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Devices of the i-CAT family consist of an x-ray system that uses a cone beam with a rotational sequence, providing two dimensional images and three dimensional volume reconstructions of the head area, which includes ENT and maxillofacial areas (such as TM Joint studies, mandible & maxilla for implant planning and diagnostic support in adult and pediatic care. Devices of the i-C AT family comprise a package of software modules capable of handling 2D and 3D data. This includes 3D reconstruction, storage, retrieval, viewing, and processing of 2D and 3D-image data.

i-CAT FLX was developed as an upgrade to the existing i-CAT Scanner. i-CAT FLX, manufactured by Imaging Sciences International, is a Cone Beam Volumetric Tomography and Panoramic X-ray dental imaging system that consists of a scanner and a software package for image reconstruction. It is a device in the i-CAT Scanner family. The components of the system include the main affixed unit, the overhead, the gantry, the tube head, the X-ray source assembly and collimator, the receptor panel, and software package for image reconstruction. The system is an open design that allows patients to sit upright during a procedure. An electric powered seat is built into the scanner for proper patient positioning.

Cone Beam Volumetric Tomography is a medical imaging technique that uses X-rays to obtain crosssectional images of the head or neck. The proposed device utilizes cone beam X-ray technology, which generates conical x-ray beams that rotate around the patient's head and incident upon the receptor that generate sufficiently contrasted images. Quality of the images depends on the level and amount of X-ray energy delivered to the tissue. When interpreted by a trained physician, these images provide useful diagnostic information.

Here's an analysis of the provided text regarding the acceptance criteria and study for the i-CAT FLX Cone Beam 3D and 2D Panoramic Dental Imaging System:

The provided document is a 510(k) summary for the i-CAT FLX system, rather than a detailed study report with specific acceptance criteria metrics. In 510(k) submissions, the primary goal is to demonstrate "substantial equivalence" to a legally marketed predicate device, not necessarily to meet pre-defined quantitative acceptance criteria against a specific benchmark. Therefore, the "acceptance criteria" discussed here are focused on demonstrating equivalence in performance, safety, and effectiveness.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a distinct "test set" in the context of an algorithm's performance evaluation. The testing described is more akin to design verification and validation for a hardware/software system.

• "Clinical Test Data" for "acceptable clinical effectiveness": The document states "Sample clinical images acquired using i-CAT FLX were reviewed to be of acceptable clinical effectiveness for the proposed indications for use."
  • Sample Size: Not specified. It refers to "Sample clinical images," implying a subset, but no number is given.
  • Data Provenance: Not specified (e.g., country of origin, retrospective or prospective). It only states images were "acquired using i-CAT FLX."

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts

• Number of Experts: Not specified. The document mentions "When interpreted by a trained physician, these images provide useful diagnostic information" but does not detail the number or role of experts in the "acceptable clinical effectiveness" review.
• Qualifications of Experts: Not specified. It generally refers to "trained physician."

4. Adjudication Method for the Test Set

• Adjudication method: Not specified. The phrase "reviewed to be of acceptable clinical effectiveness" suggests an expert review process, but details of how disagreements or consistency were handled are not provided.

5. 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

• MRMC Study: No, an MRMC comparative effectiveness study, especially one involving human readers with and without AI assistance, was not described. The goal of this submission was to demonstrate substantial equivalence of a new imaging system to existing ones, not to evaluate an AI's impact on human performance. The "AI" would be the image reconstruction software, which is intrinsic to the device, not an add-on for comparative effectiveness with human readers.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Standalone Performance: The "algorithm" in this context refers to the image reconstruction software within the i-CAT FLX system. Its "standalone performance" is implicitly evaluated through the "Design Verification and Validation" which determined that the system "can produce volumetric and panoramic images... and they are of equivalent diagnostic quality as GALILEOS." This is a system-level performance evaluation, not a focused algorithm-only study against a specific metric.

7. The Type of Ground Truth Used

• Type of Ground Truth: For the "acceptable clinical effectiveness" of sample images, the ground truth appears to be expert consensus/interpretation by unspecified "trained physician(s)" that the images provided "useful diagnostic information" and were of "acceptable clinical effectiveness." This is inferred, as no explicit ground truth methodology is detailed. For the physical and technical performance (e.g., image quality compared to a predicate, biocompatibility, electrical safety), the ground truth is based on compliance with established standards and direct comparison to a predicate device's known performance.

8. The Sample Size for the Training Set

• Sample size for training set: Not applicable and not specified. This document describes an imaging system, not a machine learning model developed with a distinct "training set" in the modern AI sense. The software package for image reconstruction is an integral part of the device.

9. How the Ground Truth for the Training Set was Established

• Ground truth for training set: Not applicable and not specified, as there is no separate "training set" for an AI algorithm described in this submission. The software development and verification would have relied on engineering principles and established image processing techniques, rather than a labeled training dataset in the context of supervised machine learning.

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