Purpose To investigate a method to identification of early progression of keratoconus using deep learning neural networks. Methods Retrospective evaluation of medical records of patients with progressive keratoconus and had more than one followup visits. Images extracted from the single scheimplug analyzer for analysis were captured during the patient visits. The baseline progression of keratoconus is detected by a change in flat or steep K of ≥1.0D which is labeled as keratometric progression (KP) and progression detected by image based deep learning convolutional neural network (CNN) models, is labeled as latent progression (LP). Patient data consisted of model data (385 eyes of 351patients) to train and test the learning models and prediction data (1331 eyes of 828 patients) to determine the LP based on the learning models. Results The LP prediction model was able to identify progression at a mean of 11.1 months earlier than KP (p < 0.001). LP prediction model was able to identify progression earlier than KP irrespective of age category, gender, the severity of keratoconus, presenting visual acuity, astigmatism, and spherical equivalent (P < 0.001). When compared to the first visit the corrected distance visual acuity was more stable in 71% of the eyes at LP prediction visit compared to 50% at KP visit (p < 0.001). Conclusion Through this study, we propose a possible solution to address the shortcomings noted in the current approaches of detecting progression relying only on KP. Avoiding bias towards feature selection from tomography images as done in the current study aids in identifying very subtle changes on the images between visits.
Definitive treatment of dry eye disease (DED), one of the commonest ocular surface disorders, has remained elusive despite several recent advances in better diagnostics and the introduction of newer therapeutic molecules. The current treatment paradigms rely heavily on lubricating eye drops and anti-inflammatory agents that may need to be used long-term and are mainly palliative. Research is ongoing not only for a curative treatment option but also to improve the potency and efficacy of existing drug molecules through better formulations and delivery platforms. In the past two decades, significant advancement has been made in terms of preservative-free formulations, biomaterials such as nanosystems and hydrogels, stem cell therapy, and creation of a bioengineered lacrimal gland. This review comprehensively summarizes the newer approaches to DED treatment, which are biomaterials such as nanosystems, hydrogels, and contact lenses for drug delivery, cell and tissue-based regenerative therapy for damaged lacrimal gland and ocular surface, and tissue engineering for developing artificial lacrimal gland. Also, their potential efficacies in animal models or in vitro studies and possible limitations are discussed. The ongoing research looks promising and needs to be supported with clinical efficacy and safety studies for human use.
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