Development of Computer-aided Detection and Diagnosis From Imaging Techniques
Development and Evaluation of Techniques for Computer Aided Detection and Diagnosis From Radiologic Images
2 other identifiers
observational
139,692
1 country
1
Brief Summary
This study will develop and evaluate new techniques for computer-aided detection and diagnosis (CAD) of medical problems using images from diagnostic tests such as computed tomography (CT), ultrasound, nuclear medicine and x-ray images. The Food and Drug Administration has approved CAD techniques for detecting masses and calcifications on mammography and lung nodules using chest x-rays. Many other applications of CAD would potentially benefit patients. This study will explore additional uses of CAD. The study will use imaging data, demographic information, and other medical information from the medical charts of Clinical Center patients to test and evaluate new CAD applications. Such applications include detection of subcutaneous (under the skin) lesions in melanoma patients, bone lesions in patients with advanced cancer, and pulmonary emboli (blood clot lodged in a lung artery) in patients who are known to have pulmonary emboli, and other uses.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Mar 2003
Longer than P75 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
March 20, 2003
CompletedFirst Posted
Study publicly available on registry
March 28, 2003
CompletedFirst Submitted
Initial submission to the registry
July 13, 2006
CompletedPrimary Completion
Last participant's last visit for primary outcome
October 2, 2020
CompletedStudy Completion
Last participant's last visit for all outcomes
October 2, 2020
CompletedJanuary 22, 2021
January 1, 2021
17.6 years
July 13, 2006
January 21, 2021
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
New computer-aided detection methods--algorithms
computer-aided detection methods
Various
Study Arms (1)
1
Patients with medical imaging records
Eligibility Criteria
Patients with medical imaging records
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
National Institutes of Health Clinical Center, 9000 Rockville Pike
Bethesda, Maryland, 20892, United States
Related Publications (3)
Liu J, Wang S, Linguraru MG, Yao J, Summers RM. Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT. Med Image Anal. 2014 Jul;18(5):725-39. doi: 10.1016/j.media.2014.04.001. Epub 2014 Apr 18.
PMID: 24835180BACKGROUNDZhang W, Liu J, Yao J, Louie A, Nguyen TB, Wank S, Nowinski WL, Summers RM. Mesenteric vasculature-guided small bowel segmentation on 3-D CT. IEEE Trans Med Imaging. 2013 Nov;32(11):2006-21. doi: 10.1109/TMI.2013.2271487. Epub 2013 Jun 27.
PMID: 23807437BACKGROUNDBurns JE, Yao J, Wiese TS, Munoz HE, Jones EC, Summers RM. Automated detection of sclerotic metastases in the thoracolumbar spine at CT. Radiology. 2013 Jul;268(1):69-78. doi: 10.1148/radiol.13121351. Epub 2013 Feb 28.
PMID: 23449957BACKGROUND
Related Links
MeSH Terms
Conditions
Study Officials
- PRINCIPAL INVESTIGATOR
Ronald M Summers, M.D.
National Institutes of Health Clinical Center (CC)
Study Design
- Study Type
- observational
- Observational Model
- OTHER
- Time Perspective
- OTHER
- Sponsor Type
- NIH
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
July 13, 2006
First Posted
March 28, 2003
Study Start
March 20, 2003
Primary Completion
October 2, 2020
Study Completion
October 2, 2020
Last Updated
January 22, 2021
Record last verified: 2021-01