Dry eye disease diagnosis based on criteria recommended in Dry Eye Workshop II report

The dataset was generated as a results of an experimental phase of a project aimed at developing a new, objective method to diagnose dry eye disease, based on Tear Clearance Rate (https://doi.org/10.18150/NZG1ZS).

This particular dataset was used to categorize participants into two subgroups, which includes:

• the control group (CNTRL) 
• dry eye disease (DED) group.

Subjects were divided based on the protocol recommended in the Dry Eye Workshop II report (reference: Wolffsohn, James S., et al. "TFOS DEWS II diagnostic methodology report", The Ocular Surface 15.3 (2017): 539-574) Each subject was given a code in a following format: XX_GROUP, where XX is participant's unique number and GROUP indicates their respective subgroup (DED or CNTRL)

The dataset contains six files, including:

(1) Lipid_Layer.ZIP: Video recordings (MKV format) of the ocular surface visualizing the lipid layer of the tear film using a thin layer white light interferometry method, capturing several blinks and the movement of the lipid layer. These recordings were used to assess lipid layer thickness as thin, normal or thick based on the observed interferenc pattern.

(2) Meibography.ZIP: Black-and-white images (BMP format) of the everted lower and upper eyelids (marked as Lower or Upper, respectively), captured using non-contact infrared meibography with Oculus Keratograph 5M, allowing visualization of the Meibomian glands. Images were given scores in Meiboscale by Heiko Pult.

(3) NIKBUT.ZIP: with recordings of a Placido disks pattern reflected from the ocular surface, recorded for each participant frame by frame (BMP format). These recordings were used to determine NIKBUT (Noninvasive Keratograph Tear Film Break-Up Time) in seconds. Two measurements per subject were performed(marked as NIKBUT_1 and NIKBUT_2) and average First and Mean NIKBUT were saved for each subject. The number of frames (BMPs) in each sequence is different, depending on how much time each subject kept their eyes open, but is not larger than 394 frames, which corresponds to 24-second-long recording.

(4) Ocular_Redness.ZIP: Images of the ocular surface (in PNG format), used to assess ocular surface rendness score with Oculus Keratograph 5M

(5) Tear_Meniscus_Height.ZIP: Black-and-white images of the ocular surface (PNG format) based on which measurements of tear meniscus height (in milimeters) were performed with an in-built feature of Oculus Keratograph 5M

(6) Results.csv: with a summary of numerical data sampled with ocular symptom questionnaires (including Ocular Surface Disease Inded and Dry Eye Questionnaire-5), demographi data, laboratory stats, tear osmolarity, NIKBUT estimation, Meiboscale and other results recorded based on this dataset.