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(every 1 hour): 1.02 (1.01-1.03), and smoking: 1.53 (1.31-1.79). CL use, smoking, and screen time are modifiable risk factors that may prevent disease progression and improve long-term prognosis for DED patients.(A) The hierarchical heat map on the correlation between individual symptoms of dry eye disease and depression. (B) Stratified clusters using dimension reduction algorism, tSNE, for contact lens-related dry eye based on the subjective symptom data collected by a mobile health application. (C) Hierarchical heat map of the correlation between the clusters identified by the t-SNE projection. SDS: Zung Self-rating Depression Scale, tSNE: t-distributed Stochastic Neighbor Embedding, OSDI: Ocular Surface Disease Index, N/A: not applicable. Figure 2A is taken from Inomata T. et al.8) and 2B and 2C taken from Inomata T. et al.14) with permission.Another of our follow-up studies revealed that many individuals experiencing DED symptoms remained undiagnosed12). This study targeted undi-agnosed Japanese users who had downloaded DryEyeRhythm between November 2016 and January 2018. It investigated distinct risk factors and characteristics of potentially undiagnosed DED patients12). The application was downloaded 18,891 times, yielding 21,394 individual profiles and DED data, and 4,454 users were ultimately included in the study. Amongst this, 53.8% (2,395 users) met the diagnostic criteria of DED without an official diagnosis from a healthcare provider. Notable odds ratio of DED-related factors pertaining to undiag-nosed individuals include younger age (every 1 year): 0.99 (0.987-0.999), male sex: 1.99 (1.61-2.46), absence of collagen disease: 0.23 (0.09-0.60), absence of mental illnesses (other than depression and schizophrenia): 0.50 (0.36-0.69), absence of ophthalmic surgery (other than cataract surgery and laser-as-sisted in situ keratomileusis): 0.41 (0.27-0.64), absence of current use of CL: 0.64 (0.54-0.77), absence of previous use of CL: 0.45 (0.34-0.58). These unique characteristics of undiagnosed individuals are crucial in the initial triaging of the suspected DED patients, especially for those with atypical or “non- Figure 2　Mobile health-based data visualizationspecific” symptoms of DED. Effective screening and early diagnosis of the undiagnosed population will lead to a better treatment efficiency and prog-nosis. It may also positively impact population health and minimize societal costs on DED treatment.The societal climate advocating the importance of mental health and quality of life has received attention in recent years28). Notably, DED and depression share common risk factors concerning hormonal imbalance, metabolic defect, and neuro-psychiatric dysfunction, leading to increased comor-bidity suspicions between the two diseases13, 29, 30). Our results indicate that the severity of DED is positively correlated with the likelihood of ongoing depressive symptoms18). The odds ratio of depres-sive symptoms (SDS score≥40) for users with severe DED symptoms was 3.29 (2.70-4.00). A subsequent AI-based hierarchical cluster heatmap of the indi-vidual items of OSDI (12 items) and SDS (20 items) enabled a comprehensive visualization of correlated DED and depressive symptoms (Figure 2A). Interestingly, subjective symptoms of DED related to environmental factors (OSDI items 10-12) were associated with depressive symptoms. With DED symptom monitoring through smart-phone applications, it is possible to flag patients with increased risk of depression and recommend proper screening and intervention from healthcare providers. Moreover, it can assist in the prevention, early detection, and treatment of depression. Imple-menting of mHealth-driven big data collection, 523