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Doppler Optical Coherence Tomography of Retinal Circulation
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A deep learning based automatic report generator for retinal optical coherence tomography images.

Xinjian Chen1,2,3, Huazhu Fu4, Jingtao Wang2

  • 1Health Management Center, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.

NPJ Digital Medicine
|October 20, 2025
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Summary
This summary is machine-generated.

A new AI model, Multi-label OCT Report Generation (MORG), can interpret Optical Coherence Tomography (OCT) images and generate reports. This deep learning tool assists ophthalmologists by accurately classifying pathologies and reducing report drafting time.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Interpreting Optical Coherence Tomography (OCT) images is crucial for diagnosing retinal diseases but is time-consuming for ophthalmologists.
  • Automating OCT image analysis can improve diagnostic efficiency and reduce workload.

Purpose of the Study:

  • To introduce the Multi-label OCT Report Generation (MORG) model, a deep learning system for automated OCT image interpretation and report generation.
  • To evaluate MORG's performance against human experts and other AI models.

Main Methods:

  • The MORG model utilizes dual image encoders for feature extraction from OCT image pairs.
  • A multi-scale module with an attention mechanism fuses features, followed by a sentence decoder for report generation.
  • The model was trained and tested on 57,308 retinal OCT image pairs.

Main Results:

  • MORG achieved high classification accuracy for 16 pathologies with 37 descriptive types.
  • In blind grading, MORG scored 4.55 out of 5, closely matching ophthalmologists' score of 4.63.
  • The model demonstrated a potential to reduce report drafting time by 58.9%.

Conclusions:

  • The MORG model shows significant promise as an AI-assisted tool for ophthalmologists, improving efficiency in OCT image interpretation.
  • MORG's performance suggests its utility in clinical settings for faster and accurate retinal image analysis.
  • This AI approach can substantially alleviate the workload of ophthalmologists, allowing them to focus on patient care.