Patient Perceptions of Eye Disease and Treatment in Bihar India

Lundquist, M. Brayden; Sharma, Nishant; Kewalramani, Kirti

doi:10.4172/2155-9570.1000213

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…In a developing country such as India, nearly 70% of the population resides in rural areas, and a ratio of only 1 ophthalmologist per 100 000 people is available for the care of the entire population. This study shows how an offline AI algorithm can help address this lack of specialist access through automatic, instant grading of the retinal images, highlighting a possible solution for implementation of large-scale models for screening for RDR.…”

Section: Discussionmentioning

confidence: 99%

“…6,7,12 Nevertheless, to estimate the specificity and sensitivity with a lower margin of error of less than 3% (at the 95% CI), a larger sample size of nearly 1050 patients will be needed, assuming a prevalence of up to 20% of DR in the Mumbai population. 13 Another drawback is that the current version of the offline AI does not permit grading of retinopathy according to the International Clinical DR severity scale or the National Health Service classification. Hence, this study is unable to assess the sensitivity and specificity of the offline AI for detection of individual grades of DR.…”

Section: Limitationsmentioning

confidence: 99%

“…Seven-Segment Early Treatment Diabetic Retinopathy Study Protocol Diagnostic Accuracy of Diabetic Retinopathy Screening With Offline Artificial Intelligence on a SmartphoneAt the 95% CI, and a population of 18.4 million in Mumbai (census of 2011), with a maximum DR prevalence of 20%,13 this resulted in a minimum sample size of 200 patients.…”

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confidence: 99%

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Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone

et al. 2019

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IMPORTANCE Offline automated analysis of retinal images on a smartphone may be a cost-effective and scalable method of screening for diabetic retinopathy; however, to our knowledge, assessment of such an artificial intelligence (AI) system is lacking. OBJECTIVE To evaluate the performance of Medios AI (Remidio), a proprietary, offline, smartphone-based, automated system of analysis of retinal images, to detect referable diabetic retinopathy (RDR) in images taken by a minimally trained health care worker with Remidio Non-Mydriatic Fundus on Phone, a smartphone-based, nonmydriatic retinal camera. Referable diabetic retinopathy is defined as any retinopathy more severe than mild diabetic retinopathy, with or without diabetic macular edema. DESIGN, SETTING, AND PARTICIPANTS This prospective, cross-sectional, population-based study took place from August 2018 to September 2018. Patients with diabetes mellitus who visited various dispensaries administered by the Municipal Corporation of Greater Mumbai in Mumbai, India, on a particular day were included. INTERVENTIONS Three fields of the fundus (the posterior pole, nasal, and temporal fields) were photographed. The images were analyzed by an ophthalmologist and the AI system. MAIN OUTCOMES AND MEASURES To evaluate the sensitivity and specificity of the offline automated analysis system in detecting referable diabetic retinopathy on images taken on the smartphone-based, nonmydriatic retinal imaging system by a health worker. RESULTS Of 255 patients seen in the dispensaries, 231 patients (90.6%) consented to diabetic retinopathy screening. The major reasons for not participating were unwillingness to wait for screening and the blurring of vision that would occur after dilation. Images from 18 patients were deemed ungradable by the ophthalmologist and hence were excluded. In the remaining participants (110 female patients [51.6%] and 103 male patients [48.4%]; mean [SD] age, 53.1 [10.3] years), the sensitivity and specificity of the offline AI system in diagnosing referable diabetic retinopathy were 100.0% (95% CI, 78.2%-100.0%) and 88.4% (95% CI, 83.2%-92.5%), respectively, and in diagnosing any diabetic retinopathy were 85.2% (95% CI, 66.3%-95.8%) and 92.0% (95% CI, 97.1%-95.4%), respectively, compared with ophthalmologist grading using the same images. CONCLUSIONS AND RELEVANCE These pilot study results show promise in the use of an offline AI system in community screening for referable diabetic retinopathy with a smartphone-based fundus camera. The use of AI would enable screening for referable diabetic retinopathy in remote areas where services of an ophthalmologist are unavailable. This study was done on patients with diabetes who were visiting a dispensary that provides curative services to the population at the primary level. A study with a larger sample size may be needed to extend the results to general population screening, however.

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Section: Discussionmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

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Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone

et al. 2019

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“…32 More importantly, understanding the seriousness of the disease and accepting the available treatment would not be possible. 33 Table 1 shows that the North West and North East GPZ, compared to other GPZs, have a higher prevalence of blindness (4.8% and 6.1%, respectively), and these two zones have been reported to have the lowest adult literacy rates among men and women. 30 Due to economic and social reasons, most illiterates cannot afford eye care services and most of the time they depend on other well-to-do family members for financial support and decision-making.…”

Section: Literacymentioning

confidence: 99%

Vision health disparities in blindness and visual impairment in Nigeria: A review of the Nigerian National Blindness and Visual Impairment Survey

Akano

2017

African Vision and Eye Health

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Purpose: Blindness and visual impairment have become public health problems with prevalence increasing year after year. Nigeria, the most populated country in Africa, is also very diverse in terms of geographical location, ethnicity and culture. This study looks at and considers the vision health disparities in blindness and visual impairment in Nigeria using socio-demographic factors such as gender, geopolitical zones, place of residence and literacy.Methods: A comprehensive literature search was conducted on PubMed and Google Scholar databases from May 2014 to May 2015. The search included articles from 2001 to May 2015 as well as a review of the Nigerian National Blindness and Visual Impairment Survey.Results: The male dominance culture and lower literacy levels among women in Nigeria have led to a higher prevalence of blindness and visual impairment among women compared to men. In Nigeria, eye diseases that lead to blindness and visual impairment occur more in certain geopolitical zones and ecological regions than others. More Nigerians live in remote rural areas, with little or no access to health care, rather than in urban areas where there are more eye care practitioners and better facilities for care.Conclusion: Differences in gender, geopolitical zones, place of residence and literacy are responsible for existing vision health disparities in blindness and visual impairment in Nigeria.

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“…It gives accurate, detailed and quantifiable information about the retina. However, the number of Ophthalmologists is very less compared to the number of Diabetic patients an estimated figure being 1 per million population [2].The ratio is further poor in rural areas of India. In this scenario and with advancement in the field of medical imaging along with machine learning, artificial intelligence, an automated system for diagnosis of condition of retina is highly desirable as it helps the ophthalmologists in the quick and easy diagnosis of ME.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Machine Learning Techniques for Macula Edema Detection

Prabhu¹,

Bhoir²,

Rao³

2019

IJCA

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Macula Edema is an abnormality in the retina seen in patients with prolonged diabetes. If left untreated, it can cause vision loss. Macula Edema is characterized by swelling of macula or proximity of surrogate exudates to the fovea. Ophthalmologists use subjective approach to diagnose Macula Edema and normally perform pupil dilation which causes inconvenience to the patients. Moreover this procedure is time consuming and laborious. Instead of using this conventional method based on surrogates which are exudates, the paper has concentrated on the exclusive features that represent macula swelling. A total of 23 such features are extracted. Support Vector Machine and Random Forest (RF) classifiers are used for detection of Macula Edema for the chosen database. It was found that the RF algorithm performed better with an accuracy of 80.95 % in comparison with SVM at 71.43 %.

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Patient Perceptions of Eye Disease and Treatment in Bihar India

Cited by 7 publications

References 12 publications

Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone

Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone

Vision health disparities in blindness and visual impairment in Nigeria: A review of the Nigerian National Blindness and Visual Impairment Survey

Effectiveness of Machine Learning Techniques for Macula Edema Detection

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