June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
HDF-Net: A Novel Convolutional Neural Network Approach of Image and Non-image Data Integration for Treatment Outcome Prediction in Neovascular Age-Related Macular Degeneration
Author Affiliations & Notes
  • Tsai-Chu Yeh
    Ophthalmology, Taipei Veterans General Hospital, Taipei, Taiwan
    National Yang-Ming University, Taipei, Taiwan
  • An-Chun Luo
    Industrial Technology Research Institute, Hsinchu, Taiwan
  • Yu-Shan Deng
    Industrial Technology Research Institute, Hsinchu, Taiwan
  • Yu-Hsien Lee
    Industrial Technology Research Institute, Hsinchu, Taiwan
  • Po-Han Chang
    Industrial Technology Research Institute, Hsinchu, Taiwan
  • Chun-Ju Lin
    Industrial Technology Research Institute, Hsinchu, Taiwan
  • Ming-Chi Tai
    National Tsing Hua University, Hsinchu, Taiwan
    Industrial Technology Research Institute, Hsinchu, Taiwan
  • Yu-Bai Chou
    Ophthalmology, Taipei Veterans General Hospital, Taipei, Taiwan
    National Yang-Ming University, Taipei, Taiwan
  • Footnotes
    Commercial Relationships   Tsai-Chu Yeh, None; An-Chun Luo, None; Yu-Shan Deng, None; Yu-Hsien Lee, None; Po-Han Chang, None; Chun-Ju Lin, None; Ming-Chi Tai, None; Yu-Bai Chou, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 125. doi:
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      Tsai-Chu Yeh, An-Chun Luo, Yu-Shan Deng, Yu-Hsien Lee, Po-Han Chang, Chun-Ju Lin, Ming-Chi Tai, Yu-Bai Chou; HDF-Net: A Novel Convolutional Neural Network Approach of Image and Non-image Data Integration for Treatment Outcome Prediction in Neovascular Age-Related Macular Degeneration. Invest. Ophthalmol. Vis. Sci. 2021;62(8):125.

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

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Abstract

Purpose : While prognosis and risk of progression is crucial in developing precise therapeutic strategy in treatment-naive neovascular age-related macular degeneration (nAMD) patients, limited predictive tools are available. We proposed a novel deep convolutional neural network (CNN) that enables feature extraction through image and non-image data integration to seize imperative information and achieve highly accurate outcome prediction.

Methods : A total of 698 evaluable nAMD patients who received intravitreal injection of anti–vascular endothelial growth factor (anti-VEGF) were analyzed. The Heterogeneous Data Fusion Net (HDF-Net) was designed to predict VA outcome in 12th months after anti-VEGF treatment. A set of baseline optical coherence tomography (OCT) image and non-image demographic features were employed as input data and the 12th-month VA as target data to train, validate, and test the HDF-Net. Accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were evaluated.

Results : Of all evaluable patients,163 patients had at least 2-line VA improvement, and 535 failed to achieve VA improvement (≥ 2 line).This newly designed HDF-Net was trained to perform 12-month VA forecast and demonstrated an AUC of 0.989 (95% CI, 0.970-0.999), accuracy of 0.936 (95% confidence interval [CI], 0.889-0.964), sensitivity of 0.933 (95% CI, 0.841-0.974), and specificity of 0.938 (95% CI, 0.877-0.969).The HDF-Net demonstrated superior performance to the classic AlexNet (AUC of 0.936 (95% CI, 0.894-0.978), accuracy of 0.895 (95% CI, 0.841-0.933), sensitivity of 0.824 (95% CI, 0.716-0.896), and specificity of 0.942 (95% CI, 0.880-0.973)). In the attention heatmap, focused locations that contributed most to decision making by HDF-Net showed validity and corresponded well to clinically relevant features within OCT images.

Conclusions : By simulating the real-world clinical decision process with mixed baseline information from OCT images and non-image data, HDF-Net demonstrated promising performance in predicting individualized treatment outcome. The results highlight the potential of deep learning to simultaneously process a broad range of clinical data to weigh and leverage the complete information of the patient. This novel approach is an important step toward personalized therapeutic strategy for nAMD.

This is a 2021 ARVO Annual Meeting abstract.

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