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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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Anthropometric parameters and catch-up growth were prospectively evaluated in fifty late-diagnosed children with coeliac disease aged 2·25–10 years after 1–4 years of adhering to a strict gluten-free diet (GFD). The anthropometric parameters were expressed as Z scores relative to National Centre for Health Statistics standards using Epi Info 2000 (weight-for-height Z score (WHZ) and height-for-age Z score (HAZ)). Catch-up growth was evaluated by repeated measures. ANOVA, overall significance by an F test and pair-wise comparisons for estimated marginal means using the least significant difference. At the time of enrolment, no significant difference was observed in WHZ and HAZ between children diagnosed before (group 1) or after (group 2) 4 years of age. On follow-up, HAZ was significantly higher in group 1 after the first and third years of the GFD (P=0·04 and 0·02, respectively), with a non-significant increase after completing 4 years of the GFD (P=0·22). Feeding the GFD resulted in an overall significant (F=3·99, P=0·011) increase in HAZ up to 4 years of follow-up. However, the catch-up in height was incomplete, with stunting in sixteen (55·4 %) of twenty-nine children after 3 years and in seven (46·6 %) of fifteen children after 4 years on the GFD. Pair-wise comparisons demonstrated a linear catch-up growth during the initial follow-up on GFD. Treatment with the GFD did not result in an overall significant increase in WHZ up to 4 years of follow-up (F=1·01, P=0·42). Our results suggest that, in children with late-diagnosed coeliac disease, treatment with a GFD leads to a normalisation of body mass and a significant but incomplete recovery in HAZ during 4 years of follow-up.