June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Machine Learning to Predict Diabetic Retinopathy Improvement in Mild NPDR Patients From EUROCONDOR and C-Tracer Using Systemic and Retinal Imaging Features
Author Affiliations & Notes
  • Dimitrios Damopoulos
    F. Hoffmann-La Roche, Basel, Switzerland
  • Luis Mendes
    AIBILI- Association for Innovation and Biomedical Research on Light and Image, Portugal
  • Beatriz Garcia Armendariz
    F. Hoffmann-La Roche, Basel, Switzerland
  • Torcato Santos
    AIBILI- Association for Innovation and Biomedical Research on Light and Image, Portugal
  • Zdenka Haskova
    Genentech, Inc., California, United States
  • Robert Weikert
    F. Hoffmann-La Roche, Basel, Switzerland
  • Fethallah Benmansour
    F. Hoffmann-La Roche, Basel, Switzerland
  • Jose G Cunha-Vaz
    AIBILI- Association for Innovation and Biomedical Research on Light and Image, Portugal
  • Footnotes
    Commercial Relationships   Dimitrios Damopoulos, Roche (E); Luis Mendes, None; Beatriz Garcia Armendariz, Roche (E); Torcato Santos, None; Zdenka Haskova, Genentech, Inc. (E); Robert Weikert, Roche (E); Fethallah Benmansour, Roche (E), Roche (I); Jose Cunha-Vaz, Alimera Sciences (C), Allergan (C), Bayer (C), Carl Zeiss Meditec (C), Gene Signal (C), Novartis (C), Oxular (C), Pfizer (C), Roche (C), Sanofi (C)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 4036. doi:
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      Dimitrios Damopoulos, Luis Mendes, Beatriz Garcia Armendariz, Torcato Santos, Zdenka Haskova, Robert Weikert, Fethallah Benmansour, Jose G Cunha-Vaz; Machine Learning to Predict Diabetic Retinopathy Improvement in Mild NPDR Patients From EUROCONDOR and C-Tracer Using Systemic and Retinal Imaging Features. Invest. Ophthalmol. Vis. Sci. 2020;61(7):4036.

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

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Abstract

Purpose : We trained and assessed the performance of machine learning (ML) algorithms predicting diabetic retinopathy (DR) improvement in nonproliferative DR (NPDR) patients with DR Severity Scale (DRSS) 20-35 (mild NPDR) based on systemic and/or retinal imaging features.

Methods : Baseline systemic data and retinal imaging features (optical coherence tomography [OCT], color fundus photographs [CFPs]) of patients with mild NPDR (DRSS 20-35) were pooled from the EUROCONDOR (NCT01726075) trial for neuroprotective eye drop assessment and the C-Tracer (NCT01607190) observational study, and were used to train random forest ML models predicting DRSS improvement over 2 years. We performed multifold cross-validation and compared model performances of ML algorithms when trained with systemic features only, retinal imaging features only, or combining all available features. The quantitative comparison is based on area under the receiver operator characteristics curve (AUROC).

Results : Data from 309 patients/eyes with mild NPDR at baseline were used to train the ML models and test in a 10-fold cross-validation setting. At baseline, 130 and 179 patients/eyes had a DRSS level of 20 and 35, respectively. After 2 years, DRSS level was improved in 133 (43%) of these patients, did not change in 159 (51.5%) patients, and worsened in 17 (5.5%) patients. DR severity improvement was predicted with an AUROC = 0.62 (95% CI, 0.56, 0.67), based on systemic features only; AUROC = 0.71 (95% CI, 0.65, 0.77), based on retinal imaging features only; and AUROC = 0.76 (95% CI, 0.70, 0.82), based on both systemic and imaging features.

Conclusions : Our results indicate that structural measurements from retinal images (OCT and CFP) have a higher predictive value than systemic features alone in predicting future DR improvement in patients with mild NPDR, while the combination of both feature families provided the best predictive outcome. Predictive ML models in NPDR patients could be used to inform personalized monitoring and follow-up.

This is a 2020 ARVO Annual Meeting abstract.

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