Applications of Artificial Intelligence in Diagnosis of Dry Eye Disease: A Systematic Review and Meta-Analysis

Abstract

Purpose: Clinical diagnosis of dry eye disease is based on a subjective Ocular Surface Disease Index questionnaire or various objective tests, however, these diagnostic methods have several limitations. Methods: We conducted a comprehensive review of articles discussing various applications of artificial intelligence (AI) models in the diagnosis of the dry eye disease by searching PubMed, Web of Science, Scopus, and Google Scholar databases up to December 2022. We initially extracted 2838 articles, and after removing duplicates and applying inclusion and exclusion criteria based on title and abstract, we selected 47 eligible full-text articles. We ultimately selected 17 articles for the meta-analysis after applying inclusion and exclusion criteria on the full-text articles. We used the Standards for Reporting of Diagnostic Accuracy Studies to evaluate the quality of the methodologies used in the included studies. The performance criteria for measuring the effectiveness of AI models included area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. We calculated the pooled estimate of accuracy using the random-effects model. Results: The meta-analysis showed that pooled estimate of accuracy was 91.91% (95% confidence interval: 87.46–95.49) for all studies. The mean (±SD) of area under the receiver operating characteristic curve, sensitivity, and specificity were 94.1 (±5.14), 89.58 (±6.13), and 92.62 (±6.61), respectively. Conclusions: This study revealed that AI models are more accurate in diagnosing dry eye disease based on some imaging modalities and suggested that AI models are promising in augmenting dry eye clinics to assist physicians in diagnosis of this ocular surface condition.