AI In Ophthalmic Pathology
On the basis of morphological datasets with millions of data points, a range of image-related diagnostic approaches in ophthalmology have started to provide previously unheard-of insights into eye illnesses. In some visual and aural identification tests, artificial intelligence (AI), which was inspired by the human multilayered neural system, has demonstrated astounding success.
Uses Of AI In Ophthalmology
The use of AI in ophthalmology includes the detection of eye cancers. For the purpose of preoperative margin delineation, reconstructive planning, and the detection of both basal cell and squamous cell carcinomas, machine learning algorithms have been created. Furthermore, research indicates that AI is effective at detecting, classifying, and calculating intraocular lens power in cataracts. Generative adversarial networks, a new AI approach, has demonstrated its ability to accurately translate between two different ocular imaging modalities.
Current Imaging Modalities In Neuro-Ophthalmology
Prior to exploring AI applications in neuro-ophthalmology, it is crucial to review the current imaging techniques now in use. Computed tomography and magnetic resonance imaging make up the main foundation of imaging in neuro-ophthalmology, and each has certain benefits and drawbacks depending on what an ophthalmologist is trying to find. Additionally, it’s critical to identify vascular anomalies that could be causing ocular disease with CT angiography and MR angiography.
Applications Of AI In Neuro-Ophthalmology
The laterality of a diseased process is crucial in neuro-ophthalmology for reducing the list of possible diagnosis. For instance, distinct illness conditions often lead to unilateral and bilateral papilledema. In order to start the process of making a diagnosis, it is crucial for AI to be able to discriminate between the laterality of the right and left eye. In this regard, AI created a DLS that was competitive with its considerably larger data set, achieving 98.78% accuracy. This leads to the intriguing finding that DLS can be reliable even with little amounts of data.
Using the terms AI, ML, DL, ophthalmology, diabetic retinopathy, age-related macular degeneration, retinopathy of prematurity, anterior-segment disorders, cataract, glaucoma, Web of Science, Embase, and Cochrane, considerable web research led to the creation of this review. The ensuing advancements in several sectors of ophthalmology were chronicled, and a chronological history of AI and its adaption in healthcare, particularly ophthalmology, was plotted.
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