Artificial intelligence (AI) is an unstoppable force that is starting to permeate all aspects of our society. It is part of the revolution which is shaking and shaping our lives in this digital era. As the population ages and developing countries move forward, AI-based systems may be a key asset in streamlining the screening, staging, and treatment planning for diseases in various medical fields. Vision is no exception. Early detection of diseases that can cause blindness is becoming a reality through the use of AI. This smart technology helps offloading the most tedious tasks of diagnosis from the experts, allowing for a greater accuracy in diagnosis and timely care.
Artificial Intelligence (AI) has experienced unparalleled growth in recent years, excelling at cognitive tasks that computers were never thought capable of performing. In the field of optometry and ophthalmology, these techniques find a particularly good fit. Firstly, the success of AI relies on having vast amounts of data, with conditions such as Diabetic Retinopathy (DR) or Age-Related Macular Degeneration (AMD). Secondly, one of the most mature AI subfields is image recognition where images from fundus or Optical Coherence Tomography (OCT) are widely adopted. This particular technology shows huge potential for automatic analysis and quantification with reasonings.
At a global level, there are several key challenges in Optometry or Ophthalmology that AI can help overcome. Aging of population means that the cases for conditions such as AMD and DR (along with diabetes) will only continue to rise, hence posing an ever-increasing burden on the already saturated healthcare systems. The COVID-19 pandemic showed glimpses of that horror. This is especially relevant for economically underdeveloped countries where such systems are more brittle and there are not enough trained specialists. Furthermore, while Retinopathy of Prematurity (ROP) only affects extremely premature infants in developed countries, in developing countries it affects older children. In this context, AI-based systems can be extremely useful in streamlining the screening, staging, and treatment planning of such conditions, offloading the most tedious tasks of diagnosis from the experts, allowing for a greater accuracy in diagnosis and care.
In practice, AI systems have already shown performances equal or above expert levels for DR grading, AMD grading, and general diagnosis from OCT images. Not only that, in 2018 the U.S. Food and Drug Administration (FDA) approved the IDx-DR, an AI-based system for DR screening, and the first FDA-authorized autonomous AI diagnostic system in any field of medicine. Furthermore, the advent of ge- netic testing and the ubiquity of Electronic Health Records(EHR) are paving the way for a fully personalized healthcare, in which an algorithm will decide the optimal treatment and dosage holistically based on all the available patient information.
In the next two to five years, the field of ophthalmology (and many others) will be deeply transformed by the universal adoption of these technologies. It is therefore crucial for the clinicians to have a solid understanding of the core algorithms that are fueling this revolution (as it is crucial for the data scientists to understand the underlying medical problem too). Hence, a significant effort has been made in this section to introduce the key concepts and algorithms underlying most research institutes.
