Diagnostic Ability of a Smartphone App for Dry Eye Disease: Protocol for a Multicenter, Open-Label, Prospective, and Cross-sectional Study

Abstract

Background Dry eye disease (DED) is one of the most common ocular surface diseases. Numerous patients with DED remain undiagnosed and inadequately treated, experiencing various subjective symptoms and a decrease in quality of life and work productivity. A mobile health smartphone app, namely, the DEA01, has been developed as a noninvasive, noncontact, and remote screening device, in the context of an ongoing paradigm shift in the health care system, to facilitate a diagnosis of DED. Objective This study aimed to evaluate the capabilities of the DEA01 smartphone app to facilitate a DED diagnosis. Methods In this multicenter, open-label, prospective, and cross-sectional study, the test method will involve using the DEA01 smartphone app to collect and evaluate DED symptoms, based on the Japanese version of the Ocular Surface Disease Index (J-OSDI), and to measure the maximum blink interval (MBI). The standard method will then involve a paper-based J-OSDI evaluation of subjective symptoms of DED and tear film breakup time (TFBUT) measurement in an in-person encounter. We will allocate 220 patients to DED and non-DED groups, based on the standard method. The primary outcome will be the sensitivity and specificity of the DED diagnosis according to the test method. Secondary outcomes will be the validity and reliability of the test method. The concordance rate, positive and negative predictive values, and the likelihood ratio between the test and standard methods will be assessed. The area under the curve of the test method will be evaluated using a receiver operating characteristic curve. The internal consistency of the app-based J-OSDI and the correlation between the app-based J-OSDI and paper-based J-OSDI will be assessed. A DED diagnosis cutoff value for the app-based MBI will be determined using a receiver operating characteristic curve. The app-based MBI will be assessed to determine a correlation between a slit lamp–based MBI and TFBUT. Adverse events and DEA01 failure data will be collected. Operability and usability will be assessed using a 5-point Likert scale questionnaire. Results Patient enrollment will start in February 2023 and end in July 2023. The findings will be analyzed in August 2023, and the results will be reported from March 2024 onward. Conclusions This study may have implications in identifying a noninvasive, noncontact route to facilitate a diagnosis of DED. The DEA01 may enable a comprehensive diagnostic evaluation within a telemedicine setting and facilitate early intervention for undiagnosed patients with DED confronting health care access barriers. Trial Registration Japan Registry of Clinical Trials jRCTs032220524; https://jrct.niph.go.jp/latest-detail/jRCTs032220524 International Registered Report Identifier (IRRID) PRR1-10.2196/45218

Clinical utility of maximum blink interval measured by smartphone application DryEyeRhythm to support dry eye disease diagnosis

“…MBI cutoff values from prior studies were entirely derived under slit lamp-based measurements 15 , 16 . To enhance the assessment of different diagnostic capabilities of MBI and encourage its utilization on mobile platforms, future research should employ methodologies that effectively explore the optimal cutoff value for app-based MBI as a primary outcome 10 .…”

“…The demand for non-invasive and non-contact examinations and the incorporation of telemedicine in routine practice have rapidly increased with the novel coronavirus disease (COVID-19) pandemic 9 , 10 . DED is diagnosed by evaluating subjective symptoms and objective findings on examinations, such as TFBUT and ocular surface staining 11 , 12 .…”

Interobserver Reliability of Tear Break-Up Time Examination Using “Smart Eye Camera” in Indonesian Remote Area

“…The average time taken to perform the OSDI test was 74.11 ± 28.61 seconds and 215.14 ± 65.27 seconds for TBUT. 24 The average time spent performing both tests was significantly lower compared to the time taken to perform the Schirmer test. The results of this study suggest that the diagnosis of DED is more reliable and less time-consuming if OSDI and TBUT are performed compared to the Schirmer test.…”

“…These data support previous research statements that both subjective and objective indicators should be used to improve the accuracy of DED diagnosis. 20,25 The use of mobile apps in DED diagnosis was discussed earlier in a recent research protocol by Nagino et al 24 Their study use DEA01 smartphone apps (InnoJin, Inc., DEA01, Tokyo, Kanto, Japan) to measure subjective symptoms using J-OSDI and maximum blink interval (MBI) as test method for DED screening only. 5,26 The biggest differences and value compared to our study are we followed the standard method by combining TBUT and OSDI for DED diagnosis.…”

2022 Glenn A. Fry Award lecture: Enhancing clinical assessment for improved ophthalmic management

“…138,139 In addition, blue (to assess fluorescein staining) 156 and infrared (to allow visualization of the meibomian glands after lid everting) 299 light modes are usually available. 300 Apps are being developed to allow some of these measures (such as symptomatology, 301 functional dynamic visual acuity, 302 and tear meniscus height imaging) 303 to be conducted on widely available smartphones; this is an area where more technological innovation is expected in the near future, including the use of artificial intelligence to quantify physiological change. 139,304 For example, we developed a simple digital test that can be presented on a smartphone (Fig.…”