New App DryEyeRhythm a reliable, valid, and non-invasive method to assess dry eye disease

Dry eye disease (DED) has become increasingly common in recent years and is characterized by different symptoms, including dryness, ocular discomfort, fatigue, and visual disturbances. Further undiagnosed and untreated DED can lead to a variety of symptoms, including ocular fatigue, sensitivity to light, lower vision quality, and a lower quality of life.

In order to meet the need of diagnosing DED early, research group, led by Professor Akira Murakami and Associate Professor Takenori Inomata of the Juntendo University Graduate School of Medicine, developed a smartphone application called DryEyeRhythm.The new App DryEyeRhythm which is a reliable, valid, and non-invasive method to assess dry eye disease.

Despite the obvious disadvantages of DED, a large portion of the population remains undiagnosed, which ultimately leads to increased disease severity. DED is currently diagnosed through a series of questionnaires and ocular examinations (which can be invasive). But this method of diagnosis is not ideal.

DED examinations do not always correspond with patients' subjective DED symptoms. Furthermore, non-invasive and non-contact dry eye examinations are required in the COVID-19 pandemic. These flaws point to a need for a simple, reliable, and accessible screening method for DED to improve diagnosis and prognosis of the disease.

"DryEyeRhythm leverages the cameras in smartphones to measure users' blink characteristics and determine maximum blink interval (MBI)-a substitute for tear film breakup time, an important diagnostic criterion of DED," explains Associate Prof. Inomata. "The app also administers Ocular Surface Disease Index (OSDI) questionnaires, which are also a crucial component of DED diagnosis."

To validate the usefulness of the app, the research team conducted a prospective, cross-sectional, observational, single-center study, the results of which have been published in The Ocular Surface (available online on 25 April 2022 and published in volume 25 in July 2022).

For their study, the team recruited 82 patients, aged 20 years or older, who visited the ophthalmology outpatient clinic at the Juntendo University Hospital between July 2020 and May 2021. The participants completed the Japanese version of the OSDI questionnaire (J-OSDI) and underwent examinations for MBI, both via the app and via other analysis techniques.

The study revealed that the J-OSDI collected with DryEyeRhythm showed good internal consistency. Moreover, the app-based questionnaire and MBI yielded significantly higher discriminant validity. The app also showed good positive and negative predictive values, with 91.3% and 69.1%, respectively.

The area under the Receiver operating characteristic (ROC) curve-a measure of clinical sensitivity and specificity-for the concurrent use of the app-based J-OSDI and MBI was also high, with a value of 0.910. These results demonstrate that the app is a reliable, valid, and moreover non-invasive, instrument for assessing DED.

Reference:

Yuichi Okumura, Takenori Inomata, Akie Midorikawa-Inomata, Jaemyoung Sung, Kenta Fujio, Yasutsugu Akasaki, Masahiro Nakamura, Masao Iwagami, Keiichi Fujimoto, Atsuko Eguchi, Maria Miura, Ken Nagino, Kunihiko Hirosawa, Tianxiang Huang, Mizu Kuwahara, Reza Dana, Akira Murakami, DryEyeRhythm: A reliable and valid smartphone application for the diagnosis assistance of dry eye. The Ocular Surface, https://doi.org/10.1016/j.jtos.2022.04.005.