# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 4 Representative Areas of Application of AI in Ophthalmology
### AI in Eye Movement Disorders  
These disorders are due to ocular motor nerve paralyses and other sources of diplopia and ocular non-alignment, joint gaze anomalies, nystagmus, or other anomalous eye motions.  
The literature on AI applications in this area is more limited. Some authors have used ML for conjugate gaze limitation assessment techniques as ocular biomarkers for neurodegenerative diseases (Parkinson's [107], Alzheimer's [108], and Huntington's disease [109]) or neuropsychiatric illnesses [110].  
Moreover, AI has helped neuroimaging modalities in certain processes such as image segmentation and classification, diagnostics, prognosis, outcome prediction, and risk evaluation [111].  
ML techniques have been used to model ocular motor information [112] or to foresee characteristics related to congenital nystagmus [113]. More powerful DL methods have been used for the identification of strabismus in pediatric ophthalmology [59; 114; 115], which could be used for cranial palsy and telemedicine.  
AI and Ocular Motor Characteristics  
Few studies have describe ML methods for researching conjugate gaze abnormalities and nystagmus. The conjugate anomalies of gaze comprise vertical or horizontal joint gaze restraints, chasing or saccadic deficiencies, or reflexive divergence of conjugated gaze.  
[PERSON] et al. [113] used electrooculography to record eye motions in 20 patients and computed some characteristics of the signals using standard computer programs. They built predictive models by means of two ML methods: Random Forest and Logistic Regression Tree. They predicted visual acuity and eye positioning variability as a function of nystagmus characteristics (such as initial oscillations and foveal periods of nystagmus) with accuracies above 0.72 and 0.70, respectively.  
The application of AI in the case of strabismus diagnosis was described in the review [59]. Certain AI systems were used for the identification of cranial nerve paralysis and other eye imbalances.  
[PERSON] et al. [116] compiled a strabismus database comprising 5685 facial images and utilized it to train and test their method in two steps: in the first stage, a fully CNN was built based on region (R-FCN) for eye area segmentation, and a second stage used a deep CNN that distinguished the eye areas as normal or strabismic. The results of their approach were heartening: The sensitivity was 93.3%, the specificity reached 96.2%, and the accuracy was 93.9%.  
Refs. [114; 117] present diagnostics and divergence models in vertical strabismus by means of an expert system (StrabNet) that used ANNs. Four hypertrophic and four hypotrophic deviations linked with a diagnostic were studied, and the researchers used 50 groups of measurements for every one of the eight diagnostics to train and evaluate the ANN. The global results of StrabNet were good: 94% accuracy and 100% specificity.  
Ref. [59] highlights some limitations or obstacles faced by AI applications in pediatric ophthalmology, as in other medical domains:  
1. lack of agreement regarding the reference standard among specialists;limited ability to reproduce and compare results when researchers do not use publicly accessible databases;
3 absence of time-related assessments;
4 the existence of uninterpretable achievements in DL applications (black boxes), which make them unreliable for some medical service providers.