Exploring Clinically-relevant Image Retrieval for Diabetic Retinopathy Diagnosis (Arizona)

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Exploring Clinically-relevant Image Retrieval for Diabetic Retinopahty Diagnosis - 2012

Summary Highlights

  • Principal Investigator: 
  • Funding Mechanism: 
    PAR: HS08-269: Exploratory and Developmental Grant to Improve Health Care Quality through Health Information Technology (IT) (R21)
  • Grant Number: 
    R21 HS 019792
  • Project Period: 
    August 2011 – July 2013
  • AHRQ Funding Amount: 
    $299,999
  • PDF Version: 
    (PDF, 354.37 KB)

Summary: Diabetic retinopathy (DR) is the leading cause of new blindness in adults aged 20-74. Among diabetics, the prevalence of DR is 28.5 percent. Despite advances in diabetes care, visual impairment is still a devastating complication. Studies show that timely DR diagnosis and treatment can significantly reduce the risk of severe vision loss. Although digital retinal imaging has quickly become an alternative to traditional face-to-face evaluation, it is laborious and prone to error and reviewer fatigue. For this reason, researchers are exploring automated detection and evaluation of diabetic retinal lesions. Potential benefits of automated DR diagnosis include improved consistency and speed over human review. However, clinicians remain superior in detecting and assessing the severity of DR over computer-based systems, which fail to incorporate the experience and variables that clinicians apply to their assessments. Therefore, more effort is required to improve the performance of such systems.

This research project explores an innovative way to retrieve clinically relevant images for facilitating timely and accurate evaluation of DR. Images are considered clinically relevant if they contain the same types of lesions with similar severity levels. Dr. Li and his team have extensive experience in acquisition and deployment of computer-assisted evaluation of DR. Building on their experience, the team is designing a machine learning-based algorithm for retrieving images of clinical relevance to contribute to building automated DR detection and evaluation systems. Additionally, the team plans to develop a prototypical DR image management system to improve reviewers’ diagnostic performance. A direct outcome of the proposed research is a system that can provide a reviewer with instant reference to annotated images from a database. Maximizing the efficiency and accuracy of assessing DR could help prevent vision disabilities and their resulting high cost to the health care system.

Specific Aims:

  • Develop a content-based retrieval system for referencing diabetic retinal images to improve diagnosis. (Ongoing)
  • Develop a prototypical DR image management system to improve reviewers’ diagnostic performance. (Ongoing)

2012 Activities: The initial focus of the project was to collect a sufficient number of DR images to support the development of the machine learning algorithm, which requires large amounts of data to diagnose different stages of DR. These stages include non-proliferative retinopathy (mild, moderate, and severe) characterized by microaneurysms, and proliferative retinopathy, characterized by neovascularization. Initially, Dr. Li planned to use images from a researcher with a large database of images, but the researcher retired. As a result, Dr. Li contacted alternative potential collaborators around the country. To date, he has amassed approximately 1,000 images.

Dr. Li began work on the algorithm using the available images. Currently there are five well-known algorithms to process DR Images. These existing algorithms do not sufficiently distinguish between shades of red, the dominant color in DR images. Dr. Li’s team focused on color contrast when developing their algorithm to address this weakness. To do this, the existing algorithms are being applied to each of the images in Dr. Li’s collection. The algorithms work by extracting several hundred data points from each image and analyzing them. These results will serve as the benchmark for the newly-developed algorithm.

Dr. Li and his team developed the first version of the user interface (UI) used by clinicians to upload new DR images and see the results produced by the algorithm. The team solicited feedback from an ophthalmologist to learn about the end-user’s experience with the system. This ophthalmologist provided advice for improving system usability, including incorporating Web-based access so that clinicians may use the system outside the office. The other recommendation was to adjust the UI to fit a wide screen computer monitor to provide more space to display information. The team is in the process of incorporating these recommendations. Significant effort will be required to adapt the system to provide end-user Web-based access.

As last self-reported in the AHRQ Research Reporting System, project progress is on track in some respects but not others, and project budget funds are slightly underspent. Project delays are due to the additional time required to incorporate Web-based access to the system. Dr. Li and his team are conserving funds for a no-cost extension.

Preliminary Impact and Findings: Classification accuracy is the performance measure used to assess the machine-learning algorithm. Dr. Li reports that the classification accuracy for his algorithm is 87 percent. The highest classification accuracy reported in the literature is 75 percent, and most are closer to 50 percent.

Target Population: Adults, Chronic Care*, Diabetes, Other Conditions: Diabetic Retinopathy

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve health care decisionmaking through the use of integrated data and knowledge management.

Business Goal: Knowledge and Creation

*This target population is one of AHRQ’s priority populations.

Exploring Clinically-relevant Image Retrieval for Diabetic Retinopathy Diagnosis - 2011

Summary Highlights

  • Principal Investigator: 
  • Funding Mechanism: 
    PAR: HS08-269: Exploratory and Developmental Grant to Improve Health Care Quality through Health Information Technology (IT) (R21)
  • Grant Number: 
    R21 HS 019792
  • Project Period: 
    August 2011 - July 2013
  • AHRQ Funding Amount: 
    $299,999
  • PDF Version: 
    (PDF, 321.35 KB)

Summary: Diabetic retinopathy (DR) is the leading cause of new blindness in adults aged 20-74. Among diabetics, the prevalence of DR is 28.5 percent. Despite advances in diabetes care, visual impairment is still a devastating complication. Studies show that timely DR diagnosis and treatment can significantly reduce the risk of severe vision loss. Although digital retinal imaging has quickly become an alternative to traditional face-to-face evaluation, it is laborious and prone to error or reviewer fatigue. For this reason, researchers are exploring automated detection and evaluation of diabetic retinal lesions. Potential benefits of automated DR diagnosis include improved consistency and speed over human reviewers. However, clinicians remain superior in detecting and assessing the severity of DR over computer-based systems, which fail to incorporate the experience and variables that clinicians apply to their assessments. Therefore, more effort is required to improve the performance of such systems.

This research project explores an innovative method to retrieve clinically-relevant images for facilitating timely and accurate evaluation of DR. Images are considered clinically relevant if they contain the same types of lesions with similar severity levels. Dr. Baoxin Li and his team have extensive experience in acquisition and deployment of computer-assisted evaluation of DR. Building on their experience, the team will design machine learning-based algorithms for retrieving images of clinical relevance to contribute to building automated DR detection and evaluation systems. Additionally, the team plans to develop a prototypical DR image management system to improve reviewers' diagnostic performance. A direct outcome of the proposed research is a system that can provide a reviewer with instant reference to annotated images from a database. Maximizing the efficiency and accuracy of assessing DR could help prevent vision disabilities and their resulting high cost to the health care system.

Specific Aims:

  • Develop a content-based retrieval system for referencing diabetic retinal images to improve diagnosis. (Ongoing)
  • Develop a prototypical DR image management system to improve reviewers' diagnostic performance. (Upcoming)

2011 Activities: The initial focus of the project was to collect a sufficient number of DR images to support the development of the machine learning algorithm, which requires large amounts of data to diagnose different stages of DR. These stages include non-proliferative retinopathy (mild, moderate, and severe) characterized by microaneurysms, and proliferative retinopathy, characterized by neovascularization. Initially, Dr. Li planned to use images from a researcher with a large database of images at the University of Wisconsin, but the researcher retired. As a result, Dr. Li contacted alternative potential collaborators around the country. To date, he has amassed approximately 500 images. While this is a large number of images, more will eventually be required to validate the algorithm.

Dr. Li began work on the algorithm using the available images. Currently there are five well-known algorithms to process DR Images. These existing algorithms do not sufficiently distinguish between shades of red, the dominant color in DR images. Dr. Li's team plans to focus on color contrast when developing their algorithm to address this weakness. To do this, the existing algorithms are being applied to each of the images in Dr. Li's collection. The algorithms work by extracting several hundred data points from each image and analyzing them. These results will serve as the benchmark for the newly-developed algorithm.

As last self-reported in the AHRQ Research Reporting System, project progress and activities are on track and project spending is on target.

Preliminary Impact and Findings: There are no findings to date.

Target Population: Other Conditions: Diabetic retinopathy

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve health care decisionmaking through the use of integrated data and knowledge management.

Business Goal: Knowledge and Creation

Exploring Clinically-relevant Image Retrieval for Diabetic Retinopathy Diagnosis - Final Report

Citation:
Li B. Exploring Clinically-relevant Image Retrieval for Diabetic Retinopathy Diagnosis - Final Report. (Prepared by Arizona State University - Tempe Campus under Grant No. R21 HS019792). Rockville, MD: Agency for Healthcare Research and Quality, 2014. (PDF, 1.69 MB)

The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services.
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Project Details - Ended

Summary:

Diabetic retinopathy (DR) is a common cause of blindness. Despite advances in diabetes care, visual impairment is still a potentially devastating complication. Studies show that timely DR diagnosis and treatment can significantly reduce the risk of severe vision loss. Digital retinal imaging has quickly become an alternative to traditional face-to-face evaluation; however it is labor intensive and prone to error or reviewer fatigue. For this reason, researchers explored automated detection and evaluation of diabetic retinal lesions. Potential benefits of automated DR diagnosis include improved consistency and speed over human reviewers. However, clinicians remain superior in detecting and assessing the severity of DR over computer-based systems, which fail to incorporate the experience and variables that clinicians are able to apply in their assessments. More effort is therefore required to improve the performance of such systems.

The specific aims of this study were to:

  • Develop a content-based retrieval system for referencing diabetic retinal images to improve diagnosis 
  • Develop a prototypical DR image management system to improve reviewers’ diagnostic performance 

This project developed innovative machine learning-based algorithms to retrieve clinically relevant images to facilitate timely and accurate evaluation of DR. Images were considered clinically relevant if they contained the same types of lesions with similar severity levels. While previous work in this field used computational approaches that mimicked clinicians’ diagnostic processes, this approach used feature extraction to detect image features appropriate for distinguishing DR lesions. A retrieval interface was also developed to support the deployment of the retrieval engine in a user-friendly fashion.

The retrieval system was evaluated by examining whether the retrieved images have similar DR severity to images stored in a database. On average, the machine-learning algorithms outperformed other approaches in the literature.