Artificial Intelligence & Deep Learning in Retina

Artificial Intelligence & Deep Learning in Retina

November 23, 2021

treatments would occur.

Screening programs must have high sensitivity in order to be clinically safe. The specificity should be high enough to be clinically useful. Furthermore, development of functional algorithms rely on the quality and the abundance of source data.

Homogenous data may lead to biases in the models, particularly when they are generalized and utilized in conjunction with underrepresented populations.  

Thus, the training set used must be diverse and include various subsets of the population at large to develop an algorithm with widespread applicability.

Additionally, despite the common goal of accurately identifying diseases, there currently is no standardized methodology/protocols for image capturing and image analysis algorithms, inherently resulting in variability and usability.

Furthermore, the importance of obtaining images with sufficient quality for grading is paramount because systems are unable to access images if below a certain threshold.

The National Institutes of Health through its collaborative community projects is working on these areas of unmet needs for various ophthalmic conditions.

Collaboration across countries and organizations as well as extensive data sharing and open-source algorithms will ensure relevant and useful AI systems in the future for screening and determining of treatment prognosis for ophthalmic conditions.

As the capabilities of AI evolve,more commercially available products for not only DR but other diseases will begin to appear.

Together with current routine clinical practice, AI and deep learning offer potential avenues to improve clinical efficiency, expand access to care, and ultimately improve the overall quality of care.

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