# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 5 AI and Different Applications in Ophthalmology and Other Fields
### Cardiovascular Risk Factors  
Precise, efficient, and scalable procedures of cardiovascular disease (CVD) risk assessment still are an important element and an urgent necessity for predicting outcomes of heart diseases, stroke, chronic kidney disease (CKD), and mortality, and for preventing unfavorable cardiovascular conditions such as heart attacks. Common risk calculators such as the Framingham test (coronary risk assessment method) and the pooled cohort all necessitate invasive blood tests. The retina, which presents as the only non-invasive visualization of the microvasculature, acts as an encouraging source of information regarding the subjects' cardiovascular risk [130]. Ocular manifestations of cardiovascular diseases encompass hypertensive retinopathy, cholesterol emboli and occlusions, flare hemorrhages, blebs, and ischemic events. Retinal vascular measurements (diameters of arteries and veins) in fundus photographs have been found to be related to CVD events. For instance, the risk of CVD is greater in individuals with narrower retinal arterioles and wider venules. In the case of patients with diabetes, predictive capacity increases when retinal imaging information is completed with extra details such as established risk parameters (e.g., blood pressure) and CRP (C-reactive protein) level [130]. Moreover, investigations have demonstrated that retinal image processing improves stroke risk prognosis compared to that given by typical risk factors (gender, age, blood pressure, total cholesterol, LDL cholesterol, glycosylated hemoglobin (HbA1c), and antihypertensive medications) [131]. However, interpretation and evaluation of retinal images is a difficult and time-consuming activity for ophthalmologists, even if with semi-automated computer programs [101]. In a study by Verily, a Google research company, a _DL-based method_ was developed and validated. It is able to better foresee several CV risk parameters than the method based on retinal fundus images alone: These parameters comprise age, gender, smoking status, systolic blood pressure, and body mass index [124]. The CNN learned by means of retinal fundus images from 48,101 patients from the Biobank Study (UK) and 236,234 patients from the eyePACS database. The research also generated additional software that estimated the onset of major adverse cardiovascular events (MACE) within 5 years using only fundus images. MACE comprises unstable angina, myocardial infarction, stroke, or death from CV causes. The model predicted very well, with an AUC of 0.70 (95% CI: 0.65, 0.74). Despite the promising results, the above DL techniques need to be clinically confirmed and trained with larger and more diverse databases. Although fundus retinal imagingcan complement CV risk evaluations nowadays, more advanced exploration is needed to determine if it can replace certain risk markers. Multiple Sclerosis  
This illness is a neurodegenerative disease originating from central nervous system (CNS) demyelination, resulting in progressive clinical disability. Diagnosis is made using the McDonald criteria, which contains clinical tests and objective evidence, including MRI of the brain and spinal cord and cerebrospinal fluid (CSF) examinations. As MS progresses, patients experience events of optic neuritis or vision loss and diplopia because of internuclear ophthalmoplegia. _Diagnosis._ Lately, some research has detected a correlation between MS and loss of both optic nerve axons, as well as macular thickness loss. Retinal and ML OCT investigations can supplement the traditional diagnosis of MS [132]. The retinal structure contains retinal ganglion cells and their axons, which compose the retinal nerve fiber layer (RNFL). By means of OCT images, research has shown that MS patients have thinner RNFL in comparison with healthy controls, both for those with and without optic neuritis [133].