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Related Experiment Video

Updated: Jun 26, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Automated Identification and Segmentation of Diabetic Macular Edema Subtypes Using Deep Learning.

Ming Yan1, Xianggui Zhang1, Ruilong Li1,2

  • 1Department of Ophthalmology, General Hospital of Central Theater Command, Wuhan, Hubei Province, People's Republic of China.

Translational Vision Science & Technology
|June 25, 2026
PubMed
Summary

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Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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This summary is machine-generated.

A deep learning algorithm accurately classifies diabetic macular edema (DME) subtypes and segments lesions from OCT images. This AI tool aids in DME diagnosis and personalized treatment planning.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Diabetic retinopathy (DR) is a leading cause of vision loss.
  • Diabetic macular edema (DME) is a severe complication of DR.
  • Accurate classification and segmentation of DME subtypes are crucial for effective treatment.

Purpose of the Study:

  • To develop a deep learning algorithm for classifying DME subtypes.
  • To segment lesions associated with DME using structural optical coherence tomography (OCT) images.
  • To evaluate the algorithm's performance and cross-device generalizability.

Main Methods:

  • A dataset (DME-Seg) of 3120 DME OCT B-scan images was curated from 823 eyes across four OCT devices.
  • Expert ophthalmologists provided multi-label annotations, including DME subtype classification and pixel-level segmentation masks.

Related Experiment Videos

Last Updated: Jun 26, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

  • The YOLO11x-Seg model was fine-tuned on the DME-Seg dataset for detection and segmentation tasks.

    • Main Results:

    • The fine-tuned model achieved high performance in lesion detection (mAP50(B) = 0.82) and segmentation (mAP50(M) = 0.84).
    • Dice coefficients for detection and segmentation were 0.82 ± 0.20 and 0.79 ± 0.18, respectively.
    • The model demonstrated promising cross-device generalizability.

    Conclusions:

    • The developed deep learning algorithm and DME-Seg dataset show potential for automated DME subtype detection and segmentation.
    • This AI-assisted tool can enhance the precision of DME diagnosis and treatment planning.
    • The findings support the clinical utility of AI in ophthalmology for managing diabetic eye disease.