ABSTRACT
PURPOSE: The aim of the study was to analyse the reliability of an offline artificial intelligence (AI) algorithm for community screening of diabetic retinopathy. METHODS: A total of 1378 patients with diabetes visiting public dispensaries under the administration of the Municipal Corporation of Greater Mumbai between August 2018 and September 2019 were enrolled for the study. Fundus images were captured by non-specialist operators using a smartphone-based camera covering the posterior pole, including the disc and macula, and the nasal and temporal fields. The offline AI algorithm on the smartphone marked the images as referable diabetic retinopathy (RDR) or non-RDR, which were then compared against the grading by two vitreoretinal surgeons to derive upon the sensitivity and specificity of the algorithm. RESULTS: Out of 1378 patients, gradable fundus images were obtained and analysed for 1294 patients. The sensitivity and specificity of diagnosing RDR were 100% (95% CI: 94.72-100.00%) and 89.55% (95% CI: 87.76-91.16%), respectively; the same values for any diabetic retinopathy (DR) were 89.13% (95% CI: 82.71-93.79%) and 94.43% (95% CI: 91.89-94.74%), respectively, with no false-negative results. CONCLUSION: The robustness of the offline AI algorithm was established in this study making it a reliable tool for community-based DR screening.
Subject(s)
Diabetes Mellitus , Diabetic Retinopathy , Artificial Intelligence , Diabetic Retinopathy/diagnosis , Humans , Mass Screening , Photography , Reproducibility of Results , SmartphoneABSTRACT
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.
ABSTRACT
OBJECTIVES: The aims of the study were to estimate the prevalence of diabetic retinopathy (DR) and enumerate history-based risk factors in the urban slums of Western India. METHODS: The population-based study was conducted in seven wards of Mumbai urban slums, where we screened 6569 subjects of ≥ 40 years age, with a response rate of 98.4%, for type 2 diabetes mellitus (T2DM) based on American Diabetes Association criteria. All subjects with T2DM underwent dilated 30° seven-field stereo-fundus-photography for DR severity grading based on modified Airlie House classification. A multivariate logistic regression model was used to assess the correlation of DR with the history-based risk factors. RESULTS: The prevalence of DR in the general population of Mumbai urban slums was 1.41% (95% CI 0.59-2.23) and in the T2DM population it was 15.37% (95% CI 8.87-21.87). The positive associations with DR were the longer duration of DM (≥ 11 years: OR, 12.77; 95% CI 2.93-55.61) and male gender (OR, 2.05; 95% CI 1.08-3.89); increasing severity of retinopathy was also significantly associated with longer duration of DM (p < 0.001). However, history of hypertension, family history of DM, consanguineous marriage and migration status were not associated with DR in the study population. CONCLUSIONS: The prevalence of DR in the general population and T2DM subjects were 1.41% and 15.37% respectively in Mumbai urban slums. Duration of DM and male gender were significantly associated with DR. The slums in Western India show the trends of urban lifestyle influences similar to the rest of urban India.
