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An interpretable and interactive deep learning algorithm for a clinically applicable retinal fundus diagnosis system

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Summary

This study introduces a novel deep learning system for diagnosing ophthalmic diseases from retinal images. The system provides interpretable diagnostic reasoning, enhancing clinical trust and accuracy in eye condition detection.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate identification of retinal abnormalities and ophthalmic diseases is crucial for managing vision-threatening conditions.
  • Deep learning-based computer-aided diagnosis (CAD) systems show promise in improving reading efficiency and consistency.
  • Clinical adoption of deep neural networks (DNNs) is hindered by their opaque reasoning processes.

Purpose of the Study:

  • To develop a novel DNN architecture for identifying 15 abnormal retinal findings and diagnosing 8 major ophthalmic diseases.
  • To introduce a method for interpreting DNN diagnostic reasoning and enabling interactive adjustments.
  • To validate the model's diagnostic correlation with expert ophthalmologists.

Main Methods:

  • Designed a novel DNN architecture for analyzing macula-centered fundus images.
  • Developed a counterfactual attribution ratio (CAR) to elucidate diagnostic reasoning.
  • Quantitatively and qualitatively evaluated the system's interpretability and diagnostic performance.
  • Compared the model's CAR with expert ophthalmologists' correlation between findings and diseases.

Main Results:

  • The DNN system achieved expert-level accuracy in identifying retinal abnormalities and diagnosing ophthalmic diseases.
  • The CAR metric effectively illuminated the system's diagnostic reasoning process.
  • Demonstrated the capability for quantitative and qualitative interpretation and interactive adjustment of CAD results.
  • Confirmed that the model's reasoning aligns with ophthalmologists' understanding of finding-disease relationships.

Conclusions:

  • The proposed DNN system offers a reliable and interpretable tool for ophthalmic disease diagnosis from retinal images.
  • The CAR metric enhances transparency and trust in AI-driven diagnostic systems.
  • This approach facilitates a deeper understanding of AI's diagnostic process, comparable to human experts.