Abstract:
CorneaInsight (Insightful Analytics for early keratoconus Detection),, gives a reliable tool to ophthalmologists for detecting SKC in order to avoid the development of such a dangerous eye condition as keratoconus. In particular, it is really vital to diagnose SKC timely and advise patients on how to protect their health before any interventions with their eyes take place. In other words, KEDS can be considered a "smart second opinion." In fact, this system takes into account two aspects at once, namely objective information (for example, specific measurements of the eye) and subjective information (eye images).
The algorithm works by using a two-stage AI approach that consists of two methodologies in order to provide more accurate results. Firstly, the methodology referred to as EfficientNet-B0 is focused on pattern recognition within eye images, whereas the other part of the model is called ExtraTreesClassifier and focuses on tabular data processing. Integrating these two methodologies into one framework helps to produce a single outcome on whether the eyes in question are healthy or are diagnosed with Keratoconus. In order to make healthcare professionals trust the outcome of the predictions, the proposed algorithm provides the reasoning behind the decisions made.
To bridge the gap between complex algorithmic processing and clinical utility, the framework integrates dedicated Explainable AI (XAI) mechanisms. This ensuring that the system does not operate as a standard "black box," but instead generates transparent decision pathways alongside its classification. By exposing the explicit features and visual markers driving the diagnostic output, the algorithm provides medical professionals with the necessary interpretability to confidently audit, validate, and trust the system's conclusions in a clinical setting
