- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05903287
Validity of Pleural Effusion Detection Software
A Study to Evaluate Accuracy and Validity of Chang Gung Pleural Effusion Detection Software Compared With Reference by Qualified Physicians
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
This clinical trial is a retrospective study. DICOM images of de-identified Chest X-Ray were collected from 6 hospitals of Chang Gung Memorial Hospital from January 1, 2018 to December 31, 2020. After confirming that the Chest X-Ray that meet the inclusion and exclusion criteria are correct, 282 samples will be sampled for this test, including 141 images with pleural effusion and 141 images without pleural effusion. The image must be in DICOM format.
Then, 3 specialists physicians interpret 282 samples whether there were pleural effusion, and the result was the standard of this study (Reference standard). After determining the reference standard of each Chest X-Ray, the 282 samples were input into the Chang Gung Pleural Effusion Detection Software, and analyzed by the primary and secondary outcomes.
Study Type
Enrollment (Estimated)
Phase
- Not Applicable
Contacts and Locations
Study Locations
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-
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Taoyuan City, Taiwan, 333
- Chang Gung Memorial Hospital
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- The case were over 20 years old and under 100 years old.
- DICOM format.
- Brand model of Canon.
- DICOM resolution length range (700-3400 pixels), width range (490-3600 pixels).
- poster-anterior view(PA-view) of chest X-ray
Exclusion Criteria:
- The chest X-ray contains items that affect interpretation, such as chest tubes, endotracheal tubes, and heart rhythm regulators, but does not include patches and circuits used in electrocardiograms.
- The chest X-ray that are difficult to interpret due to poor image quality.
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
Software diagnosis
Software diagnosis with gold standard of 3 specialist physicians' interpretation.
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The Chang Gung Pleural Effusion Detection Software is an independent software as a medical device, which inputs digital Chest X-Ray to automatically detect whether there is a pleural effusion. The inferred results output by this software can assist clinicians or professional medical personnel to identify whether a patient has pleural effusion. This product is only used to analyze the digitized Chest X-Ray DICOM of patients over 20 years old and under 100 years old. |
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Sensitivity
Time Frame: baseline
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The rate of test results that correctly indicate the presence.
|
baseline
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Specificity
Time Frame: baseline
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The rate of test results that correctly indicate the absence.
|
baseline
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Area Under the receiver operating characteristic Curve
Time Frame: baseline
|
A graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied.
|
baseline
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Collaborators and Investigators
Sponsor
Investigators
- Study Chair: Chang-Fu Kuo, MD/Ph.D, Associate Professor and Director Division of Rheumatology
Publications and helpful links
General Publications
- Suresh K. An overview of randomization techniques: An unbiased assessment of outcome in clinical research. J Hum Reprod Sci. 2011 Jan;4(1):8-11. doi: 10.4103/0974-1208.82352.
- Lim CY, In J. Randomization in clinical studies. Korean J Anesthesiol. 2019 Jun;72(3):221-232. doi: 10.4097/kja.19049. Epub 2019 Apr 1. Erratum In: Korean J Anesthesiol. 2019 Aug;72(4):396.
- Lachin JM. Properties of simple randomization in clinical trials. Control Clin Trials. 1988 Dec;9(4):312-26. doi: 10.1016/0197-2456(88)90046-3.
- Ahn JS, Ebrahimian S, McDermott S, Lee S, Naccarato L, Di Capua JF, Wu MY, Zhang EW, Muse V, Miller B, Sabzalipour F, Bizzo BC, Dreyer KJ, Kaviani P, Digumarthy SR, Kalra MK. Association of Artificial Intelligence-Aided Chest Radiograph Interpretation With Reader Performance and Efficiency. JAMA Netw Open. 2022 Aug 1;5(8):e2229289. doi: 10.1001/jamanetworkopen.2022.29289.
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Estimated)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- 202300606A5
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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