{
  "id": "1702.01816",
  "version": "v1",
  "published": "2017-02-06T22:52:43.000Z",
  "updated": "2017-02-06T22:52:43.000Z",
  "title": "Prediction of Kidney Function from Biopsy Images Using Convolutional Neural Networks",
  "authors": [
    "David Ledbetter",
    "Long Ho",
    "Kevin V Lemley"
  ],
  "comment": "11 pages, 7 figures, 1 page of Appendix",
  "categories": [
    "stat.ML",
    "q-bio.QM"
  ],
  "abstract": "A Convolutional Neural Network was used to predict kidney function in patients with chronic kidney disease from high-resolution digital pathology scans of their kidney biopsies. Kidney biopsies were taken from participants of the NEPTUNE study, a longitudinal cohort study whose goal is to set up infrastructure for observing the evolution of 3 forms of idiopathic nephrotic syndrome, including developing predictors for progression of kidney disease. The knowledge of future kidney function is desirable as it can identify high-risk patients and influence treatment decisions, reducing the likelihood of irreversible kidney decline.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-02-06T22:52:43.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "convolutional neural network",
      "biopsy images",
      "prediction",
      "kidney biopsies",
      "high-resolution digital pathology scans"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 11,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}