Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC–AUC of 0.89 (95% CI: 0.87–0.91), corresponding to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82–85%), but only half the specificity (45–50%, p < 0.001). Our model can also detect the presence of intraretinal fluid (AUC: 0.81; 95% CI: 0.81–0.86) and subretinal fluid (AUC 0.88; 95% CI: 0.85–0.91). Using deep learning to make predictions via simple 2D images without sophisticated 3D-imaging equipment and with better than specialist performance, has broad relevance to many other applications in medical imaging

Deep learning to detect optical coherence tomography-derived diabetic macular edema from retinal photographs: a multicenter validation study

PURPOSE: To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from two-dimensional color fundus photography (CFP), where the reference standard for retinal thickness and fluid presence is derived from three-dimensional optical coherence tomography (OCT). DESIGN: Retrospective validation of a DLS across international datasets. PARTICIPANTS: Paired CFP and OCT of patients from diabetic retinopathy (DR) screening programs or retina clinics. The DLS was developed using datasets from Thailand, the United Kingdom (UK) and the United States and validated using 3,060 unique eyes from 1,582 patients across screening populations in Australia, India and Thailand. The DLS was separately validated in 698 eyes from 537 screened patients in the UK with mild DR and suspicion of DME based on CFP. METHODS: The DLS was trained using DME labels from OCT. Presence of DME was based on retinal thickening or intraretinal fluid. The DLS's performance was compared to expert grades of maculopathy and to a previous proof-of-concept version of the DLS. We further simulated integration of the current DLS into an algorithm trained to detect DR from CFPs. MAIN OUTCOME MEASURES: Superiority of specificity and non-inferiority of sensitivity of the DLS for the detection of center-involving DME, using device specific thresholds, compared to experts. RESULTS: Primary analysis in a combined dataset spanning Australia, India, and Thailand showed the DLS had 80% specificity and 81% sensitivity compared to expert graders who had 59% specificity and 70% sensitivity. Relative to human experts, the DLS had significantly higher specificity (p=0.008) and non-inferior sensitivity (p 50%) and a sensitivity of 100% (p=0.02 for sensitivity > 90%). CONCLUSIONS: The DLS can generalize to multiple international populations with an accuracy exceeding experts. The clinical value of this DLS to reduce false positive referrals, thus decreasing the burden on specialist eye care, warrants prospective evaluation

Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective

Background: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83. Conclusion: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology

Grand Challenges in global eye health: a global prioritisation process using Delphi method

Background We undertook a Grand Challenges in Global Eye Health prioritisation exercise to identify the key issues that must be addressed to improve eye health in the context of an ageing population, to eliminate persistent inequities in health-care access, and to mitigate widespread resource limitations. Methods Drawing on methods used in previous Grand Challenges studies, we used a multi-step recruitment strategy to assemble a diverse panel of individuals from a range of disciplines relevant to global eye health from all regions globally to participate in a three-round, online, Delphi-like, prioritisation process to nominate and rank challenges in global eye health. Through this process, we developed both global and regional priority lists. Findings Between Sept 1 and Dec 12, 2019, 470 individuals complete round 1 of the process, of whom 336 completed all three rounds (round 2 between Feb 26 and March 18, 2020, and round 3 between April 2 and April 25, 2020) 156 (46%) of 336 were women, 180 (54%) were men. The proportion of participants who worked in each region ranged from 104 (31%) in sub-Saharan Africa to 21 (6%) in central Europe, eastern Europe, and in central Asia. Of 85 unique challenges identified after round 1, 16 challenges were prioritised at the global level; six focused on detection and treatment of conditions (cataract, refractive error, glaucoma, diabetic retinopathy, services for children and screening for early detection), two focused on addressing shortages in human resource capacity, five on other health service and policy factors (including strengthening policies, integration, health information systems, and budget allocation), and three on improving access to care and promoting equity. Interpretation This list of Grand Challenges serves as a starting point for immediate action by funders to guide investment in research and innovation in eye health. It challenges researchers, clinicians, and policy makers to build collaborations to address specific challenge

Major single nucleotide polymorphisms in polypoidal choroidal vasculopathy: a comparative analysis between Thai and other Asian populations

Patchima Chantaren1, Paisan Ruamviboonsuk1, Mathurose Ponglikitmongkol2, Montip Tiensuwan3, Somying Promso41Department of Ophthalmology, Rajavithi Hospital, Bangkok, 2Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, 3Department of Mathematics, Faculty of Science, Mahidol University, Bangkok, 4Virology and Molecular Microbiology Unit, Department of Pathology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, ThailandPurpose: To investigate the association in a Thai population between the major age-related macular degeneration (AMD) susceptibility loci, Y402H and I62V in the complement factor H (CFH) and A69S in the age-related maculopathy susceptibility 2 (ARMS2) genes, and polypoidal choroidal vasculopathy (PCV).Methods: A case-control study included 97 PCV cases and 102 age- and gender-matched controls without any retinopathy. The genotypic profiles of the three polymorphisms were obtained using a real-time polymerase chain reaction assay. The allelic and genotypic association between the polymorphisms and PCV were compared with those from the compiled data of other Asian populations reported previously.Results: Strong associations between the Y402H, I62V, and A69S polymorphisms and PCV were observed in the present study (P = 0.002, 0.003, and 0.0008 respectively) and in the compiled data (P &amp;lt; 0.0001 for all three polymorphisms). The risk allele frequencies of the polymorphisms in PCVs and in controls from the present study (15.0% and 5.4% for Y402H, 71.7% and 57.4% for I62V, and 54.1% and 37.3% for A69S respectively) were also comparable with the frequencies from the compiled data (10.3% and 6.4% for Y402H, 75.2% and 58.3% for I62V, and 56.8% and 36.8% for A69S respectively). The genotype distribution for each polymorphism was also comparable in both datasets.Conclusion: The findings of this study support a significant genetic association between the major AMD susceptibility genes and PCV across Asian populations. This suggests that AMD and PCV, despite different phenotypic manifestation, may share common genetic risk factors.Keywords: genetic, association, PCV, age-related macular degeneration, CFH gene, ARMS2 gen

Major single nucleotide polymorphisms in polypoidal choroidal vasculopathy: a comparative analysis between Thai and other Asian populations

Patchima Chantaren1, Paisan Ruamviboonsuk1, Mathurose Ponglikitmongkol2, Montip Tiensuwan3, Somying Promso41Department of Ophthalmology, Rajavithi Hospital, Bangkok, 2Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, 3Department of Mathematics, Faculty of Science, Mahidol University, Bangkok, 4Virology and Molecular Microbiology Unit, Department of Pathology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, ThailandPurpose: To investigate the association in a Thai population between the major age-related macular degeneration (AMD) susceptibility loci, Y402H and I62V in the complement factor H (CFH) and A69S in the age-related maculopathy susceptibility 2 (ARMS2) genes, and polypoidal choroidal vasculopathy (PCV).Methods: A case-control study included 97 PCV cases and 102 age- and gender-matched controls without any retinopathy. The genotypic profiles of the three polymorphisms were obtained using a real-time polymerase chain reaction assay. The allelic and genotypic association between the polymorphisms and PCV were compared with those from the compiled data of other Asian populations reported previously.Results: Strong associations between the Y402H, I62V, and A69S polymorphisms and PCV were observed in the present study (P = 0.002, 0.003, and 0.0008 respectively) and in the compiled data (P &amp;lt; 0.0001 for all three polymorphisms). The risk allele frequencies of the polymorphisms in PCVs and in controls from the present study (15.0% and 5.4% for Y402H, 71.7% and 57.4% for I62V, and 54.1% and 37.3% for A69S respectively) were also comparable with the frequencies from the compiled data (10.3% and 6.4% for Y402H, 75.2% and 58.3% for I62V, and 56.8% and 36.8% for A69S respectively). The genotype distribution for each polymorphism was also comparable in both datasets.Conclusion: The findings of this study support a significant genetic association between the major AMD susceptibility genes and PCV across Asian populations. This suggests that AMD and PCV, despite different phenotypic manifestation, may share common genetic risk factors.Keywords: genetic, association, PCV, age-related macular degeneration, CFH gene, ARMS2 gen

Artificial intelligence, the internet of things, and virtual clinics: ophthalmology at the digital translation forefront

10.1016/S2589-7500(19)30217-1The Lancet Digital Health21e8-e

Assessment of Direct Costs of Admission Due to Presumed Microbial Keratitis in a Tertiary Referral Hospital in Thailand: A 7-Year Retrospective Study

Somporn Chantra,1,2 Peranut Chotcomwongse,1 Supachase Jittreprasert,1 Wirapha Senarak,1,&ast; Anyarak Amornpetchsathaporn,1,&ast; Parinee Kemchoknatee,1,2 Paisan Ruamviboonsuk1,2 1Department of Ophthalmology, Rajavithi Hospital, Bangkok, Thailand; 2College of Medicine, Rangsit University, Bangkok, Thailand&ast;These authors contributed equally to this workCorrespondence: Somporn Chantra, Department of Ophthalmology, Rajavithi Hospital, 2 Phayatai Road, Bangkok, 10400, Thailand, Tel +66 86-541-3765, Email [email protected]: To evaluate the direct healthcare cost of admission and examine the effects of cost drivers of treating presumed microbial keratitis (MK) at a tertiary referral hospital.Design: Retrospective study.Methods: A total of 741 patients who presented with MK were included. All information regarding costs was collected, and demographic data were employed for risk factor analysis.Results: The total cost of treating MK over a 7-year period at Rajavithi Hospital was US&dollar;14,514,625.04, while the median cost was US&dollar;10,840.17 per patient (Q1– 3, US&dollar;5866.56– 24,172.28). The medical professional services were the highest cost category in terms of both total cost of treatment over 7 years and median cost per patient, accounting for US&dollar;6,474,718.43 and US&dollar;5235.20 (Q1– 3, US&dollar;2582.79– 10,474.24) respectively. In 2020, the total cost of treatment declined, corresponding with fewer hospitalized patients; however, the median cost per patient was the highest of all years, amounting to US&dollar;15,089.90 (Q1– 3, US&dollar;8064.17– 29102.50), while the median cost per patient from 2014 to 2019 was US&dollar;9969.96 (Q1– 3, US&dollar;5177.98– 21,942.68). Statistical significance was found in the median cost per patient in 2020 compared to the median cost per patient in 2014– 2019 (p-value 0.019). Risk factors associated with the more expensive cost of treatment were longer length of stay (LOS); age more than 60 years old; readmission; diabetes mellitus (DM); hypertension; and heart disease.Conclusion: There were several key factors impacting the direct healthcare costs of severe MK treatment. Medical professional services emerged as the most substantial cost category, while longer hospital stays, older age groups, readmission cases, and comorbidities such as diabetes mellitus, hypertension, and heart disease were all linked to elevated treatment expenses. There were no statistically significant differences in the direct medical expenses during hospitalization associated with treating severe MK, whether the culture results were positive or negative, or regardless of the type of cultured organism utilized.Keywords: corneal ulcer, infectious keratitis, healthcare cost, economic burde