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The C-InSight software provides a processing and conversion of 2D fluoroscopic projections from standard C-Arms into volumetric 3D image. It is intended to be used whenever the clinician and/or patient benefits from generated 3D imaging of high contrast objects particularly for orthopedic applications.

C-InSight is a software based product, which converts a sequence of Two-dimensional fluoroscopy images into a 3D volume, intraoperatively. The C-InSight is an add-on to commercially available mobile x-ray systems.

Here's an analysis of the acceptance criteria and study information for the C-InSight System, based on the provided text:

Key Takeaways Before Diving In:

• This device, the C-InSight System, is a software-based product that converts 2D fluoroscopy images into 3D volumes intraoperatively.
• No clinical performance data (human studies) are provided or stated as applicable. The evaluation relies entirely on non-clinical performance tests.
• The primary purpose of these tests is to establish substantial equivalence to predicate devices.
• The acceptance criteria are implicitly tied to meeting design and performance specifications as demonstrated by a series of non-clinical tests.

Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of quantitative acceptance criteria with corresponding performance metrics. Instead, it lists the types of non-clinical performance tests conducted and then concludes that these tests demonstrate the system "meets its design and performance specifications."

Implicit Acceptance Criteria and Reported Performance (Derived from the text):

Study Information

1. Sample Size Used for the Test Set and Data Provenance:

   • Test Set Sample Size: Not explicitly stated as a number of "cases" or "patients." For the "C-InSight accuracy vs. CT (cadaver tests)," the sample size would refer to the number of cadavers used, but this number is not provided. For synthetic and spine accuracy tests, the "sample size" would likely refer to the number of simulated or phantom test runs, which is also not specified.
   • Data Provenance:
     • Country of Origin: Not specified in the provided text.
     • Retrospective or Prospective: Not applicable, as no human clinical data was used. The tests seem to be controlled, laboratory-based non-clinical assessments.

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

   • Not applicable (N/A). The document describes non-clinical tests, not human-read clinical studies requiring expert ground truth establishment for a diagnostic output. For accuracy tests (synthetic, spine, cadaver vs. CT), "ground truth" would likely be established by known physical parameters of the phantom/cadaver or high-resolution CT scans, not human expert consensus on images generated by the C-InSight system.

3. Adjudication Method for the Test Set:

   • Not applicable (N/A). No human readers are performing diagnoses or evaluations of the device output that would require adjudication. The accuracy tests likely compare the C-InSight's 3D reconstructions against known physical measurements or higher-resolution imaging (like CT).

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, If So, What Was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance:

   • No. The document explicitly states "Clinical Performance Data: Not Applicable." Therefore, no MRMC study involving human readers was conducted or reported.

5. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

   • Yes, this appears to be the primary focus of the performance evaluation. The "Non-Clinical Performance Data" section describes tests of the C-InSight system's intrinsic capabilities (e.g., accuracy, image quality, software validation) as a standalone software product. The device is a "3-D Reconstruction Tool," and its performance is evaluated based on how well it generates these reconstructions.

6. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.):

   • For the accuracy tests (synthetic, spine, cadaver vs. CT): The ground truth would presumably be the known physical reality or a higher-fidelity imaging modality's output.
     • For "synthetic accuracy," it likely refers to a simulated environment with known parameters.
     • For "spine accuracy," it could involve phantoms with precisely known anatomical dimensions.
     • For "accuracy vs. CT (cadaver tests)," the ground truth would be the 3D anatomical information obtained from high-resolution CT scans of the cadavers, against which the C-InSight's reconstructions are compared.
   • For software validation, image quality, and radiation dose: Ground truth would be defined by pre-established engineering specifications, industry standards, or regulatory guidelines.

7. The Sample Size for the Training Set:

   • Not provided. The document describes a ready-to-use software product and its validation tests, not a machine learning model that requires a "training set." While the software likely uses algorithms developed using data, the specifics of such development data (if any) are not discussed in this 510(k) summary.

8. How the Ground Truth for the Training Set Was Established:

   • Not provided, as the document does not mention a training set in the context of device validation.

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