A Deep Learning Approach for Meibomian Gland Appearance Evaluation

PURPOSE: To develop and evaluate a deep learning algorithm for Meibomian gland characteristics calculation. DESIGN: Evaluation of diagnostic technology. SUBJECTS: A total of 1616 meibography images of both the upper (697) and lower (919) eyelids from a total of 282 individuals. METHODS: Images were collected using the LipiView II device. All the provided data were split into 3 sets: the training, validation, and test sets. Data partitions used proportions of 70/10/20% and included data from 2 optometry settings. Each set was separately partitioned with these proportions, resulting in a balanced distribution of data from both settings. The images were divided based on patient identifiers, such that all images collected for one participant could end up only in one set. The labeled images were used to train a deep learning model, which was subsequently used for Meibomian gland segmentation. The model was then applied to calculate individual Meibomian gland metrics. Interreader agreement and agreement between manual and automated methods for Meibomian gland segmentation were also carried out to assess the accuracy of the automated approach. MAIN OUTCOME MEASURES: Meibomian gland metrics, including length ratio, area, tortuosity, intensity, and width, were measured. Additionally, the performance of the automated algorithms was evaluated using the aggregated Jaccard index. RESULTS: The proposed semantic segmentation–based approach achieved average aggregated Jaccard index of mean 0.4718 (95% confidence interval [CI], 0.4680–0.4771) for the ‘gland’ class and a mean of 0.8470 (95% CI, 0.8432–0.8508) for the ‘eyelid’ class. The result for object detection–based approach was a mean of 0.4476 (95% CI, 0.4426–0.4533). Both artificial intelligence–based algorithms underestimated area, length ratio, tortuosity, width(mean), width(median), width(10th), and width(90th). Meibomian gland intensity was overestimated by both algorithms compared with the manual approach. The object detection–based algorithm seems to be as reliable as the manual approach only for Meibomian gland width(10th) calculation. CONCLUSIONS: The proposed approach can successfully segment Meibomian glands; however, to overcome problems with gland overlap and lack of image sharpness, the proposed method requires further development. The study presents another approach to utilizing automated, artificial intelligence–based methods in Meibomian gland health assessment that may assist clinicians in the diagnosis, treatment, and management of Meibomian gland dysfunction. FINANCIAL DISCLOSURE(S): The authors have no proprietary or commercial interest in any materials discussed in this article.