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

Stanford University

Status: Completed

Conditions: Bone Age

Treatments: Device: BoneAgeModel

Study type: Interventional

Funder types: Other

Identifiers

NCT03530098

IRB #44764

Details and patient eligibility

About

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.

Full description

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.

Enrollment: 1,903 patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

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.

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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1,903 participants in 2 patient groups

Control (Without-AI)

No Intervention group

Description:

This is the control arm where no intervention is provided; represents current standard of care.

Experiment (With-AI)

Experimental group

Description:

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.

Treatment:

Device: BoneAgeModel