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1.
Telemed J E Health ; 2020 Mar 16.
Artigo em Inglês | MEDLINE | ID: mdl-32209008

RESUMO

Background: The introduction of artificial intelligence (AI) in medicine has raised significant ethical, economic, and scientific controversies. Introduction: Because an explicit goal of AI is to perform processes previously reserved for human clinicians and other health care personnel, there is justified concern about the impact on patient safety, efficacy, equity, and liability. Discussion: Systems for computer-assisted and fully automated detection, triage, and diagnosis of diabetic retinopathy (DR) from retinal images show great variation in design, level of autonomy, and intended use. Moreover, the degree to which these systems have been evaluated and validated is heterogeneous. We use the term DR AI system as a general term for any system that interprets retinal images with at least some degree of autonomy from a human grader. We put forth these standardized descriptors to form a means to categorize systems for computer-assisted and fully automated detection, triage, and diagnosis of DR. The components of the categorization system include level of device autonomy, intended use, level of evidence for diagnostic accuracy, and system design. Conclusion: There is currently minimal empirical basis to assert that certain combinations of autonomy, accuracy, or intended use are better or more appropriate than any other. Therefore, at the current stage of development of this document, we have been descriptive rather than prescriptive, and we treat the different categorizations as independent and organized along multiple axes.

2.
Sci Rep ; 10(1): 5474, 2020 Mar 25.
Artigo em Inglês | MEDLINE | ID: mdl-32214123

RESUMO

To describe the 25-year surgical trends, long-term outcomes and risk factors affecting the outcomes of giant retinal tear-related rhegmatogenous retinal detachments (GRT-RRD). Patients' demographics, pre-operative characteristics, risk factors, operative procedures and post-operative outcomes were collected and divided into three groups - Group A: 1991 to 2015 (overall); Group B: 1991 to 2005, and Group C: 2006 to 2015. Functional and anatomical successes were monitored over a 5-year period. Multivariate logistic regression analysis was performed to identify the risk factors related to functional and anatomical success.127 eyes of 127 patients were included in the study. At 5th year, 69.4% patients had visual acuity (VA) < logMAR 1.0 with 87.5% primary anatomical success rate. While the functional outcome remained the same between group B and C, there was an increase in the anatomical success from 89.7% to 100%, albeit not statistically significant. Patients with worse presenting VA, 150 degrees or more of giant retina tear, macula-detached status and presence of PVR were associated with VA of> logMAR 1.0 (all p < 0.05). The types of surgery (TPPV vs combined SB/TPPV), number of breaks, lens extraction and additional cryotherapy were not associated with the functional or anatomical success. In conclusion, the GRT-RRD functional and structural outcomes were comparable between 1991-2005 and 2006-2015, albeit a statistically insignificant improvement of anatomical outcome over the past 25 years. Worse presenting VA, 150 degrees or more of giant retinal tear, detached macula and presence of PVR were associated with poorer visual outcome.

3.
NPJ Digit Med ; 3: 40, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32219181

RESUMO

Deep learning (DL) has been shown to be effective in developing diabetic retinopathy (DR) algorithms, possibly tackling financial and manpower challenges hindering implementation of DR screening. However, our systematic review of the literature reveals few studies studied the impact of different factors on these DL algorithms, that are important for clinical deployment in real-world settings. Using 455,491 retinal images, we evaluated two technical and three image-related factors in detection of referable DR. For technical factors, the performances of four DL models (VGGNet, ResNet, DenseNet, Ensemble) and two computational frameworks (Caffe, TensorFlow) were evaluated while for image-related factors, we evaluated image compression levels (reducing image size, 350, 300, 250, 200, 150 KB), number of fields (7-field, 2-field, 1-field) and media clarity (pseudophakic vs phakic). In detection of referable DR, four DL models showed comparable diagnostic performance (AUC 0.936-0.944). To develop the VGGNet model, two computational frameworks had similar AUC (0.936). The DL performance dropped when image size decreased below 250 KB (AUC 0.936, 0.900, p < 0.001). The DL performance performed better when there were increased number of fields (dataset 1: 2-field vs 1-field-AUC 0.936 vs 0.908, p < 0.001; dataset 2: 7-field vs 2-field vs 1-field, AUC 0.949 vs 0.911 vs 0.895). DL performed better in the pseudophakic than phakic eyes (AUC 0.918 vs 0.833, p < 0.001). Various image-related factors play more significant roles than technical factors in determining the diagnostic performance, suggesting the importance of having robust training and testing datasets for DL training and deployment in the real-world settings.

4.
Eye (Lond) ; 34(3): 451-460, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31488886

RESUMO

Diabetes is a global eye health issue. Given the rising in diabetes prevalence and ageing population, this poses significant challenge to perform diabetic retinopathy (DR) screening for these patients. Artificial intelligence (AI) using machine learning and deep learning have been adopted by various groups to develop automated DR detection algorithms. This article aims to describe the state-of-art AI DR screening technologies that have been described in the literature, some of which are already commercially available. All these technologies were designed using different training datasets and technical methodologies. Although many groups have published robust diagnostic performance of the AI algorithms for DR screening, future research is required to address several challenges, for examples medicolegal implications, ethics, and clinical deployment model in order to expedite the translation of these novel technologies into the healthcare setting.

5.
Eye (Lond) ; 34(3): 604, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31822855

RESUMO

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

6.
Retina ; 2019 Dec 13.
Artigo em Inglês | MEDLINE | ID: mdl-31842192

RESUMO

PURPOSE: To examine the relationship between macular microvasculature parameters and functional changes in persons with diabetic retinopathy (DR). METHODS: Cross-sectional study of 76 eyes with varying levels of DR. Optical coherence tomography angiography (OCTA) quantified superficial and deep perifoveal vessel densities and foveal avascular zone areas. Retinal sensitivity was measured using microperimetry. Optical coherence tomography angiography parameters and retinal sensitivity were correlated. RESULTS: Deep perifoveal vessel density decreased with increasing severity of DR (adjusted mean 51.93 vs. 49.89 vs. 47.96, P-trend = 0.005). Superficial and deep foveal avascular zone area increased with increasing DR severity (adjusted mean: 235.0 µm vs. 303.4 µm vs. 400.9 µm, P-trend = 0.003 [superficial]; 333.1 µm vs. 513.3 µm vs. 530.2 µm, P-trend = 0.001 [deep]). Retinal sensitivity decreased with increasing DR severity (adjusted mean: 25.12 dB vs. 22.34 dB vs. 20.67 dB, P-trend = 0.003). Retinal sensitivity correlated positively with deep perifoveal vessel density (Pearson's ρ = 0.276, P = 0.020) and inversely with superficial foveal avascular zone area (Pearson's ρ = -0.333, P = 0.010). CONCLUSION: Alterations in retinal microvasculature can be observed with OCTA with increasing severity of DR. These changes are correlated with reduced retinal sensitivity. Optical coherence tomography angiography is useful to detect and quantify the microvasculature properties of eyes with diabetic macular ischemia.

8.
J Neuroophthalmol ; 2019 Aug 22.
Artigo em Inglês | MEDLINE | ID: mdl-31453913

RESUMO

BACKGROUND: Deep learning (DL) has demonstrated human expert levels of performance for medical image classification in a wide array of medical fields, including ophthalmology. In this article, we present the results of our DL system designed to determine optic disc laterality, right eye vs left eye, in the presence of both normal and abnormal optic discs. METHODS: Using transfer learning, we modified the ResNet-152 deep convolutional neural network (DCNN), pretrained on ImageNet, to determine the optic disc laterality. After a 5-fold cross-validation, we generated receiver operating characteristic curves and corresponding area under the curve (AUC) values to evaluate performance. The data set consisted of 576 color fundus photographs (51% right and 49% left). Both 30° photographs centered on the optic disc (63%) and photographs with varying degree of optic disc centration and/or wider field of view (37%) were included. Both normal (27%) and abnormal (73%) optic discs were included. Various neuro-ophthalmological diseases were represented, such as, but not limited to, atrophy, anterior ischemic optic neuropathy, hypoplasia, and papilledema. RESULTS: Using 5-fold cross-validation (70% training; 10% validation; 20% testing), our DCNN for classifying right vs left optic disc achieved an average AUC of 0.999 (±0.002) with optimal threshold values, yielding an average accuracy of 98.78% (±1.52%), sensitivity of 98.60% (±1.72%), and specificity of 98.97% (±1.38%). When tested against a separate data set for external validation, our 5-fold cross-validation model achieved the following average performance: AUC 0.996 (±0.005), accuracy 97.2% (±2.0%), sensitivity 96.4% (±4.3%), and specificity 98.0% (±2.2%). CONCLUSIONS: Small data sets can be used to develop high-performing DL systems for semantic labeling of neuro-ophthalmology images, specifically in distinguishing between right and left optic discs, even in the presence of neuro-ophthalmological pathologies. Although this may seem like an elementary task, this study demonstrates the power of transfer learning and provides an example of a DCNN that can help curate large medical image databases for machine-learning purposes and facilitate ophthalmologist workflow by automatically labeling images according to laterality.

9.
NPJ Digit Med ; 2: 24, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31304371

RESUMO

In any community, the key to understanding the burden of a specific condition is to conduct an epidemiological study. The deep learning system (DLS) recently showed promising diagnostic performance for diabetic retinopathy (DR). This study aims to use DLS as the grading tool, instead of human assessors, to determine the prevalence and the systemic cardiovascular risk factors for DR on fundus photographs, in patients with diabetes. This is a multi-ethnic (5 races), multi-site (8 datasets from Singapore, USA, Hong Kong, China and Australia), cross-sectional study involving 18,912 patients (n = 93,293 images). We compared these results and the time taken for DR assessment by DLS versus 17 human assessors - 10 retinal specialists/ophthalmologists and 7 professional graders). The estimation of DR prevalence between DLS and human assessors is comparable for any DR, referable DR and vision-threatening DR (VTDR) (Human assessors: 15.9, 6.5% and 4.1%; DLS: 16.1%, 6.4%, 3.7%). Both assessment methods identified similar risk factors (with comparable AUCs), including younger age, longer diabetes duration, increased HbA1c and systolic blood pressure, for any DR, referable DR and VTDR (p > 0.05). The total time taken for DLS to evaluate DR from 93,293 fundus photographs was ~1 month compared to 2 years for human assessors. In conclusion, the prevalence and systemic risk factors for DR in multi-ethnic population could be determined accurately using a DLS, in significantly less time than human assessors. This study highlights the potential use of AI for future epidemiology or clinical trials for DR grading in the global communities.

10.
Prog Retin Eye Res ; 72: 100759, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31048019

RESUMO

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally.

11.
Ophthalmol Retina ; 2019 Nov 09.
Artigo em Inglês | MEDLINE | ID: mdl-31953109

RESUMO

PURPOSE: To describe the 12-month outcomes of treatment-naïve eyes with choroidal neovascularization (CNV) resulting from age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) after initiation of intravitreal anti-vascular endothelial growth factor (VEGF) monotherapy or combination therapy with verteporfin photodynamic therapy (PDT). DESIGN: A 12-month single-center, retrospective, comparative, nonrandomized cohort study. PARTICIPANTS: Patients with AMD or PCV who initiated intravitreal anti-VEGF therapy during 2015. METHODS: Demographics, visual outcomes, OCT, and treatment data were collected at baseline and months 1, 3, 6, and 12 after treatment initiation. Multivariate analysis was performed to identify baseline features predictive of visual maintenance and improvement after 12 months of treatment. MAIN OUTCOME MEASURES: Primary end point was visual acuity (VA) change from baseline to month 12. Secondary end points were treatment exposure and change in central subfield thickness on OCT. RESULTS: A total of 364 patients (165 AMD and 199 PCV) were included. Baseline vision was 41 and 43 logarithm of the minimum angle of resolution (logMAR) letters for AMD and PCV patients, respectively. Patients with AMD and PCV received 5.5 and 5.3 injections (5.0 monotherapy vs. 5.6 combination therapy; mean, 1.2 PDT sessions), respectively. Patients with AMD gained 4.7 logMAR letters after 12 months (P = 0.002), whereas PCV patients gained 6.6 logMAR letters (P = 0.001) and 10.8 logMAR letters (P < 0.001) for monotherapy and combination therapy, respectively. Only patients with presenting VA of fewer than 35 letters (Snellen equivalent, 6/60) achieved significant visual improvement (10.4 letters for AMD, 17.1 letters for PCV with monotherapy, and 35.5 letters for PCV with combination therapy). Predictors of VA gain included number of intravitreal injections (AMD and PCV adjusted odds ratio, 12.1 [P = 0.001] and 12.5 [P = 0.004] for ≥7 injections, respectively) and baseline VA of 20 logMAR letters or fewer (adjusted odds ratio, 3.8 and 10.6 for AMD and PCV, respectively). Age, gender, race, use of PDT or focal laser therapy, and central subfield thickness were not predictive of significant visual gain at 12 months. CONCLUSIONS: In Asian patients, treatment of AMD with anti-VEGF therapy yielded 12-month visual outcomes comparable with those of other real-world studies from Western populations but poorer than those of controlled trials. In contrast, for PCV eyes, anti-VEGF monotherapy and combination therapy with PDT yielded comparable outcomes as those of controlled clinical trials.

16.
Retina ; 38(8): 1509-1517, 2018 08.
Artigo em Inglês | MEDLINE | ID: mdl-28704255

RESUMO

PURPOSE: To investigate the influence of choroidal vascular hyperpermeability (CVH) and choroidal thickness on treatment outcomes in eyes with polypoidal choroidal vasculopathy (PCV) undergoing anti-vascular endothelial growth factor monotherapy or combination therapy of photodynamic therapy and anti-vascular endothelial growth factor injections. METHODS: The authors performed a prospective, observational cohort study involving 72 eyes of 72 patients with polypoidal choroidal vasculopathy (mean age 68.6 years, 51% men) treated with either monotherapy (n = 41) or combination therapy (n = 31). Each eye was imaged with color fundus photography, fluorescent angiography, indocyanine green angiography, and spectral domain optical coherence tomography. Indocyanine green angiography images were used to evaluate CVH, and spectral domain optical coherence tomography was used to measure central choroidal thickness. Changes in visual acuity over 12 months, and number of anti-vascular endothelial growth factor injections were investigated. RESULTS: Choroidal vascular hyperpermeability was present in 31 eyes (43.1%). Visual acuity change over 12 months was numerically better in the CVH group compared with the CVH (-) group (-0.099 and -0.366 logarithm of the minimal angle of resolution unit in the CVH (-) and CVH (+) groups, respectively, multivariate P = 0.063) and significantly better in a matched pair analysis (P = 0.033). Furthermore, in the combination therapy group, the number of injection was significantly lower in the CVH (+) group compared with the CVH (-) group (4.68 vs. 2.58 injections/year in the CVH (-) and CVH (+) groups; P = 0.0044). There was no significant relationship between treatment response and choroidal thickening. CONCLUSION: The presence of CVH is associated with better visual outcome in eyes with polypoidal choroidal vasculopathy and lower injection number in combination therapy. Thus, CVH, but not choroidal thickness, should be further evaluated as a potential biomarker for selecting patients for combination therapy.


Assuntos
Inibidores da Angiogênese/uso terapêutico , Neovascularização de Coroide/tratamento farmacológico , Degeneração Macular/tratamento farmacológico , Fotoquimioterapia/métodos , Fármacos Fotossensibilizantes/uso terapêutico , Porfirinas/uso terapêutico , Ranibizumab/uso terapêutico , Idoso , Idoso de 80 Anos ou mais , Corioide/diagnóstico por imagem , Neovascularização de Coroide/diagnóstico por imagem , Neovascularização de Coroide/patologia , Quimioterapia Combinada , Feminino , Angiofluoresceinografia , Humanos , Injeções Intravítreas , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Estudos Prospectivos , Tomografia de Coerência Óptica , Verteporfina
18.
Ophthalmic Epidemiol ; 23(4): 209-22, 2016 08.
Artigo em Inglês | MEDLINE | ID: mdl-27355693

RESUMO

PURPOSE: Diabetes is a major public health problem affecting 415 million people worldwide. With the increasing prevalence of diabetes, diabetic retinopathy (DR) is emerging as the leading cause of avoidable blindness worldwide. METHODS: We reviewed previous and recent literature to provide an overview of emerging trends on the burden, epidemiology, risk factors, and prevention of DR. RESULTS: First, there is clear evidence of a global increase in the prevalence of diabetes. Second, there is a decline in the incidence of blindness due to DR, particularly in developed countries. Third, diabetic macular edema (DME) rather than proliferative diabetic retinopathy (PDR) is the increasingly common cause of visual impairment. Fourth, DR awareness remains patchy and low in most populations. Fifth, hyperglycemia remains the most consistent risk factor for DR in type 1 diabetes across different studies and populations. Sixth, in contrast, blood pressure is an important risk factor for DR in type 2 diabetes. Seventh, the relationship between dyslipidemia and DR remains unclear, with inconsistent results from different studies and trials. Eighth, the utility of predictive models incorporating multiple risk factors for assessing DR risk requires evaluation. Ninth, photographic screening of DR using tele-ophthalmology platforms is increasingly recognized as being feasible and cost-effective. Finally, DR prevention in low-resource settings cannot follow models developed in high-resource countries and requires different strategies. CONCLUSIONS: The ten trends we observed in the current review may guide planning of public healthcare strategies for the management of DR and prevention of blindness.


Assuntos
Diabetes Mellitus Tipo 2/epidemiologia , Retinopatia Diabética/epidemiologia , Diabetes Mellitus Tipo 1/epidemiologia , Retinopatia Diabética/prevenção & controle , Feminino , Humanos , Incidência , Edema Macular/epidemiologia , Masculino , Prevalência , Fatores de Risco
19.
Clin Exp Ophthalmol ; 43(9): 815-9, 2015 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-26183457

RESUMO

BACKGROUND: Swept source optical coherence tomography (SS-OCT, Topcon Medical System, Japan) is known to have longer wavelength than spectral domain OCT (SD-OCT, Spectralis, Heidelberg Engineering, Germany), allowing a deeper penetration into retina and choroidal layers. This objective of this study was to compare the visibility of retinal and choroidal features in polypoidal choroidal vasculopathy (PCV) using SS-OCT and SD-OCT. DESIGN: This study employs prospective comparative observational case series in Singapore National Eye Center. PARTICIPANTS: There were 20 eyes (20 patients) with PCV confirmed with indocyanine green angiogram. METHODS: Six pre-specified OCT parameters (presence of polyps, sharp pigment epithelial detachment [PED] peak, notched PED and visibility of full maximum height of PED, inner segment/outer segment [IS/OS] line and choroid-scleral interface [CSI]) were graded using SS-OCT and SD-OCT. MAIN OUTCOME MEASURES: The Kappa statistics between the two imaging modalities were calculated. RESULTS: Both SS-OCT and SD-OCT were able to detect polypoidal lesions in the majority of eyes (90% and 85%, respectively). However, SS-OCT had better detection for CSI and IS/OS lines (CSI: 80% vs 45%, P = 0.05; IS/OS line: 65% vs 45%, P = 0.34). For sharp PED peak, notched PED, ability to visualize the full PED height and retinal pigment epithelial line, both OCT machines were able to detect in ≥80% of the eyes. CONCLUSION: In conclusion, SS-OCT and SD-OCT appeared to be similarly effective at detecting most features associated with PCV. However, SS-OCT is more superior in detecting the CSI.


Assuntos
Corioide/patologia , Neovascularização de Coroide/diagnóstico , Pólipos/diagnóstico , Retina/patologia , Tomografia de Coerência Óptica , Idoso , Corantes/administração & dosagem , Feminino , Angiofluoresceinografia , Humanos , Verde de Indocianina/administração & dosagem , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Tomografia de Coerência Óptica/instrumentação
20.
Clin Exp Ophthalmol ; 40(1): e40-6, 2012.
Artigo em Inglês | MEDLINE | ID: mdl-22044677

RESUMO

BACKGROUND: To validate the use of an economical portable multipurpose ophthalmic imaging device, EyeScan (Ophthalmic Imaging System, Sacramento, CA, USA), for diabetic retinopathy screening. DESIGN: Evaluation of a diagnostic device. PARTICIPANTS: One hundred thirty-six (272 eyes) were recruited from diabetic retinopathy screening clinic of Royal Perth Hospital, Western Australia, Australia. METHODS: All patients underwent three-field (optic disc, macular and temporal view) mydriatic retinal digital still photography captured by EyeScan and FF450 plus (Carl Zeiss Meditec, North America) and were subsequently examined by a senior consultant ophthalmologist using the slit-lamp biomicroscopy (reference standard). All retinal images were interpreted by a consultant ophthalmologist and a medical officer. MAIN OUTCOME MEASURES: The sensitivity, specificity and kappa statistics of EyeScan and FF450 plus with reference to the slit-lamp examination findings by a senior consultant ophthalmologist. RESULTS: For detection of any grade of diabetic retinopathy, EyeScan had a sensitivity and specificity of 93 and 98%, respectively (ophthalmologist), and 92 and 95%, respectively (medical officer). In contrast, FF450 plus images had a sensitivity and specificity of 95 and 99%, respectively (ophthalmologist), and 92 and 96%, respectively (medical officer). The overall kappa statistics for diabetic retinopathy grading for EyeScan and FF450 plus were 0.93 and 0.95 for ophthalmologist and 0.88 and 0.90 for medical officer, respectively. CONCLUSIONS: Given that the EyeScan requires minimal training to use and has excellent diagnostic accuracy in screening for diabetic retinopathy, it could be potentially utilized by the primary eye care providers to widely screen for diabetic retinopathy in the community.


Assuntos
Retinopatia Diabética/diagnóstico , Técnicas de Diagnóstico Oftalmológico/instrumentação , Retinopatia Diabética/etnologia , Humanos , Programas de Rastreamento , Pessoa de Meia-Idade , Fotografação/instrumentação , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Índice de Gravidade de Doença , Acuidade Visual/fisiologia
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