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Related Concept Videos

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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Optical coherence tomography-based diabetic macula edema screening with artificial intelligence.

De-Kuang Hwang1,2, Yu-Bai Chou1,2, Tai-Chi Lin1,2

  • 1Department of Ophthalmology, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.

Journal of the Chinese Medical Association : JCMA
|May 27, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) models accurately diagnose diabetic macular edema (DME) using optical coherence tomography (OCT) scans. This AI system can assist clinicians in screening patients with diabetes mellitus (DM) for DME, improving efficiency.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Optical coherence tomography (OCT) is a key noninvasive tool for evaluating macular lesions.
  • Diabetic macular edema (DME) in diabetes mellitus (DM) patients significantly impairs vision, often requiring anti-vascular endothelial growth factor (VEGF) injections.
  • Manual screening of OCT images for DME is time-consuming for clinicians due to the increasing prevalence of DM.

Purpose of the Study:

  • To develop and evaluate artificial intelligence (AI) models for automated DME detection from OCT images.
  • To reduce the clinical burden associated with manual DME diagnosis in diabetic patients.

Main Methods:

  • Retrospective collection of macular OCT images from DME patients treated between 2008 and 2018.
  • Establishment of AI models utilizing convolutional neural network architectures (InceptionV3, VGG16).
  • Training and validation of AI models to differentiate between DME and non-DME cases using OCT scans.

Main Results:

  • AI models achieved high diagnostic accuracy for DME: InceptionV3 (93.09%) and VGG16 (92.82%).
  • High sensitivity was reported for both models: VGG16 (96.48%) and InceptionV3 (95.15%).
  • An OCT-driven platform integrating these AI models was developed.

Conclusions:

  • AI models demonstrate accurate diagnosis of DME from OCT images.
  • The developed AI system shows potential for assisting clinicians in DME screening for diabetic patients.
  • This technology can help alleviate the workload in managing diabetic eye complications.