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The Panoura ULTRA Pan and Panoura ULTRA Pan/Ceph will be marketed for diagnostic radiographic use in dental, oral surgery, and orthodontic practices, and in other medical facilities where these types of dental radiographs are routinely taken. These uses are typical of the predicate devices in the Panoura-10 series.

The Panoura ULTRA Pan and Panoura ULTRA Pan/Ceph Panoramic/Cephalometric Xray Units are microprocessor controlled X-ray generator and film handling units designed mainly for dental applications. The patient stands facing the unit and is positioned by devices built into the unit. The exposure is begun under control of the operator, and the X-ray source and film rotate so as to produce a panoramic film for diagnostic use.

The provided 510(k) summary (K965072) for the Panoura ULTRA Pan and Panoura ULTRA Pan/Ceph Panoramic/Cephalometric X-ray Units does not contain acceptance criteria or a study proving performance against such criteria in the way a modern AI/CAD device would.

Instead, this submission is for a conventional X-ray device and focuses on demonstrating substantial equivalence to a predicate device (Yoshida/Kaycor Panoura-10 series). The "study" mentioned is a pre-production testing to show that radiographs from the new device are "essentially identical" to the predicate.

Therefore, many of the requested categories are not applicable to this type of submission. Below is an interpretation based on the provided text, highlighting what is (and isn't) present.

Acceptance Criteria and Study Proving Device Meets Criteria (Based on Substantial Equivalence)

1. Table of Acceptance Criteria and Reported Device Performance

For this device, the "acceptance criteria" are implicitly defined by the parameters of the predicate device, which the new device aims to match or be substantially equivalent to. The "reported device performance" is the new device's specifications.

The "study" that proves the device meets (or is substantially equivalent to) these criteria is described as:

"Preproduction testing showed that radiographs of the same patient produced on either device were essentially identical."

2. Sample size used for the test set and the data provenance

• Sample Size for Test Set: Not explicitly stated. The statement "radiographs of the same patient" implies a small number of patients were imaged with both the new and predicate devices, likely for qualitative comparison. There is no indication of a statistically powered sample size.
• Data Provenance: Not stated. Likely from a clinical setting associated with the manufacturer or a test facility, given it's "preproduction testing." It's retrospective in the sense that the radiographs were taken before the submission, but not a large-scale retrospective clinical study.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. Given the context of comparing diagnostic X-ray output, the assessment would likely be done by technical staff or radiologists, but this is an assumption.

4. Adjudication method for the test set

• Adjudication Method: Not specified. The phrase "essentially identical" suggests a subjective visual comparison rather than a formal, quantitative adjudication process. It's likely a direct comparison by technical personnel or imaging specialists.

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

• No, an MRMC comparative effectiveness study was not done. This is a conventional X-ray machine submission, not an AI/CAD device. Therefore, the concept of human readers improving with AI assistance is not applicable.

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

• No, this is not applicable. This is a hardware device, not an algorithm.

7. The type of ground truth used

• Type of Ground Truth: The "ground truth" here is the diagnostic quality and content of the radiographs produced by the predicate device. The goal was to demonstrate that the new device produced images of similar quality. This is a comparison against an existing, accepted standard of imaging output, implicitly assumed to be diagnostically adequate for its intended use. It is not an "expert consensus," "pathology," or "outcomes data" in the typical sense for an AI/CAD device.

8. The sample size for the training set

• Not applicable. This device does not use a "training set" in the context of machine learning or AI.

9. How the ground truth for the training set was established

• Not applicable. This device does not have a "training set."

Summary of Key Takeaway:

This 510(k) submission relies on demonstrating substantial equivalence to a legally marketed predicate device rather than fulfilling specific performance metrics against an independent ground truth or proving clinical efficacy through trials. The "preproduction testing" mentioned served to confirm that the new device's output was qualitatively comparable to the predicate, thus supporting the claim of substantial equivalence for its intended diagnostic use.

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