Authors: Dmitry Ryabtsev, Boris Vasilyev, Sergey Shershakov
Abstract: This paper introduces an innovative software system for fundus image analysis
that deliberately diverges from the conventional screening approach, opting not
to predict specific diagnoses. Instead, our methodology mimics the diagnostic
process by thoroughly analyzing both normal and pathological features of fundus
structures, leaving the ultimate decision-making authority in the hands of
healthcare professionals. Our initiative addresses the need for objective
clinical analysis and seeks to automate and enhance the clinical workflow of
fundus image examination. The system, from its overarching architecture to the
modular analysis design powered by artificial intelligence (AI) models, aligns
seamlessly with ophthalmological practices. Our unique approach utilizes a
combination of state-of-the-art deep learning methods and traditional computer
vision algorithms to provide a comprehensive and nuanced analysis of fundus
structures. We present a distinctive methodology for designing medical
applications, using our system as an illustrative example. Comprehensive
verification and validation results demonstrate the efficacy of our approach in
revolutionizing fundus image analysis, with potential applications across
various medical domains.
Source: http://arxiv.org/abs/2501.14689v1
