Abstract
With the emergence of unmanned plane, autonomous vehicles, face recognition, and language processing, the artificial intelligence (AI) has remarkably revolutionized our lifestyle. Recent studies indicate that AI has astounding potential to perform much better than human beings in some tasks, especially in the image recognition field. As the amount of image data in imaging center of ophthalmology is increasing dramatically, analyzing and processing these data is in urgent need. AI has been tried to apply to decipher medical data and has made extraordinary progress in intelligent diagnosis. In this paper, we presented the basic workflow for building an AI model and systematically reviewed applications of AI in the diagnosis of eye diseases. Future work should focus on setting up systematic AI platforms to diagnose general eye diseases based on multimodal data in the real world.
Highlights
As population aging has become a major demographic trend around the world, patients suffering from eye diseases are expected to increase steeply
We systematically reviewed the application of artificial intelligence (AI) in diagnosing ocular diseases, including the four leading cause of adult blindness diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), and cataract
To achieve a good performance, the data set is randomly partitioned into two independent subsets, one is for modeling and the other is for testing. e data in the former sets will be partitioned again into training set and validation set in most cases. e training set is used to fit the parameters of a model. e validation set is used to estimate how well the model had been trained and tune the parameters or to compare the performances of the prediction algorithms achieved based on the training set. e test set is used to evaluate the final performance of the trained model (Figure 2(a))
Summary
As population aging has become a major demographic trend around the world, patients suffering from eye diseases are expected to increase steeply. Deep integration of ophthalmology and artificial intelligence (AI) has the potential to revolutionize current disease diagnose pattern and generate a significant clinical impact. Machine learning (ML), occurred in 1980s, is a subset of AI, and is defined as a set of methods that automatically detect patterns in data and incorporate this information to predict future data under uncertain conditions. Miguel Caixinha and Sandrina Nunes introduced conventional machine learning (CML) techniques and reviewed applications of CML for diagnosis and monitoring of multimodal ocular. Rahimy [11] focused on DL applications in the ophthalmology field, without the mention about CML. Catania and Ernst Nicolitz systemically reviewed AI and robotic applications in multiple categories of vision and eye care but mentioned little about AI diagnosis of retinal diseases [12]. We hope we can provide both ophthalmologists and computer scientists a meaningful and comprehensive summary on AI applications in ophthalmology and facilitate promising AI projects in the ophthalmology field
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