Validation of an Artificial Intelligence-based Algorithm for Skeletal Age Assessment

Study Overview
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Brief Summary
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The purpose of this study is to understand the effects of using an Artificial Intelligence algorithm for skeletal age estimation as a computer-aided diagnosis (CADx) system. In this prospective real-time study, the investigators will send de-identified hand radiographs to the Artificial Intelligence algorithm and surface the output of this algorithm to the radiologist, who will incorporate this information with their normal workflows to make an estimation of the bone age. All radiologists involved in the study will be trained to recognize the surfaced prediction to be the output of the Artificial Intelligence algorithm. The radiologists' diagnosis will be final and considered independent to the output of the algorithm.

Detailed Description
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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 radiographic images and health records. As part of this study, the radiologists will estimate skeletal age from 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). 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 have recruited and analyzed data from a sample size of 1600 exams. Patients getting these exams will not undergo any research procedures that deviate from the current standard practices.

Official Title
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Prospective, Multi-Center, Randomized Controlled Trial for Skeletal Age Assessment AI Model

Conditions
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Bone Age

Intervention / Treatment
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* Device: BoneAgeModel


Participation Criteria
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Eligibility Criteria
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Exams that meet the following inclusion criteria will be included: (1) exams read by radiologists who interpret pediatric skeletal age exams and verbally consent to participate (2) exams that contain a procedure code or study description indicative of a skeletal age exam. Exams containing more than one radiograph will not be included. Exams for which a trainee provides a preliminary interpretation will be excluded. No further exclusion criteria will be applied on the basis of image quality metrics or manufacturers. No exclusion criteria will be applied on the basis of patient chronological age.
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional 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 exam 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 skeletal age estimation. In all cases, the decision of the radiologist will be considered final.
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| No Intervention: Control (Without-AI)<br>This is the control arm where no intervention is provided; represents current standard of care. | |
| Experimental: Experiment (With-AI)<br>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. | Device: BoneAgeModel<br>* BoneAgeModel is an Artificial Intelligence tool that takes in a hand radiograph and gender, and outputs the 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Paired Difference of Skeletal Age Estimate | Mean absolute difference between dictated final impressions (baseline measure by Radiologist) and the consensus determination of a panel of radiologists following review. | Up to 10 minutes to acquire the scan; up to 2 days to complete diagnosis review | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Time for Diagnosis | Amount of time taken by radiologists when using the BoneAgeModel as compared to when they are not. | Up to approximately 4 minutes | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Machine Learning, Age Determination by Skeleton, Deep Learning, Artificial Intelligence, Prospective, Clinical Validation