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

2025-01-24

AI Summary

To address the limited interpretability and poor clinical integration of AI systems in fundus image analysis, this study proposes a “non-diagnostic” clinical assistance paradigm, implementing a modular fundus anatomical structure analysis system. Methodologically, it integrates a Transformer-based feature encoder with classical computer vision techniques—including vessel segmentation and geometric modeling of the optic cup and optic disc—to achieve structured, multi-granularity extraction of anatomical features, without generating diagnostic conclusions—only objective, verifiable measurements. Its key contributions are: (i) the first methodology for modular CV design explicitly aligned with ophthalmic clinical reasoning; and (ii) an interpretable, interoperable AI architecture conforming to real-world clinical workflows. Multi-center validation demonstrates significant improvements over existing screening models: +23.6% consistency in feature detection, 41% faster report generation, and 89.2% clinical adoption rate.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Retina Analysis
• Medical Diagnosis
• Assistive Technology

Innovation

• Artificial Intelligence
• Retinal Health Analysis
• Medical Software Design