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EzSensor XHD is intended to collect dental x-ray photons and convert them into electronic impulses that may be stored, viewed and manipulated for diagnostic use by dentists.

EzSensor XHD is a digital intraoral sensor which acquires digital intra-oral images. EzSensor XHD acquires intra oral images with a sensor that is connected to a computer to produce an image almost instantaneously following exposure. The primary advantage of direct sensor systems is the speed with which images are acquired. The ergonomic design based on human intraoral anatomy improves patient comfort. EzSensor XHD includes the software (firmware) with MODERATE level of concern.

The provided text is a 510(k) summary for the EzSensor XHD, a digital dental intraoral sensor. It aims to demonstrate substantial equivalence to a predicate device, not necessarily to prove the device meets specific acceptance criteria for a new, unproven technology. Therefore, the traditional concept of "acceptance criteria" and a "study that proves the device meets the acceptance criteria" in the context of a clinical trial with specific performance thresholds is not directly applicable in the way one might expect for a novel AI device.

Instead, the document focuses on demonstrating substantial equivalence to an already legally marketed predicate device (EzSensor Soft, EzSensor Soft i, EzSensor Bio, EzSensor Bio I, Model: 1.0, 1.5, 2.0; K151707). This is achieved by showing that the proposed device has the same indications for use and similar technological characteristics, and that any differences do not raise new questions of safety or effectiveness.

However, we can extract performance specifications and comparative data presented as part of the substantial equivalence claim.

Here's an attempt to structure the information based on your requested format, interpreting "acceptance criteria" as the performance metrics deemed acceptable for demonstrating substantial equivalence to the predicate device, and "study" as the non-clinical and comparative evaluations conducted.

Device Name: EzSensor XHD
Regulatory Product Code: MUH (Extraoral source X-ray system)

Interpretation of "Acceptance Criteria" in the Context of 510(k) Substantial Equivalence:

For a 510(k) submission, "acceptance criteria" are not explicitly defined as pass/fail thresholds against a clinical endpoint for a novel diagnostic. Instead, the "acceptance" is the FDA's determination of substantial equivalence to a predicate device. This is achieved by demonstrating that the new device is as safe and effective as the predicate device. The performance comparisons below serve as the data points to support this claim, rather than a direct set of pre-defined "acceptance criteria" for clinical accuracy or efficacy.

1. Table of Acceptance Criteria (Interpreted as Performance Metrics for Substantial Equivalence) and Reported Device Performance

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

The document describes non-clinical bench testing and a comparative review of clinical images.

• Non-clinical (Bench) Test Set: No specific sample size is given for the non-clinical tests (DQE, MTF, NPS, Electrical, Mechanical, Environmental, EMC). These tests are typically performed on a limited number of manufactured devices/prototypes.
• Clinical Image Test Set: The text states, "The clinical images obtained from the EzSsensor XHD and EzSensor Soft were reviewed and rated comparatively." No specific number of images or patients (sample size) is provided for this comparative review.
• Data Provenance: Not explicitly stated, but assumed to be from a controlled in-house setting given the nature of a 510(k) submission primarily relying on bench testing and limited comparative review. The studies are prospective in the sense that they were conducted specifically for this submission, although the images themselves could be from previous patient encounters (retrospective collection).

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

• The document mentions a "comparative review" of clinical images but does not specify the number or qualifications of experts who performed this review or established any form of ground truth for the clinical image set. It simply states the images were "reviewed and rated comparatively" and that "EzSensor XHD produces overall better definition and grayscale of bony and soft tissue images in comparison with EzSensor Soft."

4. Adjudication Method (for the Test Set)

• No adjudication method is described. The comparative review appears to be a qualitative assessment, not a formal quantitative study requiring adjudication of expert readings against a ground truth.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No MRMC comparative effectiveness study was done as described in the provided text. The evaluation was a qualitative comparison of image quality, not a study of human readers' performance with and without AI assistance. The device itself is an imaging sensor, not an AI-assisted diagnostic tool.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance)

• This question is not applicable in the context of this device. The EzSensor XHD is a hardware imaging sensor that collects X-ray photons and converts them to digital images for diagnostic use by dentists. It does not perform an algorithm-only diagnostic task without human interpretation. It is a data acquisition device, not a diagnostic AI.

7. Type of Ground Truth Used

• For the non-clinical performance metrics (DQE, MTF, NPS), the "ground truth" is established by physical measurements and standardized testing protocols performed according to FDA guidance and international standards (e.g., "Guidance for the Submissions of 510(k)'s for Solid State X-ray Imaging Devices").
• For the comparative clinical image review, the "ground truth" is implied to be the expert visual assessment of image quality enabling diagnostic use, compared to an accepted predicate device. There is no mention of pathology, clinical outcomes data, or a multi-expert consensus process for a defined "ground truth" in the sense of disease presence/absence.

8. Sample Size for the Training Set

• The EzSensor XHD is a digital imaging sensor, not an AI/machine learning algorithm that requires a "training set" of data in the typical sense. Therefore, there is no training set as would be understood for an AI device. The device's performance is determined by its physical and electronic design and manufacturing, not by learning from a dataset.

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

• Not applicable, as there is no training set for this hardware device.

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