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Approach to Designing CV Systems for Medical Applications: Data, Architecture and AI

Dmitry Ryabtsev, Boris Vasilyev, Sergey Shershakov

TL;DR

The paper addresses a key gap in medical imaging AI by avoiding direct disease prediction and instead delivering structured descriptions of fundus features to assist ophthalmologists. It introduces EYAS, a modular, plug-and-play fundus image analysis system that aligns with clinical workflows through localization, segmentation, and classification modules and produces interpretable intermediate results. Emphasizing data quality and clinician involvement, the work demonstrates validation and expert feedback to support safe, practical adoption, suggesting improved workflow efficiency and trust. Overall, EYAS provides a blueprint for clinician-centered AI design in medicine, with potential to extend to other domains beyond ophthalmology.

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.

Approach to Designing CV Systems for Medical Applications: Data, Architecture and AI

TL;DR

The paper addresses a key gap in medical imaging AI by avoiding direct disease prediction and instead delivering structured descriptions of fundus features to assist ophthalmologists. It introduces EYAS, a modular, plug-and-play fundus image analysis system that aligns with clinical workflows through localization, segmentation, and classification modules and produces interpretable intermediate results. Emphasizing data quality and clinician involvement, the work demonstrates validation and expert feedback to support safe, practical adoption, suggesting improved workflow efficiency and trust. Overall, EYAS provides a blueprint for clinician-centered AI design in medicine, with potential to extend to other domains beyond ophthalmology.

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.
Paper Structure (11 sections, 3 figures)

This paper contains 11 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Requirements Analysis
  4. Data Challenges and Strategies
  5. General Methodology and Approach
  6. Details of Implementation
  7. Architecture Design
  8. Analysis Pipeline
  9. Verification and Validation
  10. Performance evaluation
  11. Conclusion

Figures (3)

  • Figure 1: Architecture of the EYAS fundus image analysis system, illustrating the client-server model, API gateways, analysis microservices for fundus structures, and the report service that synthesizes results for clinical use.
  • Figure 2: The modular analysis pipeline of EYAS, showing the three-step process of localization, segmentation, and classification for fundus characterization.
  • Figure 3: Performance comparison of ONH Shape classifiers using different input formats, highlighting the impact of the analysis pipeline steps on classification accuracy.