¿´Æ¬ÊÓƵ

Object moved to here.

¿´Æ¬ÊÓƵ :: Pilot & Feasibility Program Application

¿´Æ¬ÊÓƵ


Automated quantification of ultrastructural pathology of diabetic nephropathy using deep learning
Summary Data Summary
Applicant Najafian, Behzad
E-Mail Address najafian@uw.edu
Project Title Automated quantification of ultrastructural pathology of diabetic nephropathy
using deep learning
CBU ID 20AU4150
External SubContract ID 32307-82
Diabetic Complication Nephropathy
Funding Program Group Pilot & Feasibility [PF2020]
Abstract Diabetic nephropathy is by far the most common cause of end stage kidney
disease. The combination of morphometric approaches and electron microscopy have
provided major contributions to the current understanding of the progression of
diabetic nephropathy. Structural changes when properly quantified are not only
regarded as robust biomarker of progression and severity of diabetic
nephropathy, but also correlate with renal function and can predict progression
of diabetic nephropathy. Application of these methods has been limited to
research studies, largely because currently automated approaches are not
available. Deep learning is a form of machine learning methods based on
artificial neural networks which has been proven to be a powerful tool for image
analysis. Here, we aim to develop deep learning algorithms to automate
segmentation and quantification of key glomerular structural parameters that are
relevant to diabetic nephropathy, including glomerular basement membrane
thickness, podocyte foot process width and expansion of mesangium and mesangial
matrix. We will train deep learning algorithms on a large collection of electron
microscopy images from kidney biopsies obtained from patients with type 1 and
type 2 diabetes with a wide spectrum of diabetic nephropathy severity, as well
as kidney biopsies from normal control subjects and will validate the method
using multiple approaches.
Application PDF Application Research Plan
Status Contract Executed
Key Personnel Linda Shapiro, Ph.D. - Michael Mauer, M.D., Robert Nelson, Ph.D.
Salary Total Costs 47272
Supply Total Costs 471
Equipment Total Costs 5000
Travel/Other Total Costs 13350
Direct Costs 66093
Indirect Costs Proposed 33906
Total Costs Proposed 99999
Total Costs Approved 99999
Start Date 11/1/2020
End Date 10/31/2021
IFO Name Gebrenegus, Lily
IFO E-Mail Address gcahelp@uw.edu
IACUC/IRB No. 99999
IACUC/IRB Institution University of Washington
Entity ID No. 91-6001537
Report Request Date 10/31/2021
T1D NO
TypeCount
Invoices 9
Progress Reports 1
Data Submission


Invoices
UrlCBU IDExternal IDInstitutionDateDirectIndirectInvoiceBalancePDF
  View  20AU415032307-82University of Washington7/9/2021$6,957.79$3,861.57$10,819.36-View PDF
  View  20AU415032307-82University of Washington6/9/2021$3,270.57$1,815.17$5,085.74-View PDF
  View  20AU415032307-82University of Washington5/11/2021$4,620.86$2,564.58$7,185.44-View PDF
  View  20AU415032307-82University of Washington4/9/2021$3,706.81$2,057.28$5,764.09-View PDF
  View  20AU415032307-82University of Washington3/10/2021$6,844.24$3,798.56$10,642.80-View PDF
  View  20AU415032307-82University of Washington2/8/2022$12,607.99$6,997.33$19,605.32-View PDF
  View  20AU415032307-82University of Washington2/10/2021$3,801.20$2,109.67$5,910.87-View PDF
  View  20AU415032307-82University of Washington10/20/2021$15,862.32$8,803.59$24,665.91-View PDF
  View  20AU415032307-82University of Washington1/11/2021$6,636.31$3,683.16$10,319.47-View PDF
Click here to cancel and return to funding program application

*Author:
*SubContract:
*Select File:

Click browse and select the file to upload.
(Please upload ONLY TXT, Image Files (not histolgy), PDF, XLS, XLSX, DOC, or DOCX files.)