Assessment of Anterior Uveitis Through Anterior-Segment Optical Coherence Tomography and Artificial Intelligence-Based Image Analyses

Current Status, Hotspots, and Prospects of Artificial Intelligence in Ophthalmology: A Bibliometric Analysis (2003-2023)

PURPOSE

Artificial intelligence (AI) has gained significant attention in ophthalmology. This paper reviews, classifies, and summarizes the research literature in this field and aims to provide readers with a detailed understanding of the current status and future directions, laying a solid foundation for further research and decision-making.

METHODS

Literature was retrieved from the Web of Science database. Bibliometric analysis was performed using VOSviewer, CiteSpace, and the R package Bibliometrix.

RESULTS

The study included 3,377 publications from 4,035 institutions in 98 countries. China and the United States had the most publications. Sun Yat-sen University is a leading institution. Translational Vision Science & Technology"published the most articles, while "Ophthalmology" had the most co-citations. Among 13,145 researchers, Ting DSW had the most publications and citations. Keywords included "Deep learning," "Diabetic retinopathy," "Machine learning," and others.

CONCLUSION

The study highlights the promising prospects of AI in ophthalmology. Automated eye disease screening, particularly its core technology of retinal image segmentation and recognition, has become a research hotspot. AI is also expanding to complex areas like surgical assistance, predictive models. Multimodal AI, Generative Adversarial Networks, and ChatGPT have driven further technological innovation. However, implementing AI in ophthalmology also faces many challenges, including technical, regulatory, and ethical issues, and others. As these challenges are overcome, we anticipate more innovative applications, paving the way for more effective and safer eye disease treatments.

Managing a patient with uveitis in the era of artificial intelligence: Current approaches, emerging trends, and future perspectives

The integration of artificial intelligence (AI) with healthcare has opened new avenues for diagnosing, treating, and managing medical conditions with remarkable precision. Uveitis, a diverse group of rare eye conditions characterized by inflammation of the uveal tract, exemplifies the complexities in ophthalmology due to its varied causes, clinical presentations, and responses to treatments. Uveitis, if not managed promptly and effectively, can lead to significant visual impairment. However, its management requires specialized knowledge, which is often lacking, particularly in regions with limited access to health services. AI's capabilities in pattern recognition, data analysis, and predictive modelling offer significant potential to revolutionize uveitis management. AI can classify disease etiologies, analyze multimodal imaging data, predict outcomes, and identify new therapeutic targets. However, transforming these AI models into clinical applications and meeting patient expectations involves overcoming challenges like acquiring extensive, annotated datasets, ensuring algorithmic transparency, and validating these models in real-world settings. This review delves into the complexities of uveitis and the current AI landscape, discussing the development, opportunities, and challenges of AI from theoretical models to bedside application. It also examines the epidemiology of uveitis, the global shortage of uveitis specialists, and the disease's socioeconomic impacts, underlining the critical need for AI-driven approaches. Furthermore, it explores the integration of AI in diagnostic imaging and future directions in ophthalmology, aiming to highlight emerging trends that could transform management of a patient with uveitis and suggesting collaborative efforts to enhance AI applications in clinical practice.

Diagnosing and Managing Uveitis Associated with Immune Checkpoint Inhibitors: A Review

This review aims to provide an understanding of the diagnostic and therapeutic challenges of uveitis associated with immune checkpoint inhibitors (ICI). In the wake of these molecules being increasingly employed as a treatment against different cancers, cases of uveitis post-ICI therapy have also been increasingly reported in the literature, warranting an extensive exploration of the clinical presentations, risk factors, and pathophysiological mechanisms of ICI-induced uveitis. This review further provides an understanding of the association between ICIs and uveitis, and assesses the efficacy of current diagnostic tools, underscoring the need for advanced techniques to enable early detection and accurate assessment. Further, it investigates the therapeutic strategies for ICI-related uveitis, weighing the benefits and limitations of existing treatment regimens, and discussing current challenges and emerging therapies in the context of their potential efficacy and side effects. Through an overview of the short-term and long-term outcomes, this article suggests recommendations and emphasizes the importance of multidisciplinary collaboration between ophthalmologists and oncologists. Finally, the review highlights promising avenues for future research and development in the field, potentially informing transformative approaches in the ocular assessment of patients under immunotherapy and the management of uveitis following ICI therapy.

Quality assessment of anterior segment OCT images: Development and validation of quality criteria

BACKGROUND

The utility of medical imaging is dependant on image quality. We aimed to develop and validate quality criteria for ocular anterior segment optical coherence tomography (AS-OCT) images.

METHODS

We undertook a cross-sectional study using AS-OCT images from patients aged 6-16. A novel three-level grading system (good, limited or poor) was developed based on the presence of image artefact (categorised as lid, eyelash, cropping, glare, or movement artefact). Three independent experts graded 2825 images, with agreement assessed using confusion matrices and intraclass correlation coefficients (ICC) for each parameter.

RESULTS

There was very good inter-grader IQA agreement assessing image quality with ICC 0.85 (95 %CI: 0.84-0.87). The most commonly occurring artefact was eyelash artefact (1008/2825 images, 36 %). Graders labelled 621/2825 (22 %) images as good and 384 (14 %) as poor. There was complete agreement at either end of the confusion matrix with no 'good' images labelled as 'poor' by other graders, and vice versa. Similarly, there was very good agreement when assessing presence of lash (0.96,0.94-0.98), movement (0.97,0.96-0.99), glare (0.82,0.80-0.84) and cropping (0.90,0.88-0.92).

CONCLUSIONS

The novel image quality assessment criteria (IQAC) described here have good interobserver agreement overall, and excellent agreement on the differentiation between 'good' and 'poor' quality images. The large proportion of images graded as 'limited' suggests the need for refine this classification, using the specific IQAC features, for which we also report high interobserver agreement. These findings support the future potential for wider clinical and community care implementation of AS-OCT for the diagnosis and monitoring of ocular disease.

Imaging of Anterior Segment Tumours: A Comparison of Ultrasound Biomicroscopy Versus Anterior Segment Optical Coherence Tomography

Anterior segment tumours of the eye are relatively rare but can pose significant morbidity and mortality. We conducted a literature review to compare the performance of ultrasound biomicroscopy to anterior segment optical coherence tomography in the imaging of these tumours. A total of seven studies were included accounting for a cumulative 1,114 eyes. Ultrasound biomicroscopy has traditionally formed, and remains, the mainstay of tumour imaging due to its ability to penetrate pigmented lesions and delineate the posterior border of tumours, and the current evidence supports this.

Anterior chamber inflammation grading methods: A critical review

Assessing anterior chamber inflammation is highly subjective and challenging. Although various grading systems attempt to offer objectivity and standardization, the clinical assessment has high interobserver variability. Traditional techniques, such as laser flare meter and fluorophotometry, are not widely used since they are time-consuming. With the development of optical coherence tomography with high sensitivity, direct imaging offers an excellent alternative to assess objectively inflammation with the potential for automated analysis. We describe various anterior chamber inflammation grading methods and discuss their utility, advantages, and disadvantages.

A machine learning framework for the quantification of experimental uveitis in murine OCT

This paper presents methods for the detection and assessment of non-infectious uveitis, a leading cause of vision loss in working age adults. In the first part, we propose a classification model that can accurately predict the presence of uveitis and differentiate between different stages of the disease using optical coherence tomography (OCT) images. We utilize the Grad-CAM visualization technique to elucidate the decision-making process of the classifier and gain deeper insights into the results obtained. In the second part, we apply and compare three methods for the detection of detached particles in the retina that are indicative of uveitis. The first is a fully supervised detection method, the second is a marked point process (MPP) technique, and the third is a weakly supervised segmentation that produces per-pixel masks as output. The segmentation model is used as a backbone for a fully automated pipeline that can segment small particles of uveitis in two-dimensional (2-D) slices of the retina, reconstruct the volume, and produce centroids as points distribution in space. The number of particles in retinas is used to grade the disease, and point process analysis on centroids in three-dimensional (3-D) shows clustering patterns in the distribution of the particles on the retina.

Diagnosis, Classification, and Assessment of the Underlying Etiology of Uveitis by Artificial Intelligence: A Systematic Review

Recent years have seen the emergence and application of artificial intelligence (AI) in diagnostic decision support systems. There are approximately 80 etiologies that can underly uveitis, some very rare, and AI may lend itself to their detection. This synthesis of the literature selected articles that focused on the use of AI in determining the diagnosis, classification, and underlying etiology of uveitis. The AI-based systems demonstrated relatively good performance, with a classification accuracy of 93-99% and a sensitivity of at least 80% for identifying the two most probable etiologies underlying uveitis. However, there were limitations to the evidence. Firstly, most data were collected retrospectively with missing data. Secondly, ophthalmic, demographic, clinical, and ancillary tests were not reliably integrated into the algorithms' dataset. Thirdly, patient numbers were small, which is problematic when aiming to discriminate rare and complex diagnoses. In conclusion, the data indicate that AI has potential as a diagnostic decision support system, but clinical applicability is not yet established. Future studies and technologies need to incorporate more comprehensive clinical data and larger patient populations. In time, these should improve AI-based diagnostic tools and help clinicians diagnose, classify, and manage patients with uveitis.

Artificial intelligence in uveitis: A comprehensive review

Uveitis is a disease complex characterized by intraocular inflammation of the uvea that is an important cause of blindness and social morbidity. With the dawn of artificial intelligence (AI) and machine learning integration in healthcare, their application in uveitis creates an avenue to improve screening and diagnosis. Our review identified the use of artificial intelligence in studies of uveitis and classified them as diagnosis support, finding detection, screening, and standardization of uveitis nomenclature. The overall performance of models is poor, with limited datasets and a lack of validation studies and publicly available data and codes. We conclude that AI holds great promise to assist with the diagnosis and detection of ocular findings of uveitis, but further studies and large representative datasets are needed to guarantee generalizability and fairness.

Automated Quantitative Analysis of Anterior Segment Inflammation Using Swept-Source Anterior Segment Optical Coherence Tomography: A Pilot Study

Background: The aim of this study is to develop an automated evaluation of anterior chamber (AC) cells in uveitis using anterior segment (AS) optical coherence tomography (OCT) images. Methods: We analyzed AS swept-source (SS)-OCT (CASIA 2) images of 31 patients (51 eyes) with uveitis using image analysis software (Python). An automated algorithm was developed to detect cellular spots corresponding to hyper-reflective spots in the AC, and the correlation with Standardization of Uveitis Nomenclature (SUN) grading AC cells score was evaluated. The approximated AC grading value was calculated based on the logarithmic approximation curve between the number of cellular spots and the SUN grading score. Results: Among 51 eyes, cellular spots were automatically segmented in 48 eyes, whereas three eyes (all SUN grading AC cells score: 4+) with severe fibrin formation in the AC were removed by the automated algorithm. The AC cellular spots increased with an increasing SUN grading score (p < 0.001). The 48 eyes were split into training data (26 eyes) and test data (22 eyes). There was a significant correlation between the SUN grading score and the number of cellular spots in 26 eyes (rho: 0.843, p < 0.001). There was a significant correlation between the SUN grading score and the approximated grading value of 22 eyes based on the logarithmic approximation curve (rho: 0.774, p < 0.001). Leave-one-out cross-validation analysis demonstrated a significant correlation between the SUN grading score and the approximated grading value of 48 eyes (rho: 0.748, p < 0.001). Conclusions: This automated anterior AC cell analysis using AS SS-OCT showed a significant correlation with clinical SUN grading scores and provided SUN AC grading values as a continuous variable. Our findings suggest that automated grading of AC cells could improve the accuracy of a quantitative assessment of AC inflammation using AS-OCT images and allow the objective and rapid evaluation of anterior segment inflammation in uveitis. Further investigations on a large scale are required to validate this quantitative measurement of anterior segment inflammation in uveitic eyes.