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VEX-P300 is an extraoral diagnostic dental X-ray source to produce X-ray images using intraoral image receptors. It is indicated for use by a dental technician for both adult and pediatric patients.

VEX-P300, a portable dental X-ray system, operates on 22.2V DC supplied by a rechargeable Li-ion polymer battery pack. The portable x-ray system is an x-ray generating device which is mainly designed for dental examination (teeth and jaw). The portable X-ray system is composed of an x-ray generating part with an x-ray tube including a device controller, a power controller, a user interface, a beam limiting part, a back scattering shield, and an optional remote exposure switch. VEX-P300 is designed to diagnose tooth and jaw through X-ray exposure using intraoral image receptors.

The document describes the EzRay Air (Model VEX-P300), an extraoral diagnostic dental X-ray source, and compares it to a predicate device, the NOMAD Pro X-ray System (K081664). The focus of the provided text is on demonstrating substantial equivalence to the predicate device, rather than defining specific acceptance criteria for a new AI/ML-driven diagnostic device. Therefore, much of the requested information regarding AI/ML-specific acceptance criteria, ground truth, and expert evaluation is not present in this document.

However, I can extract the information related to the device's technical performance and the studies conducted to establish its equivalence.

1. A table of acceptance criteria and the reported device performance

The document does not explicitly present a table of acceptance criteria with corresponding performance values in the way one might expect for a diagnostic accuracy assessment of an AI device. Instead, it focuses on demonstrating compliance with federal and international standards for X-ray systems and verifying similar performance to the predicate device. The "acceptance criteria" here are implied by adherence to these standards and comparable performance to the predicate.

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

The document describes "Performance Bench Testing" conducted on the subject device (EzRay Air VEX-P300) and the predicate device (NOMAD Pro X-ray System). This testing involved physical measurements and evaluations of the X-ray systems' output and safety characteristics. It does not refer to a "test set" in the context of diagnostic images or data for an AI algorithm.

• Sample size for test set: Not applicable in the context of images or diagnostic data for AI. The "test set" here refers to the devices themselves and the conditions under which they were tested. The assessment was done on one subject device and one predicate device.
• Data provenance: Not applicable in the context of retrospective/prospective diagnostic data. The "data" comes from physical measurements and evaluations of the devices' X-ray output and safety in a laboratory or testing environment. The country of origin of the data is implied to be from the testing facilities that generated the compliance reports, likely associated with the manufacturer (VATECH Co., Ltd. in Korea) or accredited testing bodies.

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

This is not applicable as the study described is a technical performance and safety validation of an X-ray hardware device, not a diagnostic AI algorithm that requires expert-established ground truth for image interpretation. The "ground truth" for this type of device relates to adherence to physical parameters and safety standards.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

Not applicable. The evaluation focused on objective physical measurements and compliance with technical standards, not on expert adjudication of diagnostic interpretations.

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

Not applicable. This submission is for an X-ray device, not an AI algorithm. Therefore, no MRMC study or assessment of human reader performance with or without AI assistance was conducted or discussed.

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

Not applicable. This submission is for an X-ray device, not an AI algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The "ground truth" for this submission is based on established engineering principles, international and federal safety and performance standards for X-ray generating equipment (e.g., IEC 60601 series, 21 CFR 1020.30 and 1020.31), and the technical specifications of the predicate device. It's about verifiable physical properties and output, not diagnostic outcomes from patient data.

8. The sample size for the training set

Not applicable. This document pertains to the performance and safety testing of an X-ray hardware device, not an AI or machine learning algorithm that requires a training set.

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

Not applicable, as there is no training set mentioned or used in the context of this device submission.

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