Purpose: This study aims to develop and assess an infrastructure using Python-based deep learning code for future diagnostic and management purposes related to dry eye disease (DED) utilizing smartphone images.

Design: Cross-sectional study using data which was gathered in Vision Health Research Clinic.

Participants: One thousand twenty-one eye images from 734 patients were included in this article that categorizes into 70% females and 30% males, with no sex and age limit.

Methods: One specialist captured eye images using Samsung A71 (601 images) and iPhone 11 (420 images) cell phones with the flashlight on and direct gaze to the camera. These images include the area of only 1 eye (left/right).

Main outcome measures: First, our specialist did 3 different segmentations for every eye image separately for 80% of the training data. This part contains eye, lower eyelid, and iris segmentation. In 20% of test data after automated cropping of the lower eyelid margin and upscaling by 8×, the appropriate tear meniscus height segmentation will be chosen and measured using a deep learning algorithm.

Results: The model was trained on 80% of the data and 20% of the data used for validation from both phones with different resolutions. The dice coefficient of the trained model for validation data is 98.68%, and the accuracy of the overall model is 95.39%.

Conclusions: It appears that this algorithm holds the potential to herald an evolution in the future of diagnosis and management of DED by homecare devices solely through smartphones.

Financial disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article.