Comprehensive Summary
In this article, Abdelmotaal et al. study the detection of early ectasia (E) via the development of an artificial intelligence (AI)-based index system. The research was performed by evaluating the diagnostic accuracy of the AI systems upon consensual and ethically approved patient data from private ophthalmological centers in Iran and Germany. Then, Abdelmotaal et al. used the AI systems to also identify different corneal regions of the eye, whilst analysing pictures and further improving a more robust biomechanical index for the AI. The findings from this study thus revolve around the fact that the AI-derived index had an AUC of 0.995 and an accuracy of 96%. As such, one of the main points from the article’s discussion is that, as opposed to the traditional analysis methods such as N-tomographic methods, developing AI models has high potential to revolutionize the detection of localized features concerning disease and our body.
Outcomes and Implications
The research performed by Abdelmotaal et al. is important due to the widespread results that it contains related to not just the development of AI infrastructure that can help boost clinical efficiency, but also the background and nuance of early corneal E detection. Regarding medicine, this work has a comprehensive display of different parameters that can help to compare and enhance the diagnostic value of E screening and detection of early keratoconus (KC). Yet, the focus on the development of specific AI models that can actually be integrated into the clinical setting is limited. As a result, Abdelmotaal et al. note that there is still more work to be done on the user interface end to help seamlessly integrate their research findings into clinical implementation.