# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 4 Representative Areas of Application of AI in Ophthalmology
### AI in Retinal Diseases: Other Applications
#### 4.7.2 Applications of AI in Retinal Diseases  
AI in Diabetic Retinopathy  
Various applications have been described in previous sections.  
AI in Age-Related Macular Degeneration  
This subject has been covered in previous sections.  
AI in Choroidal Neovascularization and Macular Diseases  
OCT images, especially macular ones, are suitable for developing DL algorithms. The large amount of macular OCT images can provide very large datasets needed to train DL networks. Yet, the precision in the acquisition of OCT images can reduce data complexity and allow DL networks to find meaningful information from a small dataset. Because macular OCTs provide a large amount of structural information about retinal layers, DL can also be utilized to find new biomarkers for macular diseases.  
The initial study of DL in the analysis of macular OCTs was the automatic classification of AMD (age-related macular degeneration). [PERSON] et al., using over 100,000 OCT images, trained a DL network, achieving an AUC of 0.97 [47].  
Most studies using macular OCT in DL have used B-scans (2D) of OCT rather than 3D images, which is a challenge in the use of DL in methods based on OCT.  
DL applied to CNNs has been utilized successfully for improved segmentation of retinal structural margins, intra-retinal cyst fluid, and subretinal fluid on B-scans [100].  
A new application of AI was made by [PERSON] et al. [7]: segmentation and classification of OCT images. Initially, a segmentation stage found nearly 15 retinal morphological features and artifacts from OCT images. A network categorized the results of the segmentation network into ten diseases (together with membrane choroidal neovascularization: CNV, macular edema, drusen, full-thickness macular hole, geographic atrophy, partial-thickness macular hole, vitreomacular traction, epiretinal membrane, central serous retinopathy, and \"normal\"). The above diseases were further classified into urgent, semi-urgent, routine, and observational. Their conclusion was that the application classified similarly to that of experts [7].  
This DL network can be introduced for quick access in \"virtual clinics\" and can help triage patients with macular diseases, reducing the burden on tertiary health institutions. Additionally, these triage clinics can also be used by optometrists in rural or outpatient environments.  
AI in Retinopathy of Prematurity  
This subject has been covered in previous sections.  
Retinal Vein Occlusions (RVOs)  
RVO is the most common cause of visual damage in the group of retinal vascular causes, after DR. It produces retinal hemorrhages, macular edema, and exudation. RVO is more common in older age, where high blood pressure, atherosclerosis, and heart disease are major risk factors.  
Currently, AI has not been extensively used for RVO. A study showed that using CNN integrated with image voting methods for fundus analysis automatically detected RVO. The authors obtained a great precision of over 97% [101].  
[PERSON] et al. [44] implemented DL approach in the detection of retinal non-perfusion areas in RVO by using OCT angiography. The researchers used heat maps to identify sectors of retinal non-perfusion. The detection of OCT angiographic images with RVO from healthy individuals was very good (AUC = 0.986). The sensitivity, specificity, and mean time needed to distinguish the images were, respectively, 93.7%, 97.3%, and 176.9 s, and the DL approach performed better than ophthalmologists in all parameters (!).  
Table 5 shows the power of AI methods in identifying retinal diseases using fundus images.