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PaX-Flex3D is a computed tomography x-ray system intended to produce panoramic, cephalometric or cross-sectional images of the oral anatomy on a real time basis by computer reconstruction of x-ray image data from the same axial plane taken at different angles. It provides diagnostic details of the anatomic structures by acquiring 360° rotational image sequences of oral and maxillofacial area for a precise treatment planning in adult and pediatric dentistry. The device is operated and used by physicians, dentists, and x-ray technicians.

PaX-Flex3D (PHT-7000), a dental radiographic imaging system, consists of dual image acquisition modes; panoramic, cephalometirc and cone beam computed tomography. Specifically designed for dental radiography of the teeth or jaws, PaX-Flex3D (PHT-7000) is a complete dental X-ray system equipped with x-ray tube, generator and dedicated SSXI detector for dental panoramic, cephalometric and cone beam computed tomographic radiography. The dental CBCT system is based on CMOS digital X-ray detector. CMOS CT detector is used to capture radiographic diagnostic images of oral anatomy in 3D for dental treatment such as oral surgery or implant. The device can also be operated as the panoramic and cephalometric dental x-ray system based on CMOS X-ray detector.

Here's a summary of the acceptance criteria and study information for the PaX-Flex3D (PHT-7000) based on the provided 510(k) summary.

1. Table of Acceptance Criteria (Performance Specifications) and Reported Device Performance:

The document primarily focuses on demonstrating substantial equivalence to a predicate device rather than explicitly stating acceptance criteria as pass/fail thresholds for clinical outcomes. Instead, it lists the technical and imaging specifications of the proposed device and compares them to the predicate device. The "reported device performance" is implicitly that the new device meets or is comparable to these specifications, as a conclusion of substantial equivalence is made.

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

The document mentions "an expert review of image comparisons" but does not specify the sample size (number of images or cases) used for this "clinical consideration" or "image comparison" part of the evaluation. It also does not specify the data provenance (e.g., country of origin, retrospective or prospective nature) for this image comparison.

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

The document states "an expert review of image comparisons." It does not specify the number of experts involved or their specific qualifications (e.g., radiologist with X years of experience).

4. Adjudication Method for the Test Set:

The document mentions "an expert review" but does not specify any adjudication method (e.g., 2+1, 3+1, none) for resolving discrepancies among experts.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

No, a MRMC comparative effectiveness study was not explicitly mentioned or described. The submission focuses on demonstrating substantial equivalence primarily through technical specifications and an "expert review of image comparisons" between the new device and its predicate. There is no mention of human readers improving with or without AI assistance, as the device itself is an imaging system, not an AI-powered diagnostic tool.

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

This section is not applicable as the device is an X-ray imaging system (hardware and associated software for reconstruction and viewing), not an AI algorithm. Its "performance" is its ability to produce diagnostic images.

7. The Type of Ground Truth Used:

For the "expert review of image comparisons," the ground truth was expert consensus (an "expert review" concluding that images from both devices were substantially equivalent in terms of safety and effectiveness).

8. The Sample Size for the Training Set:

This information is not applicable. The device is an imaging system, not a machine learning algorithm that requires a "training set" in the conventional sense for performance evaluation in a 510(k) submission like this. The design and engineering of the device are based on established physics and medical imaging principles.

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

This information is not applicable for the reasons stated in point 8.

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