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Objectives: The challenges in diagnosing keratoconus (KC) have led researchers to explore the use of artificial intelligence (AI) as a diagnostic tool. AI has emerged as a new way to improve the efficiency of KC diagnosis. This study analyzed the use of AI as a diagnostic modality for KC. Methods: This study used a systematic review and meta-analysis following the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched selected databases using a combination of search terms: "((Artificial Intelligence) OR (Diagnostic Modality)) AND (Keratoconus)" from PubMed, Medline, and ScienceDirect within the last 5 years (2018-2023). Following a systematic review protocol, we selected 11 articles and 6 articles were eligible for final analysis. The relevant data were analyzed with Review Manager 5.4 software and the final output was presented in a forest plot. Results: This research found neural networks as the most used AI model in diagnosing KC. Neural networks and naïve bayes showed the highest accuracy of AI in diagnosing KC with a sensitivity of 1.00, while random forests were >0.90. All studies in each group have proven high sensitivity and specificity over 0.90. Conclusions: AI potentially makes a better diagnosis of the KC with its high performance, particularly on sensitivity and specificity, which can help clinicians make medical decisions about an individual patient.
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INTRODUCTION: Families, especially parents, play an important role in identifying their children's talents and directing their educational journey. The role of parents in their children career applies both to collectivist and to noncollectivist culture. AIM: To examine the correlation between parental influences on medical students' self-efficacy and career exploration in collectivist culture. METHODS: This research was a quantitative study. The study involved 1017 medical students of medical faculties in four faculties of medicine in Indonesia. All data were collected online in August 2018. The study was conducted using an online survey questionnaire and analyzed to finally form a model that displays the determinants of career exploration behavior. The data were analyzed using the maximum likelihood estimation in IBM AMOS 24. RESULTS: The results found a model that has various routes toward career exploration in collectivist culture. Path analysis revealed both direct and direct effect toward the variable studied. Parents' expectations had influence on self-efficacy. CONCLUSION: The findings show the important role between the influence of parents expectations for self-efficacy and career exploration in children.