Development and Evaluation of Techniques for Computer Aided Detection and Diagnosis From Existing Radiologic Images

Background:

Radiologic images are getting more complex. They are also being used more often. Medical personnel are overwhelmed with data. Computer Aided Detection (CAD) and diagnosis may be able to improve medical care. Researchers want to create and test ways to use CAD. To do this, they want to use data from the Clinical Center s Department of Diagnostic Radiology.

Objective:

To create algorithms and software that accurately detect and characterize lesions, model anatomy, and monitor diseases on radiologic studies.

Eligibility:

People of all ages who have radiologic exams stored in the clinical PACS (picture archiving system) of the Clinical Center since July 6, 1953 with no end date. People with target lesions of any kind will be included.

Design:

This study will use existing data. Participants will include males and females of all ages. They will be chosen by keyword search on NIH databases.

The data that is used may include CT, MRI, ultrasound, and other images. It may include a participant s name, date of birth, and date of exam. It may include the name of the doctor, radiologist, and hospital. Data such as age, gender, race, disease, and treatment may be used. Other data from charts or studies may be used.

Imaging data of all organs of the body will be studied.

Data will be kept in computers and servers. The equipment will be password protected. Printouts will be stored in locked rooms.

This study will last 10 years.

Study Overview

• Status: Active, not recruiting
• Conditions: Radiology Information Systems

Detailed Description

Study Description:

This study uses artificial intelligence techniques to improve radiology diagnosis.

Objective:

Development of Computer Aided Detection techniques to improve radiology imaging.

Study Population:

Up to 1,000,000 NIH Clinical Center patients.

Description of Sites/Facilities conducting research:

NIH Clinical Center

Study Duration:

10 years

• Study Type: Observational
• Enrollment (Actual): 144625

Contacts and Locations

Study Locations

• United States
  • Maryland
    • Bethesda, Maryland, United States, 20892
      • National Institutes of Health Clinical Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Special classes of subjects, such as pregnant women, children, cognitively impaired individuals, or others likely to be vulnerable will be included since the data has already been obtained under an IRB-approved protocol and there are no anticipated additional risks from this research

Description

• INCLUSION CRITERIA:
• All radiologic exams available in the clinical PACS (picture archiving system) of the Clinical Center taken from July, 6, 1953 with no end date.
• Males and Females of all ages.
• Patients with target lesions of any kind will be included
• Patients without the target lesion will be included to determine the specificity of the computer aided detection or diagnosis algorithm

EXCLUSION CRITERIA:

None

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
1/All Subjects; Data will be derived from primary studies on all subjects.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Development of Computer Aided Detection techniques to improve radiology imaging	Development of Computer Aided Detection techniques to improve radiology imaging	End of study

Collaborators and Investigators

• Sponsor: National Institutes of Health Clinical Center (CC)
• Investigators: Principal Investigator: Ronald M Summers, M.D., National Institutes of Health Clinical Center (CC)

Publications and helpful links

Helpful Links

• NIH Clinical Center Detailed Web Page

Study record dates

Study Major Dates

• Study Start (Actual): October 5, 2020
• Primary Completion (Estimated): June 30, 2030
• Study Completion (Estimated): June 30, 2030

Study Registration Dates

• First Submitted: October 7, 2020
• First Submitted That Met QC Criteria: October 7, 2020
• First Posted (Actual): October 8, 2020

Study Record Updates

• Last Update Posted (Actual): December 22, 2023
• Last Update Submitted That Met QC Criteria: December 21, 2023
• Last Verified: December 20, 2023

More Information

Terms related to this study

• Keywords: Natural History; Artificial Intelligence; Lymphadenopathy; Opportunistic screening; Automated Detection; Epidural Masses
• Other Study ID Numbers: 10000037; 000037-CC

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No