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The device is intended to be used for x-ray computed tomography and projection x-ray imaging of upper and lower extremities of adult patients and pediatric patients aged 12 and over.

The Carestream Health Onsight 3D Extremity System is a medical x-ray imaging device designed to acquire three-dimensional, volumetric CT data of patient extremities (feet, ankles, lower leg, knees, hands, wrists, arms and elbows). The device is configured as a "cone beam computed tomography" system (CBCT) in that the x-ray field covers the whole anatomy of interest (~25cm in length) and the data is acquired with a single rotation of the detector and x-ray source (actually a short scan of 180 degrees plus "fan angle" for a total of 216.5 degrees) with no patient motion through the irradiation field. The device is intended for use in a range of different locations including officebased medical practice, hospital departments and other imaging facilities. The system is also capable of acquiring standard two-dimensional projection radiographs of the same body parts.

Here's a breakdown of the acceptance criteria and the study details for the Carestream Health OnSight 3D Extremity System, based on the provided document:

Acceptance Criteria and Device Performance

The document states that predefined acceptance criteria were met, but it does not explicitly list the quantitative acceptance criteria. Instead, it generally indicates that the device's performance was evaluated against the predicate and reference devices and found to be "statistically equivalent to or better than."

Study Details

1. Sample Size and Data Provenance

• Test Set Sample Size: Not explicitly stated. The document mentions a "clinical study" and "Reader Study" but does not provide the number of cases or images included in the test set.
• Data Provenance: Not explicitly stated. The document describes a "clinical study," which generally implies prospective data collection, but no details on the country of origin are provided.

2. Number of Experts and Qualifications

• Number of Experts: Not explicitly stated. The document refers to a "Reader Study," which implies multiple readers, but the exact number is not given.
• Qualifications of Experts: Not explicitly stated. The document does not provide details on the specialty or experience level of the readers.

3. Adjudication Method

• Not explicitly stated. The document mentions a "Reader Study" to determine diagnostic capability but does not describe any specific adjudication method used for discrepancies between readers, if any.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC study done? Yes, a "Reader Study" was performed to compare the diagnostic capability of the OnSight 3D Extremity System against reference devices.
• Effect Size (Improvement with AI vs. without AI assistance): Not applicable. This study compared the diagnostic capability of the OnSight device's images to images from existing reference devices, not the improvement of human readers with AI assistance versus without AI assistance. The OnSight 3D Extremity System itself generates the images, and the study assesses the diagnostic quality of those images.

5. Standalone Performance Study

• Was a standalone study done? Yes, the "Reader Study" assessed the diagnostic capability of the images produced by the OnSight 3D Extremity System. This implies an evaluation of the algorithm's output (the images) for diagnostic quality.

6. Type of Ground Truth Used

• Not explicitly stated. Given that the study evaluated "diagnostic capability" and compared to reference devices, it is highly likely that the ground truth was established by expert consensus or comparison to a gold standard diagnosis, but the specific method (e.g., pathology, clinical follow-up) is not detailed.

7. Sample Size for Training Set

• Not applicable. The document describes a clinical study and bench testing for a medical imaging device (a CT system) that generates images, rather than an AI/ML algorithm that is trained on a dataset. Therefore, there is no mention of a training set for an AI model.

8. How Ground Truth for Training Set was Established

• Not applicable, as there is no mention of a training set for an AI model.

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