ABSTRACT
BACKGROUND: Diabetic retinopathy (DR) is a leading cause of blindness. Our objective was to evaluate the performance of an artificial intelligence (AI) system integrated into a handheld smartphone-based retinal camera for DR screening using a single retinal image per eye. METHODS: Images were obtained from individuals with diabetes during a mass screening program for DR in Blumenau, Southern Brazil, conducted by trained operators. Automatic analysis was conducted using an AI system (EyerMaps™, Phelcom Technologies LLC, Boston, USA) with one macula-centered, 45-degree field of view retinal image per eye. The results were compared to the assessment by a retinal specialist, considered as the ground truth, using two images per eye. Patients with ungradable images were excluded from the analysis. RESULTS: A total of 686 individuals (average age 59.2 ± 13.3 years, 56.7% women, diabetes duration 12.1 ± 9.4 years) were included in the analysis. The rates of insulin use, daily glycemic monitoring, and systemic hypertension treatment were 68.4%, 70.2%, and 70.2%, respectively. Although 97.3% of patients were aware of the risk of blindness associated with diabetes, more than half of them underwent their first retinal examination during the event. The majority (82.5%) relied exclusively on the public health system. Approximately 43.4% of individuals were either illiterate or had not completed elementary school. DR classification based on the ground truth was as follows: absent or nonproliferative mild DR 86.9%, more than mild (mtm) DR 13.1%. The AI system achieved sensitivity, specificity, positive predictive value, and negative predictive value percentages (95% CI) for mtmDR as follows: 93.6% (87.8-97.2), 71.7% (67.8-75.4), 42.7% (39.3-46.2), and 98.0% (96.2-98.9), respectively. The area under the ROC curve was 86.4%. CONCLUSION: The portable retinal camera combined with AI demonstrated high sensitivity for DR screening using only one image per eye, offering a simpler protocol compared to the traditional approach of two images per eye. Simplifying the DR screening process could enhance adherence rates and overall program coverage.
ABSTRACT
This study aimed to measure the prevalence of Problematic Internet Use (PIU) among high school students and to identify factors associated with PIU underlining gender differences. The students filled a self-administered, anonymous questionnaire collecting information on demographic characteristics and patterns of Internet use. Multiple logistic regression analysis was performed to identify factors associated with PIU in the overall sample and by gender. Twenty-five schools and 2022 students participated in the survey. Prevalence of PIU was 14.2% among males and 10.1% among females. Males 15-year-olds and females 14-year-olds had the highest PIU prevalence that progressively lowered with age among females. Only 13.5% of pupils declared parents controlled their Internet use. The sensation of feeling lonely, the frequency of use, the number of hours of connection, and visiting pornographic websites were associated with the risk of PIU in both genders. Attending vocational schools, the activities of chatting and file downloading, and the location of use at Internet point among males, and younger age among females were associated with PIU, whilst information searching was protective among females. PIU could become a public health problem in the next years. The physical and mental health consequences should be studied.