Validation of an Artificial Intelligence-based Algorithm for Skeletal Age Assessment
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|ClinicalTrials.gov Identifier: NCT03530098|
Recruitment Status : Active, not recruiting
First Posted : May 21, 2018
Last Update Posted : October 11, 2019
|Condition or disease||Intervention/treatment||Phase|
|Bone Age||Device: BoneAgeModel||Not Applicable|
The investigators are targeting to study the effect of their Artificial Intelligence algorithm on the radiologists' estimation of skeletal age. Currently, radiologists make the estimation using only the patients' radiographic images and health records. As part of this study, the radiologists will make diagnoses about their patients using the patients' radiographic images, health records, and the output of the CADx algorithm. The investigators wish to understand how radiologists using the Artificial Intelligence algorithm compare to radiologists who do not for the specific task of estimating skeletal age.
This study is organized as a multi-institutional randomized control trial with two arms - experiment (receiving the Artificial Intelligence algorithm's output) and control (no intervention). Both of these arms will be compared to a clinical reference standard ("gold standard") composed of a panel of radiologists. The metric of comparison will be Mean Absolute Distance (MAD) and Root-Mean-Square (RMS) error. The investigators plan to use statistical tests such as the t-test to determine any statistically-significant difference in skeletal age estimation between the two groups.
The investigators hope to recruit and analyze data from a sample size of 1000 patients.The patients will not undergo any research procedures that deviate from the current standard practices.
|Study Type :||Interventional (Clinical Trial)|
|Estimated Enrollment :||1200 participants|
|Intervention Model:||Parallel Assignment|
|Intervention Model Description:||A hand radiograph will be randomly assigned to one of two groups - control and experiment. In the control group, participating radiologists will diagnose the case using the current standard of care (no intervention). In the experiment group, the radiologists will factor in the output of the Artificial Intelligence algorithm in their diagnosis. In all cases, the decision of the radiologist will be considered final.|
|Masking:||None (Open Label)|
|Official Title:||Prospective Clinical Validation of an Artificial Intelligence-based Algorithm for Skeletal Age Assessment|
|Actual Study Start Date :||July 12, 2018|
|Estimated Primary Completion Date :||October 2019|
|Estimated Study Completion Date :||November 2019|
No Intervention: Control
This is the control arm where no intervention is provided; represents current standard of care.
This is the experiment arm where the intervention, "BoneAgeModel", is provided. The participating radiologists in this arm will receive the output of the Artificial Intelligence algorithm. They will be asked to incorporate this new information with their normal workflows to make a diagnosis. The radiologists' diagnosis will be considered final.
BoneAgeModel is an Artificial Intelligence tool that takes in a pediatric patient's hand radiograph and gender, and outputs their skeletal (bone) age. The intervention involves using this tool as a factor in the clinical decision making process of the participating radiologists. The radiologist's decision will be considered final.
- Paired Difference of Skeletal Age Estimate [ Time Frame: Through study completion, an average of 8 months ]The skeletal age estimates from radiologists in the experiment arm will compared to that from the radiologists in the control group. The metrics used for comparison will be Mean Absolute Difference (MAD) and Root Mean Squared (RMS), both of which will be computed against a clinical reference standard composed of expert radiologists. Intuitively, this outcome compares the "accuracy" of the radiologists when they are using the BoneAgeModel to when they are not.
- Time Saved [ Time Frame: Through study completion, an average of 8 months ]Amount of time taken by radiologists when using the BoneAgeModel as compared to when they are not.
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03530098
|United States, California|
|Stanford, California, United States, 94305|
|United States, Connecticut|
|Yale New Haven Hospital|
|New Haven, Connecticut, United States, 06519|
|United States, District of Columbia|
|MedStar Georgetown University Hospital|
|Washington, District of Columbia, United States, 20007|
|United States, Georgia|
|Atlanta, Georgia, United States, 30322|
|United States, Kansas|
|University of Kansas Medical Center|
|Kansas City, Kansas, United States, 66160|
|United States, Massachusetts|
|Boston Children's Hospital|
|Boston, Massachusetts, United States, 02115|
|United States, New York|
|New York University|
|New York, New York, United States, 10016|
|United States, Ohio|
|Cincinnati Children's Hospital Medical Center|
|Cincinnati, Ohio, United States, 45229|
|United States, Pennsylvania|
|Children's Hospital of Philadelphia|
|Philadelphia, Pennsylvania, United States, 19104|
|Study Chair:||Curtis Langlotz, M.D. Ph.D.||Stanford University|
|Study Director:||David Eng, B.S.||Stanford University|
|Study Director:||Nishith Khandwala, B.S.||Stanford University|
|Principal Investigator:||Safwan Halabi, M.D.||Stanford University|