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Abstract: TH-PO632

Deep Learning Retinal Image Analysis for the Detection of CKD and Cardiovascular Risk Factors in the General Population

Session Information

Category: Hypertension and CVD

  • 1501 Hypertension and CVD: Epidemiology‚ Risk Factors‚ and Prevention

Authors

  • Sexton, Donal J., Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Dublin, Ireland
  • Romero-Ortuno, Roman, FRAILMatics group, TILDA, Trinity College Dublin., Dublin, Ireland
  • Knight, Silvin P., FRAILMatics group, TILDA, Trinity College Dublin., Dublin, Ireland
  • Dahyot, Rozenn, Maynooth University, Maynooth, Kildare, Ireland
  • Kenny, Rose Anne M., Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Dublin, Ireland
  • Karaali, Ali, Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Dublin, Ireland
Background

Retinal blood vessel patterns provide an opportunity to personalize an individuals risk assessment for CKD and cardiovascular risk factors (CVRF). In this study we propose a deep learning (DL) based prediction tool that uses retinal images from the Irish Longitudinal Study on Ageing (TILDA) to detect the existence of CKD in community dwelling individuals aged 50 years and over.

Methods

TILDA is a stratified random sample of the general population of Ireland. N=4569 participants underwent a detailed health assessment including retinal photography. We developed a convolutional neural network architecture inputting a single retinal image per participant for the prediction of CKD & CVRF. Binary cross entropy was used as a loss function. Analyses were conducted on the FRAILMatics HPC “Tinney”.

Results

See Table 1 & Image 1 for results.

Conclusion

A DL retinal image algorithm has good discrimination for CKD, eGFR and CVRF in community dwelling individuals. The prediction emphasis of our DL algorithm focuses on slightly different structures within the retinal image to predict serum creatinine versus serum cystatin.

Results of Deep Learning Retinal Image Analysis for CKD
Classification Neural Network (Image only)AUC
CKD by Serum Creatinine0.68
CKD by Serum Cystatin C0.71
Regression Neural Network (image only)MAE
Age6.84 yrs
Systolic BP16.55 mmHg
eGFR CKDEPI Creatinine13.9 ml/min
eGFR CKDEPI Cystatin C14.8 ml/min

Attention Map demonstrating where within the retinal image the algorithm is focusing to predict CKD by Cystatin C. Red-Yellow-Blue in descending order of importance.