June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Artificial Intelligence Machine Learning of Optical Coherence Tomography Angiography for the Diagnosis of Age-related Macular degeneration
Author Affiliations & Notes
  • TAI-CHI LIN
    Taipei Veterans General Hospital , Taipei, Taiwan
    National Yang-Ming University, Taipei, Taiwan
  • Ying-Chun Jheng
    Taipei Veterans General Hospital , Taipei, Taiwan
    National Yang-Ming University, Taipei, Taiwan
  • Shih-Jen Chen
    Taipei Veterans General Hospital , Taipei, Taiwan
    National Yang-Ming University, Taipei, Taiwan
  • Shih-Hwa Chiou
    Taipei Veterans General Hospital , Taipei, Taiwan
    National Yang-Ming University, Taipei, Taiwan
  • Footnotes
    Commercial Relationships   TAI-CHI LIN, None; Ying-Chun Jheng, None; Shih-Jen Chen, None; Shih-Hwa Chiou, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 2031. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      TAI-CHI LIN, Ying-Chun Jheng, Shih-Jen Chen, Shih-Hwa Chiou; Artificial Intelligence Machine Learning of Optical Coherence Tomography Angiography for the Diagnosis of Age-related Macular degeneration. Invest. Ophthalmol. Vis. Sci. 2020;61(7):2031.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose : To establish an optical coherence tomography angiography (OCTA) based artificial intelligence (AI) machine learning system for detecting different disease status in age-related macular degeneration (AMD) patients.

Methods :
A total of 395 OCTA images were randomly divided into two datasets, in which 356 images formed a training dataset, and the remaining images formed a validation dataset. All OCTA images were defined into 4 different categories: normal, dry AMD (drusen), and wet AMD with either active or inactive choroidal neovascularization (CNV), respectively. ResNet34 was applied to establish the AI model in detecting different diseases status of AMD. Moreover, augmentation skill has been applied to overcome the limitation of the data size and improvement the performance of our AI model. The augmentation skill contained horizontal & vertical flip, random rotation, lighting & contrast change.

Results :
The choroid capillary layer was selected to establish the AI model. (Fig.1)
The heat map and confusion matrix were illustrated in Fig. 2. The accuracy of this AI model was 87.2%. The specificity and sensitivity in most categories ranged from 80% to 100%. However, the sensitivity for drusen was only 50%.

Conclusions : We successfully developed a quick AI screening system in detecting different status in AMD patients by using OCTA images. Despite the unsatisfied sensitivity for detecting druse, this AI model showed good performance in all the other categories and thus provided a useful screening tool for AMD patients.

This is a 2020 ARVO Annual Meeting abstract.

 

The choroid capillary layer was elected to establish the AI model

The choroid capillary layer was elected to establish the AI model

 

The heat map and confusion matrix

The heat map and confusion matrix

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×