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Title: Combining Image and Non-Image Data for Automatic Detection of Retina Disease in a Telemedicine Network

Abstract

A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion/anomaly detection is a low-cost way of achieving broad-based screening for diabetic retinopathy (DR) and other eye diseases. In the process of a routine eye-screening examination, other non-image data is often available which may be useful in automated diagnosis of disease. In this work, we report on the results of combining this non-image data with image data, using the protocol and processing steps of a prototype system for automated disease diagnosis of retina examinations from a telemedicine network. The system includes quality assessments, automated physiology detection, and automated lesion detection to create an archive of known cases. Non-image data such as diabetes onset date and hemoglobin A1c (HgA1c) for each patient examination are included as well, and the system is used to create a content-based image retrieval engine capable of automated diagnosis of disease into 'normal' and 'abnormal' categories. The system achieves a sensitivity and specificity of 91.2% and 71.6% using hold-one-out validation testing.

Authors:
ORCiD logo [1];  [2];  [3];  [4];  [1]; ORCiD logo [1];  [2];  [1]; ORCiD logo [1]
  1. ORNL
  2. University of Tennessee, Knoxville (UTK)
  3. Delta Health Alliance
  4. University of North Carolina
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1028151
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: BSEC 2011 - Knoxville, Tennessee, United States of America - 3/20/2011 12:00:00 AM-
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; CAMERAS; DETECTION; DIABETES MELLITUS; DIAGNOSIS; DISEASES; EYES; HEMOGLOBIN; IMAGE PROCESSING; IMAGES; MEDICAL EXAMINATIONS; PATIENTS; PHYSIOLOGY; PROCESSING; QUALITY CONTROL; REMOTE VIEWING EQUIPMENT; RETINA; SENSITIVITY; SPECIFICITY; TESTING; VALIDATION

Citation Formats

Aykac, Deniz, Chaum, Ed, Fox, Karen, Garg, Seema, Giancardo, Luca, Karnowski, Thomas, Li, Yi-Liang, Nichols, Trent L., and Tobin Jr, Kenneth. Combining Image and Non-Image Data for Automatic Detection of Retina Disease in a Telemedicine Network. United States: N. p., 2011. Web.
Aykac, Deniz, Chaum, Ed, Fox, Karen, Garg, Seema, Giancardo, Luca, Karnowski, Thomas, Li, Yi-Liang, Nichols, Trent L., & Tobin Jr, Kenneth. Combining Image and Non-Image Data for Automatic Detection of Retina Disease in a Telemedicine Network. United States.
Aykac, Deniz, Chaum, Ed, Fox, Karen, Garg, Seema, Giancardo, Luca, Karnowski, Thomas, Li, Yi-Liang, Nichols, Trent L., and Tobin Jr, Kenneth. Tue . "Combining Image and Non-Image Data for Automatic Detection of Retina Disease in a Telemedicine Network". United States.
@article{osti_1028151,
title = {Combining Image and Non-Image Data for Automatic Detection of Retina Disease in a Telemedicine Network},
author = {Aykac, Deniz and Chaum, Ed and Fox, Karen and Garg, Seema and Giancardo, Luca and Karnowski, Thomas and Li, Yi-Liang and Nichols, Trent L. and Tobin Jr, Kenneth},
abstractNote = {A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion/anomaly detection is a low-cost way of achieving broad-based screening for diabetic retinopathy (DR) and other eye diseases. In the process of a routine eye-screening examination, other non-image data is often available which may be useful in automated diagnosis of disease. In this work, we report on the results of combining this non-image data with image data, using the protocol and processing steps of a prototype system for automated disease diagnosis of retina examinations from a telemedicine network. The system includes quality assessments, automated physiology detection, and automated lesion detection to create an archive of known cases. Non-image data such as diabetes onset date and hemoglobin A1c (HgA1c) for each patient examination are included as well, and the system is used to create a content-based image retrieval engine capable of automated diagnosis of disease into 'normal' and 'abnormal' categories. The system achieves a sensitivity and specificity of 91.2% and 71.6% using hold-one-out validation testing.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2011},
month = {3}
}

Conference:
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