Automated Method of Grading Vitreous Haze in Patients With Uveitis for Clinical Trials

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.

AUTOMATED DETECTION OF VITRITIS USING ULTRAWIDE-FIELD FUNDUS PHOTOGRAPHS AND DEEP LEARNING

BACKGROUND/PURPOSE

Evaluate the performance of a deep learning algorithm for the automated detection and grading of vitritis on ultrawide-field imaging.

METHODS

Cross-sectional noninterventional study. Ultrawide-field fundus retinophotographs of uveitis patients were used. Vitreous haze was defined according to the six steps of the Standardization of Uveitis Nomenclature classification. The deep learning framework TensorFlow and the DenseNet121 convolutional neural network were used to perform the classification task. The best fitted model was tested in a validation study.

RESULTS

One thousand one hundred eighty-one images were included. The performance of the model for the detection of vitritis was good with a sensitivity of 91%, a specificity of 89%, an accuracy of 0.90, and an area under the receiver operating characteristics curve of 0.97. When used on an external set of images, the accuracy for the detection of vitritis was 0.78. The accuracy to classify vitritis in one of the six Standardization of Uveitis Nomenclature grades was limited (0.61) but improved to 0.75 when the grades were grouped into three categories. When accepting an error of one grade, the accuracy for the six-class classification increased to 0.90, suggesting the need for a larger sample to improve the model performances.

CONCLUSION

A new deep learning model based on ultrawide-field fundus imaging that produces an efficient tool for the detection of vitritis was described. The performance of the model for the grading into three categories of increasing vitritis severity was acceptable. The performance for the six-class grading of vitritis was limited but can probably be improved with a larger set of images.

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.

Measuring Inflammation in the Vitreous and Retina: A Narrative Review

Uveitis consists of a group of syndromes characterised by intraocular inflammation, accounting for up to 15% of visual loss in the western world and 10% worldwide. Assessment of intraocular inflammation has been limited to clinician-dependent, subjective grading. Developments in imaging technology, such as optical coherence tomography (OCT), have enabled the development of objective, quantitative measures of inflammatory activity. Important quantitative metrics including central macular thickness and vitreous signal intensity allow longitudinal monitoring of disease activity and can be used in conjunction with other imaging modalities enabling holistic assessment of ocular inflammation. Ongoing work into the validation of instrument-based measures alongside development of core outcome sets is crucial for standardisation of clinical trial endpoints and developing guidance for quantitative multi-modal imaging approaches. This review outlines methods of grading inflammation in the vitreous and retina, with a focus on the use of OCT as an objective measure of disease activity.

Artificial Intelligence and Imaging Processing in Optical Coherence Tomography and Digital Images in Uveitis

INTRODUCTION

Computer vision, understood as the area of science that trains computers to interpret digital images through both artificial intelligence (AI) and classical algorithms, has significantly advanced the analysis and interpretation of optical coherence tomography (OCT) in retina research. The aim of this review is to summarise the recent advances of computer vision in imaging processing in uveitis, with a particular focus in optical coherence tomography images.

MATERIAL AND METHODS

Literature review.

RESULTS

The development of computer vision to assist uveitis diagnosis and prognosis is still undergoing, but important efforts have been made in the field.

CONCLUSION

The automatising of image processing in uveitis could be fundamental to establish objective and standardised outcomes for future clinical trials. In addition, it could help to better understand the disease and its progression.

Noninvasive Instrument-based Tests for Detecting and Measuring Vitreous Inflammation in Uveitis: A Systematic Review

PURPOSE

This systematic review aims to identify instrument-based tests for quantifying vitreous inflammation in uveitis, report the test reliability and the level of correlation with clinician grading.

METHODS

Studies describing instrument-based tests for detecting vitreous inflammation were identified by searching bibliographic databases and trials registers. Test reliability measures and level of correlation with clinician vitreous haze grading are extracted.

RESULTS

Twelve studies describing ultrasound, optical coherence tomography (OCT), and retinal photography for detecting vitreous inflammation were included: Ultrasound was used for detection of disease features, whereas OCT and retinal photography provided quantifiable measurements. Correlation with clinician grading for OCT was 0.53-0.60 (three studies) and for retinal photography was 0.51 (1 study). Both instruments showed high inter- and intra-observer reliability (>0.70 intraclass correlation and Cohen's kappa), where reported in four studies.

CONCLUSION

Retinal photography and OCT are able to detect and measure vitreous inflammation. Both techniques are reliable, automatable, and warrant further evaluation.

Quantitative Assessment of Experimental Ocular Inflammatory Disease

Ocular inflammation imposes a high medical burden on patients and substantial costs on the health-care systems that mange these often chronic and debilitating diseases. Many clinical phenotypes are recognized and classifying the severity of inflammation in an eye with uveitis is an ongoing challenge. With the widespread application of optical coherence tomography in the clinic has come the impetus for more robust methods to compare disease between different patients and different treatment centers. Models can recapitulate many of the features seen in the clinic, but until recently the quality of imaging available has lagged that applied in humans. In the model experimental autoimmune uveitis (EAU), we highlight three linked clinical states that produce retinal vulnerability to inflammation, all different from healthy tissue, but distinct from each other. Deploying longitudinal, multimodal imaging approaches can be coupled to analysis in the tissue of changes in architecture, cell content and function. This can enrich our understanding of pathology, increase the sensitivity with which the impacts of therapeutic interventions are assessed and address questions of tissue regeneration and repair. Modern image processing, including the application of artificial intelligence, in the context of such models of disease can lay a foundation for new approaches to monitoring tissue health.

Structural Endpoints and Outcome Measures in Uveitis

Most uveitis entities are rare diseases but, taken together, are responsible for 5-10% of worldwide visual impairment which largely affects persons of working age. As with many rare diseases, there is a lack of high-level evidence regarding its clinical management, partly due to a dearth of reliable and objective quantitative endpoints for clinical trials. This review provides an overview of available structural outcome measures for uveitis disease activity and damage in an anatomical order from the anterior to the posterior segment of the eye. While there is a multitude of available structural outcome measures, not all might qualify as endpoints for clinical uveitis trials, and thorough testing of applicability is warranted. Furthermore, a consensus on endpoint definition, standardization, and "core outcomes" is required. As stipulated by regulatory agencies, endpoints should be precisely defined, clinically important, internally consistent, reliable, responsive to treatment, and relevant for the respective subtype of uveitis. Out of all modalities used for assessment of the reviewed structural outcome measures, optical coherence tomography, color fundus photography, fundus autofluorescence, and fluorescein/indocyanine green angiography represent current "core modalities" for reliable and objective quantification of uveitis outcome measures, based on their practical availability and the evidence provided so far.

Multimodal imaging in infectious and noninfectious intermediate, posterior and panuveitis

PURPOSE OF REVIEW

Given the heterogeneity of uveitis, markers of inflammation vary from patient to patient. Multimodal imaging has proven itself to be critical for accurate evaluation for disease activity and treatment response in uveitis.

RECENT FINDINGS

Ultra-widefield (UWF) fluorescein angiography and autofluorescence (AF) as well as optical coherence tomography angiography (OCTA) have provided insights into disease pathogenesis and monitoring not previously appreciated. In addition to structural retinal imaging, OCT can be used to assess the choroid, the posterior cortical vitreous and the retinal vasculature in eyes with uveitis.

SUMMARY

Multimodal ocular imaging in eyes with uveitis is critical for disease diagnosis and assessing response to treatment. UWF fluorescein angiography can detect retinal vasculitis even in the absence of overt vascular sheathing. UWF AF can help detect more chorioretinal lesions than clinically visible. OCT can be used to assess the posterior cortical vitreous, retina, large retinal vessels and choroid in uveitis. The use of multimodal imaging will likely be needed to determine clinical trial endpoints in studies evaluating therapeutics for uveitis.