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Interpreting Imaging in the Era of Artificial Intelligence: Future Possibilities in Ocular Inflammatory Disease
K Matthew McKay1, Leopold Schmetterer2, SriniVas R Sadda3
1From the Department of Ophthalmology (K.M.M., L.S.), Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA.
Purpose:
Uveitis encompasses a heterogeneous group of ocular inflammatory diseases with a high risk of permanent vision loss. Accurate diagnosis and disease monitoring are dependent on integrating disparate clinical data from patient history, examination findings and multimodal imaging, which is complex and time-consuming. The advent of artificial intelligence (AI) in the field of ophthalmology has provided new opportunities for automation of data gathering and interpretation, particularly for imaging data through deep learning algorithms. This article will review the current abilities, limitations, and future promise for the application of AI to the interpretation of multimodal imaging in the field of uveitis.
Methods:
Review article.
Results:
AI has provided new opportunities for automation of data gathering and interpretation in the field of uveitis, particularly for analysis of imaging data using deep learning techniques. Tools for diagnostic support, grading of intraocular inflammation, and quantification of disease features by multimodal imaging are under development.
Conclusion:
Application of AI in the field of uveitis is in its infancy but holds promise for improving efficiency and quality of patient care.
