Diagnostic classification of autoantibody repertoires in endocrine ophthalmopathy using an artificial neural network

Application of artificial intelligence in oculoplastics

Oculoplastics is a subspecialty of ophthalmology/dermatology concerned with eyelid, orbital, and lacrimal diseases. Artificial intelligence (AI), with its powerful ability to analyze large data sets, has dramatically benefited oculoplastics. The cutting-edge AI technology is widely applied to extract ocular parameters and to use these results for further assessment, such as screening and diagnosis of blepharoptosis and predicting the progression of thyroid eye disease. AI also assists in treatment procedures, such as surgical strategy planning in blepharoptosis. High efficiency and high reliability are the most apparent advantages of AI, with promising prospects. The possibilities of AI in oculoplastics may lie in three-dimensional modeling technology and image generation. We retrospectively summarize AI applications involving eyelid, orbital, and lacrimal diseases in oculoplastics, and we also examine the strengths and weaknesses of AI technology in oculoplastics.

Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review

PURPOSE

This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools.

METHODS

This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included.

RESULTS

A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data.

CONCLUSION

AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice.

Global research on artificial intelligence in thyroid-associated ophthalmopathy: A bibliometric analysis

Purpose

To provide an overview of global publications on artificial intelligence (AI) in thyroid-associated ophthalmopathy (TAO) through bibliometric analysis.

Methods

Publications related to AI in TAO from inception until April 2023 were retrieved from the Web of Science database. The trends of publications and citations, publishing performance, collaboration among countries and institutions, and the funding agencies, relevant research domains, leading journals, hotspots and their evolution were identified.

Results

A total of 55 publications were included for analysis. The number of publications and citations continued to grow since 1998, with a significant acceleration of growth after 2020. China is the most productive country with the highest number of productive institutions, followed by the United States. European countries have the most extensive collaboration. The most relevant research domain was radiology, nuclear medicine & medical imaging. The European Journal of Radiology was one of the most productive journals, with the most influential articles published. "Thyroid-associated ophthalmopathy" and "neural network" maintain hotspots during the entire period. Studies were more focused on clinical features during 1998 and 2016, clinical features and medical data during 2017 and 2020, and medical data and AI techniques during 2021 and 2023.

Conclusions

This study summarized the global research status regarding AI in TAO in terms of trends, countries, institutions, research domains, journals, and key topics. AI has shown great potential in TAO. Sponsored by funding agencies such as NSFC, China has become the most productive country in the field of AI in TAO. Our findings help researchers better understand the development of this field and provide valuable clues for future research directions.

Research progress and application of artificial intelligence in thyroid associated ophthalmopathy

Thyroid-associated ophthalmopathy (TAO) is a complicated orbitopathy related to dysthyroid, which severely destroys the facial appearance and life quality without medical interference. The diagnosis and management of thyroid-associated ophthalmopathy are extremely intricate, as the number of professional ophthalmologists is limited and inadequate compared with the number of patients. Nowadays, medical applications based on artificial intelligence (AI) algorithms have been developed, which have proved effective in screening many chronic eye diseases. The advanced characteristics of automated artificial intelligence devices, such as rapidity, portability, and multi-platform compatibility, have led to significant progress in the early diagnosis and elaborate evaluation of these diseases in clinic. This study aimed to provide an overview of recent artificial intelligence applications in clinical diagnosis, activity and severity grading, and prediction of therapeutic outcomes in thyroid-associated ophthalmopathy. It also discussed the current challenges and future prospects of the development of artificial intelligence applications in treating thyroid-associated ophthalmopathy.

Analysis of tear protein patterns by a neural network as a diagnostical tool for the detection of dry eyes

The electrophoretic patterns of tears from patients with dry-eye disease (n = 43) and from healthy subjects (n = 17) were analyzed by means of multivariate statistical methods and an artificial neural network (ANN), following sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). From each electrophoretic pattern a data set was created, randomly divided into test (unknown samples) and training patterns (known samples), with ANN training by one of these sets. After training, the performance of the ANN was checked by presenting the test data set to the ANN. Furthermore, the data was classified using multivariate analysis of discriminance. The groups were significantly different from each other (P<0.05). The statistical procedure yielded 97% (known samples) and 71% (unknown samples) correct classifications. The ANN revealed 89% of correct classifications using the test set (unknown samples). The use of pruning algorithms (optimization procedure which automatically eliminates small weighted neurons) or genetic algorithms (optimization procedure which performs genetically induced changes of the neural net) resulted in a slight decrease of correct classifications compared to those of the nonoptimized neural network. The results reveal significant differences between the two groups. Using the ANN we were able to classify the electrophoretic tear protein pattern for diagnostic purposes.