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Meibomian gland dysfunction (MGD) is increasingly recognized as a critical contributor to evaporative dry eye, significantly impacting visual quality. With a global prevalence estimated at 35.8 %, it presents substantial challenges for clinicians. Conventional manual evaluation techniques for MGD face limitations characterized by inefficiencies, high subjectivity, limited big data processing capabilities, and a dearth of quantitative analytical tools. With rapidly advancing artificial intelligence (AI) techniques revolutionizing ophthalmology, studies are now leveraging sophisticated AI methodologies--including computer vision, unsupervised learning, and supervised learning--to facilitate comprehensive analyses of meibomian gland (MG) evaluations. These evaluations employ various techniques, including slit lamp examination, infrared imaging, confocal microscopy, and optical coherence tomography. This paradigm shift promises enhanced accuracy and consistency in disease evaluation and severity classification. While AI has achieved preliminary strides in meibomian gland evaluation, ongoing advancements in system development and clinical validation are imperative. We review the evolution of MG evaluation, juxtapose AI-driven methods with traditional approaches, elucidate the specific roles of diverse AI technologies, and explore their practical applications using various evaluation techniques. Moreover, we delve into critical considerations for the clinical deployment of AI technologies and envisages future prospects, providing novel insights into MG evaluation and fostering technological and clinical progress in this arena.
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AIM: To investigate a pioneering framework for the segmentation of meibomian glands (MGs), using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis. METHODS: Totally 203 infrared meibomian images from 138 patients with dry eye disease, accompanied by corresponding annotations, were gathered for the study. A rectified scribble-supervised gland segmentation (RSSGS) model, incorporating temporal ensemble prediction, uncertainty estimation, and a transformation equivariance constraint, was introduced to address constraints imposed by limited supervision information inherent in scribble annotations. The viability and efficacy of the proposed model were assessed based on accuracy, intersection over union (IoU), and dice coefficient. RESULTS: Using manual labels as the gold standard, RSSGS demonstrated outcomes with an accuracy of 93.54%, a dice coefficient of 78.02%, and an IoU of 64.18%. Notably, these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%, 2.06%, and 2.69%, respectively. Furthermore, despite achieving a substantial 80% reduction in annotation costs, it only lags behind fully annotated methods by 0.72%, 1.51%, and 2.04%. CONCLUSION: An innovative automatic segmentation model is developed for MGs in infrared eyelid images, using scribble annotation for training. This model maintains an exceptionally high level of segmentation accuracy while substantially reducing training costs. It holds substantial utility for calculating clinical parameters, thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.
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Introduction: To investigate the effects of an orthokeratology lens on the tear film and tarsal glands and myopia control in children with unilateral myopia using an intelligent analysis model. Methods: We retrospectively reviewed the medical records from November 2020 to November 2022 of 68 pediatric patients with unilateral myopia in Fujian Provincial Hospital who had been wearing an orthokeratology lens for more than 1 year. The 68 myopic eyes were included in the treatment group, while the 68 healthy, untreated contralateral eyes were included in the control group. Tear film break-up times (TBUTs) were compared between the two groups at various intervals, and an intelligent analysis model was used to compare the deformation coefficients of 10 meibomian glands in the central area and the different positions of the glands in the two groups after 12 months of treatment. Changes in axial length and equivalent spherical power were also compared between the groups before and after 12 months of treatment. Results: In the treatment group, TBUTs differed significantly between 1 and 12 months after treatment, although no significant differences from baseline were observed at 3 or 6 months. No significant differences in TBUTs were observed at any time point in the control group. After 12 months of treatment, significant between-group differences were observed for glands 2, 3, 4, 5, 6, 7, 8, and 10 (numbered from the temporal to nasal regions). The treatment group also exhibited significant differences in deformation coefficients at different detection positions in the central region, with glands 5 and 6 exhibiting the highest deformation coefficients. Increases in axial length and equivalent spherical power were significantly greater in the control group than in the treatment group after 12 months of treatment. Discussion: Wearing orthokeratology lenses at night can effectively control myopia progression in children with unilateral myopia. However, long-term use of these lenses may lead to meibomian gland deformation and impact tear film function, and the extent of deformation may vary at different positions in the central region.
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Introduction: This study aimed to compare compliance between pediatric patients with amblyopia undergoing a smartphone virtual reality-based training method (EYEBIT) and those receiving traditional patching method training. Methods: A crossover design was adopted in this study. The enrolled children (n = 76) were randomized into the traditional patching and EYEBIT training method groups. The patients received training methods once a day for 2 h and 1 h in the patching and EYEBIT groups, respectively. Follow-up assessments involved interviews with parents regarding children's compliance and questionnaire-based interviews with children; compliance rating was compared between the methods. Results: All children completed the training and follow-up assessments. There were significant differences in parent and children compliance-related behavior and attitudes between the two training methods (p < 0.05). The EYEBIT method was associated with better compliance than the traditional patching method. Significant correlations were observed among compliance components in both methods. In the correlation analysis between the two groups, the research results showed that in the EYEBIT group, the correlation between children's compliance behaviors and children's compliance attitudes, the correlation between children's compliance behaviors and parents' compliance behaviors, and the correlations between children's compliance attitudes and parents' compliance attitudes were all negatively correlated, and in the traditional patching group, the above three correlation analysis results were all positive. Conclusion: The use of the EYEBIT method may improve compliance in children with amblyopia; this method appears acceptable to the parents of children with amblyopia.