .,Digital Diagnostics, Coralville, IA, USA.
| | - Cybil Roehrenbeck
- AI Healthcare Coalition, Washington, DC, USA.,Hogan Lovells LLP, Washington, DC, USA
| | | | | | | | - Michael X Repka
- Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Ezequiel Zeke Silva Iii
- South Texas Radiology, San Antonio, TX, USA.,University of Texas Health, Long School of Medicine, San Antonio, TX, USA
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27
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Zafar S, Mahjoub H, Mehta N, Domalpally A, Channa R. Artificial Intelligence Algorithms in Diabetic Retinopathy Screening. Curr Diab Rep 2022; 22:267-274. [PMID: 35438458 DOI: 10.1007/s11892-022-01467-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/07/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE OF REVIEW In this review, we focus on artificial intelligence (AI) algorithms for diabetic retinopathy (DR) screening and risk stratification and factors to consider when implementing AI algorithms in the clinic. RECENT FINDINGS AI algorithms have been adopted, and have received regulatory approval, for automated detection of referable DR with clinically acceptable diagnostic performance. While these metrics are an important first step, performance metrics that go beyond measures of technical accuracy are needed to fully evaluate the impact of AI algorithm on patient outcomes. Recent advances in AI present an exciting opportunity to improve patient care. Using DR as an example, we have reviewed factors to consider in the implementation of AI algorithms in real-world clinical practice. These include real-world evaluation of safety, efficacy, and equity (bias); impact on patient outcomes; ethical, logistical, and regulatory factors.
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Affiliation(s)
- Sidra Zafar
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Heba Mahjoub
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Nitish Mehta
- Department of Ophthalmology, New York University School of Medicine, New York, NY, USA
| | - Amitha Domalpally
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA.
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28
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Potential reduction in healthcare carbon footprint by autonomous artificial intelligence. NPJ Digit Med 2022; 5:62. [PMID: 35551275 PMCID: PMC9098499 DOI: 10.1038/s41746-022-00605-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 04/15/2022] [Indexed: 11/09/2022] Open
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Morrison SL, Dukhovny D, Chan RVP, Chiang MF, Campbell JP. Cost-effectiveness of Artificial Intelligence-Based Retinopathy of Prematurity Screening. JAMA Ophthalmol 2022; 140:401-409. [PMID: 35297945 PMCID: PMC8931675 DOI: 10.1001/jamaophthalmol.2022.0223] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Artificial intelligence (AI)-based retinopathy of prematurity (ROP) screening may improve ROP care, but its cost-effectiveness is unknown. Objective To evaluate the relative cost-effectiveness of autonomous and assistive AI-based ROP screening compared with telemedicine and ophthalmoscopic screening over a range of estimated probabilities, costs, and outcomes. Design, Setting, and Participants A cost-effectiveness analysis of AI ROP screening compared with ophthalmoscopy and telemedicine via economic modeling was conducted. Decision trees created and analyzed modeled outcomes and costs of 4 possible ROP screening strategies: ophthalmoscopy, telemedicine, assistive AI with telemedicine review, and autonomous AI with only positive screen results reviewed. A theoretical cohort of infants requiring ROP screening in the United States each year was analyzed. Main Outcomes and Measures Screening and treatment costs were based on Current Procedural Terminology codes and included estimated opportunity costs for physicians. Outcomes were based on the Early Treatment of ROP study, defined as timely treatment, late treatment, or correctly untreated. Incremental cost-effectiveness ratios were calculated at a willingness-to-pay threshold of $100 000. One-way and probabilistic sensitivity analyses were performed comparing AI strategies to telemedicine and ophthalmoscopy to evaluate the cost-effectiveness across a range of assumptions. In a secondary analysis, the modeling was repeated and assumed a higher sensitivity for detection of severe ROP using AI compared with ophthalmoscopy. Results This theoretical cohort included 52 000 infants born 30 weeks' gestation or earlier or weighed 1500 g or less at birth. Autonomous AI was as effective and less costly than any other screening strategy. AI-based ROP screening was cost-effective up to $7 for assistive and $34 for autonomous screening compared with telemedicine and $64 and $91 compared with ophthalmoscopy in the primary analysis. In the probabilistic sensitivity analysis, autonomous AI screening was more than 60% likely to be cost-effective at all willingness-to-pay levels vs other modalities. In a second simulated cohort with 99% sensitivity for AI, the number of late treatments for ROP decreased from 265 when ROP screening was performed with ophthalmoscopy to 40 using autonomous AI. Conclusions and Relevance AI-based screening for ROP may be more cost-effective than telemedicine and ophthalmoscopy, depending on the added cost of AI and the relative performance of AI vs human examiners detecting severe ROP. As AI-based screening for ROP is commercialized, care must be given to appropriately price the technology to ensure its benefits are fully realized.
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Affiliation(s)
- Steven L Morrison
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
| | - Dmitry Dukhovny
- Department of Pediatrics, Oregon Health & Science University, Portland
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago
| | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - J Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
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30
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Zhang Y, Bai W, Li R, Du Y, Sun R, Li T, Kang H, Yang Z, Tang J, Wang N, Liu H. Cost-Utility Analysis of Screening for Diabetic Retinopathy in China. HEALTH DATA SCIENCE 2022; 2022:9832185. [PMID: 38487485 PMCID: PMC10904067 DOI: 10.34133/2022/9832185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 03/01/2022] [Indexed: 03/17/2024]
Abstract
Background. Diabetic retinopathy (DR) has been primarily indicated to cause vision impairment and blindness, while no studies have focused on the cost-utility of telemedicine-based and community screening programs for DR in China, especially in rural and urban areas, respectively.Methods. We developed a Markov model to calculate the cost-utility of screening programs for DR in DM patients in rural and urban settings from the societal perspective. The incremental cost-utility ratio (ICUR) was calculated for the assessment.Results. In the rural setting, the community screening program obtained 1 QALY with a cost of $4179 (95% CI 3859 to 5343), and the telemedicine screening program had an ICUR of $2323 (95% CI 1023 to 3903) compared with no screening, both of which satisfied the criterion of a significantly cost-effective health intervention. Likewise, community screening programs in urban areas generated an ICUR of $3812 (95% CI 2906 to 4167) per QALY gained, with telemedicine screening at an ICUR of $2437 (95% CI 1242 to 3520) compared with no screening, and both were also cost-effective. By further comparison, compared to community screening programs, telemedicine screening yielded an ICUR of 1212 (95% CI 896 to 1590) per incremental QALY gained in rural setting and 1141 (95% CI 859 to 1403) in urban setting, which both meet the criterion for a significantly cost-effective health intervention.Conclusions. Both telemedicine and community screening for DR in rural and urban settings were cost-effective in China, and telemedicine screening programs were more cost-effective.
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Affiliation(s)
- Yue Zhang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing Institute of Ophthalmology, Beijing, China
| | - Weiling Bai
- Beijing Tongren Eye Center, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing Institute of Ophthalmology, Beijing, China
| | - Ruyue Li
- Beijing Tongren Eye Center, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing Institute of Ophthalmology, Beijing, China
| | - Yifan Du
- Beijing Tongren Eye Center, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing Institute of Ophthalmology, Beijing, China
| | - Runzhou Sun
- Beijing Tongren Eye Center, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing Institute of Ophthalmology, Beijing, China
| | - Tao Li
- College of Computer Science, Nankai University, Tianjin, China
| | - Hong Kang
- College of Computer Science, Nankai University, Tianjin, China
| | - Ziwei Yang
- School of Agricultural Economics and Rural Development, Renmin University of China, Beijing, China
| | - Jianjun Tang
- School of Agricultural Economics and Rural Development, Renmin University of China, Beijing, China
| | - Ningli Wang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing Institute of Ophthalmology, Beijing, China
- National Institute of Health Data Science at Peking University, Beijing, China
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Science Key Lab, Beijing, China
| | - Hanruo Liu
- National Institute of Health Data Science at Peking University, Beijing, China
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Science Key Lab, Beijing, China
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China
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31
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Voets MM, Veltman J, Slump CH, Siesling S, Koffijberg H. Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:340-349. [PMID: 35227444 DOI: 10.1016/j.jval.2021.11.1362] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 10/14/2021] [Accepted: 11/10/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVES This study aimed to systematically review recent health economic evaluations (HEEs) of artificial intelligence (AI) applications in healthcare. The aim was to discuss pertinent methods, reporting quality and challenges for future implementation of AI in healthcare, and additionally advise future HEEs. METHODS A systematic literature review was conducted in 2 databases (PubMed and Scopus) for articles published in the last 5 years. Two reviewers performed independent screening, full-text inclusion, data extraction, and appraisal. The Consolidated Health Economic Evaluation Reporting Standards and Philips checklist were used for the quality assessment of included studies. RESULTS A total of 884 unique studies were identified; 20 were included for full-text review, covering a wide range of medical specialties and care pathway phases. The most commonly evaluated type of AI was automated medical image analysis models (n = 9, 45%). The prevailing health economic analysis was cost minimization (n = 8, 40%) with the costs saved per case as preferred outcome measure. A total of 9 studies (45%) reported model-based HEEs, 4 of which applied a time horizon >1 year. The evidence supporting the chosen analytical methods, assessment of uncertainty, and model structures was underreported. The reporting quality of the articles was moderate as on average studies reported on 66% of Consolidated Health Economic Evaluation Reporting Standards items. CONCLUSIONS HEEs of AI in healthcare are limited and often focus on costs rather than health impact. Surprisingly, model-based long-term evaluations are just as uncommon as model-based short-term evaluations. Consequently, insight into the actual benefits offered by AI is lagging behind current technological developments.
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Affiliation(s)
- Madelon M Voets
- Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands
| | - Jeroen Veltman
- Multi-Modality Medical Imaging, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Department of Radiology, Ziekenhuisgroep Twente, Almelo, The Netherlands
| | - Cornelis H Slump
- Department of Robotics and Mechatronics, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Sabine Siesling
- Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands
| | - Hendrik Koffijberg
- Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
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32
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Gomez Rossi J, Rojas-Perilla N, Krois J, Schwendicke F. Cost-effectiveness of Artificial Intelligence as a Decision-Support System Applied to the Detection and Grading of Melanoma, Dental Caries, and Diabetic Retinopathy. JAMA Netw Open 2022; 5:e220269. [PMID: 35289862 PMCID: PMC8924723 DOI: 10.1001/jamanetworkopen.2022.0269] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE To assess the cost-effectiveness of artificial intelligence (AI) for supporting clinicians in detecting and grading diseases in dermatology, dentistry, and ophthalmology. IMPORTANCE AI has been referred to as a facilitator for more precise, personalized, and safer health care, and AI algorithms have been reported to have diagnostic accuracies at or above the average physician in dermatology, dentistry, and ophthalmology. DESIGN, SETTING, AND PARTICIPANTS This economic evaluation analyzed data from 3 Markov models used in previous cost-effectiveness studies that were adapted to compare AI vs standard of care to detect melanoma on skin photographs, dental caries on radiographs, and diabetic retinopathy on retina fundus imaging. The general US and German population aged 50 and 12 years, respectively, as well as individuals with diabetes in Brazil aged 40 years were modeled over their lifetime. Monte Carlo microsimulations and sensitivity analyses were used to capture lifetime efficacy and costs. An annual cycle length was chosen. Data were analyzed between February 2021 and August 2021. EXPOSURE AI vs standard of care. MAIN OUTCOMES AND MEASURES Association of AI with tooth retention-years for dentistry and quality-adjusted life-years (QALYs) for individuals in dermatology and ophthalmology; diagnostic costs. RESULTS In 1000 microsimulations with 1000 random samples, AI as a diagnostic-support system showed limited cost-savings and gains in tooth retention-years and QALYs. In dermatology, AI showed mean costs of $750 (95% CI, $608-$970) and was associated with 86.5 QALYs (95% CI, 84.9-87.9 QALYs), while the control showed higher costs $759 (95% CI, $618-$970) with similar QALY outcome. In dentistry, AI accumulated costs of €320 (95% CI, €299-€341) (purchasing power parity [PPP] conversion, $429 [95% CI, $400-$458]) with 62.4 years per tooth retention (95% CI, 60.7-65.1 years). The control was associated with higher cost, €342 (95% CI, €318-€368) (PPP, $458; 95% CI, $426-$493) and fewer tooth retention-years (60.9 years; 95% CI, 60.5-63.1 years). In ophthalmology, AI accrued costs of R $1321 (95% CI, R $1283-R $1364) (PPP, $559; 95% CI, $543-$577) at 8.4 QALYs (95% CI, 8.0-8.7 QALYs), while the control was less expensive (R $1260; 95% CI, R $1222-R $1303) (PPP, $533; 95% CI, $517-$551) and associated with similar QALYs. Dominance in favor of AI was dependent on small differences in the fee paid for the service and the treatment assumed after diagnosis. The fee paid for AI was a factor in patient preferences in cost-effectiveness between strategies. CONCLUSIONS AND RELEVANCE The findings of this study suggest that marginal improvements in diagnostic accuracy when using AI may translate into a marginal improvement in outcomes. The current evidence supporting AI as decision support from a cost-effectiveness perspective is limited; AI should be evaluated on a case-specific basis to capture not only differences in costs and payment mechanisms but also treatment after diagnosis.
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Affiliation(s)
- Jesus Gomez Rossi
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - Natalia Rojas-Perilla
- Department of Economics, Freie Universität Berlin, Germany
- Department of Analytics in the Digital Era, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
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Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med 2022; 28:31-38. [PMID: 35058619 DOI: 10.1038/s41591-021-01614-0] [Citation(s) in RCA: 504] [Impact Index Per Article: 252.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/05/2021] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human-AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI's potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide.
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Affiliation(s)
- Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard University, Cambridge, MA, USA
| | - Emma Chen
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Oishi Banerjee
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Eric J Topol
- Scripps Translational Science Institute, San Diego, CA, USA.
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Vehi J, Mujahid O, Contreras I. Aim and Diabetes. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Lin T, Gubitosi-Klug RA, Channa R, Wolf RM. Pediatric Diabetic Retinopathy: Updates in Prevalence, Risk Factors, Screening, and Management. Curr Diab Rep 2021; 21:56. [PMID: 34902076 DOI: 10.1007/s11892-021-01436-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/22/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE OF REVIEW Diabetic retinopathy (DR) is a microvascular complication of diabetes mellitus and a major cause of vision loss worldwide. The purpose of this review is to provide an update on the prevalence of diabetic retinopathy in youth, discuss risk factors, and review recent advances in diabetic retinopathy screening. RECENT FINDINGS While DR has long been considered a microvascular complication, recent data suggests that retinal neurodegeneration may precede the vascular changes associated with DR. The prevalence of DR has decreased in type 1 diabetes (T1D) patients following the results of the Diabetes Control and Complications Trial and implementation of intensive insulin therapy, with prevalence ranging from 14-20% before the year 2000 to 3.7-6% after 2000. In contrast, the prevalence of diabetic retinopathy in pediatric type 2 diabetes (T2D) is higher, ranging from 9.1-50%. Risk factors for diabetic retinopathy are well established and include glycemic control, diabetes duration, hypertension, and hyperlipidemia, whereas diabetes technology use including insulin pumps and continuous glucose monitors has been shown to have protective effects. Screening for DR is recommended for youth with T1D once they are aged ≥ 11 years or puberty has started and diabetes duration of 3-5 years. Pediatric T2D patients are advised to undergo screening at or soon after diagnosis, and annually thereafter, due to the insidious nature of T2D. Recent advances in DR screening methods including point of care and artificial intelligence technology have increased access to DR screening, while being cost-saving to patients and cost-effective to healthcare systems. While the prevalence of diabetic retinopathy in youth with T1D has been declining over the last few decades, there has been a significant increase in the prevalence of DR in youth with T2D. Improving access to diabetic retinopathy screening using novel screening methods may help improve detection and early treatment of diabetic retinopathy.
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Affiliation(s)
- Tyger Lin
- Department of Pediatrics, Division of Pediatric Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA
| | - Rose A Gubitosi-Klug
- Department of Pediatrics, Division of Endocrinology, Case Western Reserve University School of Medicine and Rainbow Babies and Children's Hospital, Cleveland, OH, USA
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA
| | - Risa M Wolf
- Department of Pediatrics, Division of Pediatric Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA.
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Updates in deep learning research in ophthalmology. Clin Sci (Lond) 2021; 135:2357-2376. [PMID: 34661658 DOI: 10.1042/cs20210207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 09/14/2021] [Accepted: 09/29/2021] [Indexed: 12/13/2022]
Abstract
Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.
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Gomez Rossi J, Feldberg B, Krois J, Schwendicke F. A systematic scoping review analysing clinical, technical and financial aspects of cost-effectiveness of Artificial Intelligence applied in medicine: A theory and framework of analysis (Preprint). JMIR Med Inform 2021; 10:e33703. [PMID: 35969458 PMCID: PMC9419048 DOI: 10.2196/33703] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 03/29/2022] [Accepted: 05/13/2022] [Indexed: 11/13/2022] Open
Abstract
Background Cost-effectiveness analysis of artificial intelligence (AI) in medicine demands consideration of clinical, technical, and economic aspects to generate impactful research of a novel and highly versatile technology. Objective We aimed to systematically scope existing literature on the cost-effectiveness of AI and to extract and summarize clinical, technical, and economic dimensions required for a comprehensive assessment. Methods A scoping literature review was conducted to map medical, technical, and economic aspects considered in studies on the cost-effectiveness of medical AI. Based on these, a framework for health policy analysis was developed. Results Among 4820 eligible studies, 13 met the inclusion criteria for our review. Internal medicine and emergency medicine were the clinical disciplines most frequently analyzed. Most of the studies included were from the United States (5/13, 39%), assessed solutions requiring market access (9/13, 69%), and proposed optimization of direct resources as the most frequent value proposition (7/13, 53%). On the other hand, technical aspects were not uniformly disclosed in the studies we analyzed. A minority of articles explicitly stated the payment mechanism assumed (5/13, 38%), while it remained unspecified in the majority (8/13, 62%) of studies. Conclusions Current studies on the cost-effectiveness of AI do not allow to determine if the investigated AI solutions are clinically, technically, and economically viable. Further research and improved reporting on these dimensions seem relevant to recommend and assess potential use cases for this technology.
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Affiliation(s)
- Jesus Gomez Rossi
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany
| | - Ben Feldberg
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany
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Ferm ML, DeSalvo DJ, Prichett LM, Sickler JK, Wolf RM, Channa R. Clinical and Demographic Factors Associated With Diabetic Retinopathy Among Young Patients With Diabetes. JAMA Netw Open 2021; 4:e2126126. [PMID: 34570208 PMCID: PMC8477260 DOI: 10.1001/jamanetworkopen.2021.26126] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
IMPORTANCE Diabetic retinopathy (DR) is a leading cause of vision loss worldwide. As the incidence of both type 1 and type 2 diabetes among youths continues to increase around the world, understanding the factors associated with the development of DR in this age group is important. OBJECTIVE To identify factors associated with DR among children, adolescents, and young adults with type 1 or type 2 diabetes in the US. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study pooled data from 2 large academic pediatric centers in the US (Baylor College of Medicine/Texas Children's Hospital [BCM/TCH] Diabetes and Endocrine Care Center and Johns Hopkins University [JHU] Pediatric Diabetes Center) to form a diverse population for analysis. Data were collected prospectively at the JHU center (via point-of-care screening using fundus photography) from December 3, 2018, to November 1, 2019, and retrospectively at the BCM/TCH center (via electronic health records of patients who received point-of-care screening using retinal cameras between June 1, 2016, and May 31, 2019). A total of 1640 individuals aged 5 to 21 years with type 1 or type 2 diabetes (308 participants from the JHU center and 1332 participants from the BCM/TCH center) completed DR screening and had gradable images. MAIN OUTCOME AND MEASURES Prevalence of DR, as identified on fundus photography, and factors associated with DR. RESULTS Among 1640 participants (mean [SD] age, 15.7 [3.6] years; 867 female individuals [52.9%]), 1216 (74.1%) had type 1 diabetes, and 416 (25.4%) had type 2 diabetes. A total of 506 participants (30.9%) were Hispanic, 384 (23.4%) were non-Hispanic Black or African American, 647 (39.5%) were non-Hispanic White, and 103 (6.3%) were of other races or ethnicities (1 was American Indian or Alaska Native, 50 were Asian, 1 was Native Hawaiian or Pacific Islander, and 51 did not specify race or ethnicity, specified other race or ethnicity, or had unavailable data on race or ethnicity). Overall, 558 of 1216 patients (45.9%) with type 1 diabetes used an insulin pump, and 5 of 416 patients (1.2%) with type 2 diabetes used an insulin pump. Diabetic retinopathy was found in 57 of 1640 patients (3.5%). Patients with DR vs without DR had a greater duration of diabetes (mean [SD], 9.4 [4.4] years vs 6.6 [4.4] years; P < .001) and higher hemoglobin A1c (HbA1c) levels (mean [SD], 10.3% [2.4%] vs 9.2% [2.1%]; P < .001). Among those with type 1 diabetes, insulin pump use was associated with a lower likelihood of DR after adjusting for race and ethnicity, insurance status, diabetes duration, and HbA1c level (odds ratio [OR], 0.43; 95% CI, 0.20-0.93; P = .03). The likelihood of having DR was 2.1 times higher among Black or African American participants compared with White participants (OR, 2.12; 95% CI, 1.12-4.01; P = .02); this difference was no longer significant after adjusting for duration of diabetes, insurance status, insulin pump use (among patients with type 1 diabetes only), and mean HbA1c level (type 1 diabetes: OR, 1.79; 95% CI, 0.83-3.89; P = .14; type 2 diabetes: OR, 1.08; 95% CI, 0.30-3.85; P = .91). CONCLUSIONS AND RELEVANCE This study found that although the duration of diabetes and suboptimal glycemic control have long been associated with DR, insulin pump use (among those with type 1 diabetes) was independently associated with a lower likelihood of DR, which is likely owing to decreased glycemic variability and increased time in range (ie, the percentage of time blood glucose levels remain within the 70-180 mg/dL range). Black or African American race was found to be associated with DR in the univariable analysis but not in the multivariable analysis, which may represent disparities in access to diabetes technologies and care.
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Affiliation(s)
- Michael L. Ferm
- Baylor College of Medicine, Texas Children’s Hospital, Houston
| | - Daniel J. DeSalvo
- Pediatric Endocrinology and Metabolism, Baylor College of Medicine, Texas Children’s Hospital, Houston
| | - Laura M. Prichett
- Biostatistics, Epidemiology, and Data Management Core, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | - Risa M. Wolf
- Division of Endocrinology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison
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Ruamviboonsuk P, Chantra S, Seresirikachorn K, Ruamviboonsuk V, Sangroongruangsri S. Economic Evaluations of Artificial Intelligence in Ophthalmology. Asia Pac J Ophthalmol (Phila) 2021; 10:307-316. [PMID: 34261102 DOI: 10.1097/apo.0000000000000403] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
ABSTRACT Artificial intelligence (AI) is expected to cause significant medical quality enhancements and cost-saving improvements in ophthalmology. Although there has been a rapid growth of studies on AI in the recent years, real-world adoption of AI is still rare. One reason may be because the data derived from economic evaluations of AI in health care, which policy makers used for adopting new technology, have been fragmented and scarce. Most data on economics of AI in ophthalmology are from diabetic retinopathy (DR) screening. Few studies classified costs of AI software, which has been considered as a medical device, into direct medical costs. These costs of AI are composed of initial and maintenance costs. The initial costs may include investment in research and development, and costs for validation of different datasets. Meanwhile, the maintenance costs include costs for algorithms upgrade and hardware maintenance in the long run. The cost of AI should be balanced between manufacturing price and reimbursements since it may pose significant challenges and barriers to providers. Evidence from cost-effectiveness analyses showed that AI, either standalone or used with humans, was more cost-effective than manual DR screening. Notably, economic evaluation of AI for DR screening can be used as a model for AI to other ophthalmic diseases.
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Affiliation(s)
- Paisan Ruamviboonsuk
- Department of Ophthalmology, Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok, Thailand
| | - Somporn Chantra
- Department of Ophthalmology, Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok, Thailand
| | - Kasem Seresirikachorn
- Department of Ophthalmology, Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok, Thailand
| | - Varis Ruamviboonsuk
- Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sermsiri Sangroongruangsri
- Social and Administrative Pharmacy Division, Department of Pharmacy, Faculty of Pharmacy, Mahidol University, Bangkok, Thailand
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Valikodath NG, Al-Khaled T, Cole E, Ting DSW, Tu EY, Campbell JP, Chiang MF, Hallak JA, Chan RVP. Evaluation of pediatric ophthalmologists' perspectives of artificial intelligence in ophthalmology. J AAPOS 2021; 25:164.e1-164.e5. [PMID: 34087473 PMCID: PMC8328946 DOI: 10.1016/j.jaapos.2021.01.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 12/31/2022]
Abstract
PURPOSE To survey pediatric ophthalmologists on their perspectives of artificial intelligence (AI) in ophthalmology. METHODS This is a subgroup analysis of a study previously reported. In March 2019, members of the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) were recruited via the online AAPOS discussion board to voluntarily complete a Web-based survey consisting of 15 items. Survey items assessed the extent participants "agreed" or "disagreed" with statements on the perceived benefits and concerns of AI in ophthalmology. Responses were analyzed using descriptive statistics. RESULTS A total of 80 pediatric ophthalmologists who are members of AAPOS completed the survey. The mean number of years since graduating residency was 21 years (range, 0-46). Overall, 91% (73/80) reported understanding the concept of AI, 70% (56/80) believed AI will improve the practice of ophthalmology, 68% (54/80) reported willingness to incorporate AI into their clinical practice, 65% (52/80) did not believe AI will replace physicians, and 71% (57/80) believed AI should be incorporated into medical school and residency curricula. However, 15% (12/80) were concerned that AI will replace physicians, 26% (21/80) believed AI will harm the patient-physician relationship, and 46% (37/80) reported concern over the diagnostic accuracy of AI. CONCLUSIONS Most pediatric ophthalmologists in this survey viewed the role of AI in ophthalmology positively.
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Affiliation(s)
- Nita G Valikodath
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Tala Al-Khaled
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Emily Cole
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Daniel S W Ting
- Singapore National Eye Center, Duke-NUS Medical School Singapore, Singapore
| | - Elmer Y Tu
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - J Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Joelle A Hallak
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois.
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Wolf RM, Liu TYA, Thomas C, Prichett L, Zimmer-Galler I, Smith K, Abramoff MD, Channa R. The SEE Study: Safety, Efficacy, and Equity of Implementing Autonomous Artificial Intelligence for Diagnosing Diabetic Retinopathy in Youth. Diabetes Care 2021; 44:781-787. [PMID: 33479160 DOI: 10.2337/dc20-1671] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 12/23/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Diabetic retinopathy (DR) is a leading cause of vision loss worldwide. Screening for DR is recommended in children and adolescents, but adherence is poor. Recently, autonomous artificial intelligence (AI) systems have been developed for early detection of DR and have been included in the American Diabetes Association's guidelines for screening in adults. We sought to determine the diagnostic efficacy of autonomous AI for the diabetic eye exam in youth with diabetes. RESEARCH DESIGN AND METHODS In this prospective study, point-of-care diabetic eye exam was implemented using a nonmydriatic fundus camera with an autonomous AI system for detection of DR in a multidisciplinary pediatric diabetes center. Sensitivity, specificity, and diagnosability of AI was compared with consensus grading by retinal specialists, who were masked to AI output. Adherence to screening guidelines was measured before and after AI implementation. RESULTS Three hundred ten youth with diabetes aged 5-21 years were included, of whom 4.2% had DR. Diagnosability of AI was 97.5% (302 of 310). The sensitivity and specificity of AI to detect more-than-mild DR was 85.7% (95% CI 42.1-99.6%) and 79.3% (74.3-83.8%), respectively, compared with the reference standard as defined by retina specialists. Adherence improved from 49% to 95% after AI implementation. CONCLUSIONS Use of a nonmydriatic fundus camera with autonomous AI was safe and effective for the diabetic eye exam in youth in our study. Adherence to screening guidelines improved with AI implementation. As the prevalence of diabetes increases in youth and adherence to screening guidelines remains suboptimal, effective strategies for diabetic eye exams in this population are needed.
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Affiliation(s)
- Risa M Wolf
- Division of Endocrinology, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, MD
| | - T Y Alvin Liu
- Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD
| | - Chrystal Thomas
- Division of Endocrinology, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, MD
| | - Laura Prichett
- Biostatistics, Epidemiology, and Data Management Core, Johns Hopkins School of Medicine, Baltimore, MD
| | | | - Kerry Smith
- Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD
| | - Michael D Abramoff
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA.,IDx, Coralville, IA.,Iowa City VA Medical Center, Iowa City, IA.,Department of Biomedical Engineering, The University of Iowa, Iowa City, IA.,Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA
| | - Roomasa Channa
- Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD .,Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI
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Aim and Diabetes. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_158-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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